07-01-2011, 09:45 AM
hello
projectsofme has posted a ppt in this thread. please check 3rd page carefully.
projectsofme has posted a ppt in this thread. please check 3rd page carefully.
07-01-2011, 09:45 AM
hello
projectsofme has posted a ppt in this thread. please check 3rd page carefully.
24-01-2011, 05:12 PM
amitGrid_lecture1.ppt (Size: 4.34 MB / Downloads: 324) Dheeraj Bhardwaj Department of Computer Science and Engineering Indian Institute of Technology, Delhi Outline The technology landscape Grid computing The Globus Toolkit Applications and technologies Data-intensive; distributed computing; collaborative; remote access to facilities Grid infrastructure Open Grid Services Architecture Global Grid Forum Summary and conclusions Living in an Exponential World:(2) Storage Storage density doubles every 12 months Dramatic growth in online data (1 petabyte = 1000 terabyte = 1,000,000 gigabyte) 2000 ~0.5 petabyte 2005 ~10 petabytes 2010 ~100 petabytes 2015 ~1000 petabytes? Transforming entire disciplines in physical and, increasingly, biological sciences; humanities next? Data Intensive Physical Sciences High energy & nuclear physics Including new experiments at CERN Gravity wave searches LIGO, GEO, VIRGO Time-dependent 3-D systems (simulation, data) Earth Observation, climate modeling Geophysics, earthquake modeling Fluids, aerodynamic design Pollutant dispersal scenarios Astronomy: Digital sky surveys Ongoing Astronomical Mega-Surveys Large number of new surveys Multi-TB in size, 100M objects or larger In databases Individual archives planned and under way Multi-wavelength view of the sky > 13 wavelength coverage within 5 years Impressive early discoveries Finding exotic objects by unusual colors L,T dwarfs, high redshift quasars Finding objects by time variability Gravitational micro-lensing Coming Floods of Astronomy Data The planned Large Synoptic Survey Telescope will produce over 10 petabytes per year by 2008! All-sky survey every few days, so will have fine-grain time series for the first time Data Intensive Biology and Medicine Medical data X-Ray, mammography data, etc. (many petabytes) Digitizing patient records (ditto) X-ray crystallography Molecular genomics and related disciplines Human Genome, other genome databases Proteomics (protein structure, activities, …) Protein interactions, drug delivery Virtual Population Laboratory (proposed) Simulate likely spread of disease outbreaks Brain scans (3-D, time dependent) Evolution of Business Pre-Internet Central corporate data processing facility Business processes not compute-oriented Post-Internet Enterprise computing is highly distributed, heterogeneous, inter-enterprise (B2B) Outsourcing becomes feasible => service providers of various sorts Business processes increasingly computing- and data-rich The Grid “Resource sharing & coordinated problem solving in dynamic, multi-institutional virtual organizations” The Grid Opportunity:eScience and eBusiness Physicists worldwide pool resources for peta-op analyses of petabytes of data Civil engineers collaborate to design, execute, & analyze shake table experiments An insurance company mines data from partner hospitals for fraud detection An application service provider offloads excess load to a compute cycle provider An enterprise configures internal & external resources to support eBusiness workload Challenging Technical Requirements Dynamic formation and management of virtual organizations Online negotiation of access to services: who, what, why, when, how Establishment of applications and systems able to deliver multiple qualities of service Autonomic management of infrastructure elements Open Grid Services Architecture
17-02-2011, 03:43 PM
Grid_computing.pdf (Size: 462.54 KB / Downloads: 118) Grid Computing Scenario For years, Dr. Rayburn has been looking for tools to helphis architecture students move beyond paper sketchesand scaled-down models. He knows that as workingarchitects, they will be using computer simulations thatrequire not just design skill but proficiency with increasinglycomplex software and hardware. Unfortunately,his department cannot afford to purchase and supporta computing system with the necessary processing capacityto run such advanced applications.Over the summer, the university’s IT staff, working withthe computer science department, set up a computergrid running on the campus network. The grid connectsnearly all university-owned computers, includingthose in labs, the library, as well as faculty and staffoffices. The software that runs the grid gives local userspriority for those machines, but when they are idle,their processors can be used over the grid. Using thepower of the campus grid, Dr. Rayburn’s students cannow use sophisticated architectural design softwarethat previously was unavailable because of its processingrequirements. With the software, students candesign buildings and other structures as well as the areassurrounding them, and create three-dimensional,interactive animations of their designs. As presentations,the animations allow viewers to “fly” over andaround the scenes the students generate, zooming inand out and moving in any direction they want to go.The university’s grid supplies enough unused computingpower to process the animations fast enough for itall to function smoothly.After several weeks of using the software, two of Dr.Rayburn’s students persuade faculty in the meteorologydepartment to connect a very large climatic databaseto the grid. The database includes data about theexact positioning of the sun and moon at any latitudeon the globe during daily, monthly, and yearly cycles,as well as historical data on weather conditions formost parts of the world. With the database availableon the grid, the students can incorporate seasonalchanges into their animations. They can render a buildingat a particular latitude, at a specific time of the yearor spanning weeks or months. Dr. Rayburn sees thatwith the new capabilities, his students are able to createbetter designs, ones that make more creative useof natural light—even as seasons change—and thatdemonstrate students’ deliberation about how theirstructures interact with the environment. What is it? Computing grids are conceptually not unlike electrical grids. In anelectrical grid, wall outlets allows us to link to an infrastructure ofresources that generate, distribute, and bill for electricity. When youconnect to the electrical grid, you don’t need to know where thepower plant is or how the current gets to you. Grid computing usesmiddleware to coordinate disparate IT resources across a network,allowing them to function as a virtual whole. The goal of a computinggrid, like that of the electrical grid, is to provide users withaccess to the resources they need, when they need them.Grids address two distinct but related goals: providing remoteaccess to IT assets, and aggregating processing power. The mostobvious resource included in a grid is a processor, but grids alsoencompass sensors, data-storage systems, applications, andother resources. One of the first commonly known grid initiativeswas the SETI@home project, which solicited several million volunteersto download a screensaver that used idle processor capacityto analyze data in the search for extraterrestrial life. In a morerecent example, the Telescience Project provides remote access toan extremely powerful electron microscope at the National Centerfor Microscopy and Imaging Research in San Diego. Users of thegrid can remotely operate the microscope, allowing new levels ofaccess to the instrument and its capabilities. Who’s doing it? Many grids are appearing in the sciences, in fields such as chemistry,physics, and genetics, and cryptologists and mathematicianshave also begun working with grid computing. Grid technology hasthe potential to significantly impact other areas of study with heavycomputational requirements, such as urban planning. Anotherimportant area for the technology is animation, which requiresmassive amounts of computational power and is a common tool ina growing number of disciplines. By making resources available tostudents, these communities are able to effectively model authenticdisciplinary practices. How does it work? Grids use a layer of middleware to communicate with and manipulateheterogeneous hardware and data sets. In some fields—astronomy, for example—hardware cannot reasonably be movedand is prohibitively expensive to replicate on other sites. In other
19-02-2011, 03:32 PM
Grid Computing.ppt (Size: 269.5 KB / Downloads: 219) Grid Computing Terminology Authentication: – Establishing who you are Authorization: – Establishing what you are allowed to do Assurance/accreditation – Validating authority of a service provider Accounting and auditing – Tracking, limiting and charging for resources Messages – Message integrity – Message confidentiality Non-repudiation – Proof that you got the message Digital signature – Assurance about the message Certificate authority – A body which issues and manages security credentials Delegation – Authority to act as someone else TLS/SSL TLS: Transport Layer Security Protocol is the successor to SSL: Secure Socket Layer. Secured Sockets Layer is a protocol that transmits your communications over the Internet in an encrypted form. SSL ensures that the information is sent, unchanged, only to the server you intended to send it to. Lies above TCP/IP layer and below HTTP layer. Developed by Netscape for transmitting private documents via the Internet. SSL works by using a private key to encrypt data that's transferred over the SSL connection. Both Netscape Navigator and Internet Explorer support SSL, and many Web sites use the protocol to obtain confidential user information, such as credit card numbers. By convention, URLs that require an SSL connection start with https: instead of http:. http://wp.netscapeeng/ssl3/ http://www.ietfhtml.charters/tls-charter.html Requires a direct transport layer between endpoints Public Key Encryption Entity generates two keys, one is designated as the public key, one is the private key. The private key must be kept private! Public key is given out (eg in an X.509 certificate) If one key is used to encrypt a message, the other key must be used to decrypt it. Possession of private key (and ability to encrypt/decrypt challenge messages) proves ownership. Encryption method is public knowledge so does not provide data integrity or authentication of data origin Slower than other methods (not so good for bulk transfer or lots of small items) Based on belief that it is not possible to determine the decryption mechanism from the encryption mechanism. More secure than username/password (requires passphrase and possession of private key. Security relies on identify establishment.
26-02-2011, 10:43 AM
PRESENTED BY:
SAI SANDEEP TIIRLANGI NAGURBABU CHINNAM GRID COMPUTING.doc (Size: 2.37 MB / Downloads: 82) ABSTRACT Today we are in the Internet world and everyone prefers to enjoy fast access to the Internet. But due to multiple downloading, there is a chance that the system hangs up or slows down the performance that leads to the restarting of the entire process from the beginning. This is one of the serious problems that need the attention of the researchers. So we have taken this problem for our research and in this paper we are providing a layout for implementing our proposed Grid Model that can access the Internet very fast. By using our Grid we can easily download any number of files very fast depending on the number of systems employed in the Grid. We have used the concept of Grid Computing for this purpose. The Grid formulated by us uses the standard Globus Architecture, which is the only Grid Architecture currently used world wide for developing the Grid. And we have proposed an algorithm for laying our Grid Model that we consider as a blueprint for further implementation. When practically implemented, our Grid provides the user to experience the streak of lightening over the Internet while downloading multiple files. Key words: Grid Security Interface (GSI), Global Access to Secondary Storage (GASS), Monitoring and Discovery Service (MDS), Globus Resource Allocation Manager (GRAM). CPU cycles can be efficiently used by uniting pools of servers, storage systems and networks into a single large virtual system for resource sharing dynamically at runtime. These systems can be distributed across the globe; they're heterogeneous (some PCs, some servers, maybe mainframes and supercomputers); somewhat autonomous (a Grid can potentially access resources in different organizations). Although Grid computing is firmly ensconced in the realm of academic and research activities, more and more companies are starting to turn to it for solving hard-nosed, real-world problems. WHAT IS GRID? “Resource sharing & coordinated problem solving in dynamic, multi-institutional virtual organizations”. IMPORTANCE OF GRID COMPUTING Grid computing is emerging as a viable technology that businesses can use to wring more profits and productivity out of IT resources -- and it's going to be up to you developers and administrators to understand Grid computing and put it to work It's really more about bringing a problem to the computer (or Grid) and getting a solution to that problem. Grid computing is flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources. Grid computing enables the virtualization of distributed computing resources such as processing, network bandwidth, and storage capacity to create a single system image, granting users and applications seamless access to vast IT capabilities. Just as an Internet user views a unified instance of content via the World Wide Web, a Grid user essentially sees a single, large, virtual computer. Grid computing will give worldwide access to a network of distributed resources - CPU cycles, storage capacity, devices for input and output, services, whole applications, and more abstract elements like licenses and certificates. For example, to solve a compute-intensive problem, the problem is split into multiple tasks that are distributed over local and remote systems, and the individual results are consolidated at the end. Viewed from another perspective, these systems are connected to one big computing Grid. The individual nodes can have different architectures, operating systems, and software versions. Some of the target systems can be clusters of nodes themselves or high performance servers. WHY GRIDS AND WHY NOW? • A biochemist exploits 10, 000 computers to screen 100,000 compounds in an hour • 1,000 physicists worldwide pool resources for petaop analyses of petabytes of data • Civil engineers collaborate to design, execute, & analyze shake table experiments • Climate scientists visualize, annotate, & analyze terabyte simulation datasets • An emergency response team couples real time data, weather model, population data • A multidisciplinary analysis in aerospace couples code and data in four companies • A home user invokes architectural design functions at an application service provider • Scientists working for a multinational soap company design a new product • A community group pools members’ PCs to analyze alternative designs for a local road Why Now? The following are the reasons why now we are concentrating on Grids: • Moore’s law improvements in computing produce highly functional end systems • The Internet and burgeoning wired and wireless provide universal connectivity • Changing modes of working and problem solving emphasize teamwork, computation • Network exponentials produce dramatic changes in geometry and geography The network potentials are as follows: Network vs. computer performance • Computer speed doubles every 18 months • Network speed doubles every 9 months • Difference = order of magnitude per 5 years
08-03-2011, 10:42 AM
GRID TECHNOLOGY.doc (Size: 932.5 KB / Downloads: 123) 1. ABSTRACT: The last decade has seen a substantial increase in computer and network performance, mainly as a result of faster hardware and more sophisticated software. Nevertheless, there are still problems, in the fields of science, engineering, and business, which cannot be effectively dealt with using the current generation of supercomputers. In fact, due to their size and complexity, these problems are often very data intensive and consequently require a variety of heterogeneous resources that are not available on a single machine. A number of teams have conducted experimental studies on the cooperative use of geographically distributed resources unified to act as a single powerful computer. This new approach is known by several names, such as metacomputing, scalable computing, global computing, Internet computing, and more recently peer-to-peer or Grid computing. Grid computing provides key infrastructure for distributed problem solving in dynamic virtual environments. It has been adopted by many scientific projects, and industrial interest is rising rapidly. However, Grids are still the domain of a few highly trained programmers with expertise in networking, high-performance computing, and operating systems. The early efforts in Grid computing started as a project to link supercomputing sites, but have now grown far beyond their original intent. In fact, many applications can benefit from the Grid infrastructure, including collaborative engineering, data exploration, high-throughput computing, and of course distributed supercomputing. Moreover, due to the rapid growth of the Internet and Web, there has been a rising interest in Web-based distributed computing, and many projects have been started and aim to exploit the Web as an infrastructure for running distributed and parallel applications. In this context, the Web has the capability to be a platform for parallel and collaborative work as well as a key technology to create a pervasive and ubiquitous Grid-based infrastructure. 2. INTRODUCTION: Parallel supercomputers continue to increase in power and in ability to solve very large and complex problems in computational science. For many users, however, there are a number of practical limitations associated with these machines, including their high cost, the difficulty of obtaining access to them, and the difficulty of writing or procuring software tools that execute on them. In recent years, there has been a good deal of interest in alternative computing platforms known as computational grids, which are made up of large collections of geographically dispersed CPUs, storage, and visualization devices linked by local networks and the Internet. Of particular interest to the optimization community are computational grids that are made up of workstations, PCs and PC clusters, and supercomputer nodes, and which may be owned by a number of different individuals and institutions. Grids grant access to computer cycles that would not otherwise be used by the owners of the machines of which they are composed, without interfering with the computing activities of the machine owners. A key contribution of Grid computing is the potential for seamless aggregations of and interactions among computing, data, and information resources, which is enabling a new generation of scientific and engineering applications that are self-optimizing and dynamic data driven. However, achieving this goal requires a service-oriented Grid infrastructure that leverages standardized protocols and services to access hardware, software, and information resources Usually, grids provide sophisticated interfaces to distributed resources management as well as application execution and monitoring in wide and local area networks. These networks may connect thousands of computers by high-speed of up to 40 Gigabits/sec links. The computing resources include nodes made of thousands of processors, and terabytes of storage media. Grid resources can be used to solve grand challenge problems in areas such as biophysics, chemistry, biology, scientific instrumentation, drug design, high energy physics, data mining, financial analysis, nuclear simulations, material science, chemical engineering, environmental studies, climate modeling, weather prediction, molecular biology, neuroscience/brain activity analysis, structural analysis, mechanical CAD/CAM, and astrophysics. 3. DISTRIBUTED COMPUTING: Distributed Computing is an environment in which a group of independent and geographically dispersed computer systems take part to solve a complex problem, each by solving a part of the problem and then combining the result from all computers. It utilizes a network of many computers, each accomplishing a portion of an overall task, to achieve a computational result much more quicker than with a single computer. Distributed Computing normally refers to managing or pooling of hundreds or thousands of computer systems which individually are limited in their memory and processing power. These systems are loosely coupled systems coordinately working with a common goal. 4. THE BASICS OF GRID COMPUTING: The term grid computing originated in the early 1990s as a metaphor for making computer power as easy to access as an electrical power grid. Grid computing is a form of distributed computing that involves coordinating and sharing computing, application, data, storage or network resources across dynamic and geographically dispersed organizations. However the vision of a large scale resource sharing is not yet a reality in many areas as Grid computing is an evolving area of computing, while standards and technology are still being developed to enable this new paradigm. Grid computing offers a model for solving massive computational problems by making use of the unused resources (CPU cycles and/or disk storage) of large numbers of disparate, often desktop, computers treated as a virtual cluster embedded in a distributed telecommunications infrastructure. It is an emerging computing model that provides the ability to perform higher throughput computing by taking advantage of many networked computers to model a virtual computer architecture that is able to distribute process execution across a parallel infrastructure. Grids use the resources of many separate computers connected by a network (usually the internet) to solve large-scale computation problems. They provide the ability to perform computations on large data sets, by breaking them down into many smaller ones, or provide the ability to perform many more computations at once than would be possible on a single computer, by modeling a parallel division of labour between processes. Many use the idle time on many thousands of computers throughout the world. Such arrangements permit handling of data that would otherwise require the power of expensive super computers or would have been impossible to analyze otherwise. Grid computing has the design goal of solving problems too big for any single supercomputer, while retaining the flexibility to work on multiple smaller problems. Thus grid computing provides a multi-user environment. Its secondary aims are: better exploitation of the available computing power, and catering for the intermittent demands of large computational exercises. Grid Computing can be seen as a super set of distributed computing. Functionally one can classify grids into several types: • Computational Grids: which focus primarily on computationally intensive operations. • Data Grids: which control the sharing or management of large amount of distributed data. • Equipment Grids: which have a primary piece of equipment e.g., a telescope, and where the surrounding grid is used to control the equipment remotely and to analyze the data produced. Many projects using grid computing are covering tasks such as protein folding, research into drugs for cancer, mathematical problems and climate models. Most of these projects work by running as a screensaver on users' personal computers, which process small pieces of the overall data while the computer is either completely idle or lightly used. These programs generally run in the background or as a screensaver when the user does not use the entire computing power of the PC. Many such projects have made progress in fields that would have otherwise taken prohibitive investment or a delay in/on results.
11-03-2011, 02:13 PM
Presented BY:
BISMITA BARIK bismita slides.pptx (Size: 4.32 MB / Downloads: 68) Grid computing ”The Computational Grid” is analogous to Electricity (Power) Grid and the vision is to offer a (almost) dependable, consistent, pervasive, and inexpensive access to high-end resources irrespective their location of physical existence and the location of access. HISTORY THE TERM COMPUTING ORIGINATED IN 1990’S AS A METAPHOR FOR MAKING COMPUTER POWER AS EASY TO ACCESS AS ELECTRIC POWER GRID. THE IDEAS OF GRID WERE BROUGHT TOGETHER BY IAN FOSTER,CARL KELESSMAN,AND STEVE TEUCKER WIDELY REGARDED AS “””FATHER OF GRID”””” Scalable HPC: Breaking Administrative Barriers WHY IS IT USED? USING A GRID Title A Typical Grid Computing Environment ARCHITECTURE A Layered View of a Grid Computers, supercomputers, storage devices, instruments … FEATURES…………… Grid computing is driven by five big areas: Secure access: Trust between resource providers and users is essential, especially when they don't know each other. Sharing resources conflicts with security policies in many individual computer centers, and on individual PCs, so getting grid security right is crucial Resource sharing: Global sharing is the very essence of grid computing. Resource use: Efficient, balanced use of computing resources is essential. The death of distance: Distance should make no difference: you should be able to access to computer resources from wherever you are. Open standards: Interoperability between different grids is a big goal, and is driven forward by the adoption of open standards for grid development, making it possible for everyone can contribute constructively to grid development. Standardization also encourages industry to invest in developing commercial grid services and infrastructure. How does a Grid Service work? Client uses a Grid service interface A grid service instance is created from a Factory with the help of a Registry The grid service instances run with appropriate resources automatically allocated New instances can allocated and destroyed dynamically, to benefit performance Example: A web serving environment could dynamically allocate extra instances to provide consistent user response time Simple Invocation Example Grid: Towards Internet Computing for (Coordinated) Resource Sharing Drug Design: Data Intensive Computing on Grid It involves screening millions of chemical compounds (molecules) in the Chemical DataBase (CDB) to identify those having potential to serve as drug candidates. Title Main components Title REAL GRIDS…………………… Typical current grid Virtual organisations negotiate with sites to agree access to resources Grid middleware runs on each shared resource to provide Data services Computation services Single sign-on Distributed services (both people and middleware) enable the grid Grid initiatives
17-03-2011, 02:29 PM
Abstract1 (1).doc (Size: 310 KB / Downloads: 59) Abstract Grid computing, emerging as a new paradigm for next-generation computing, enables the sharing, selection, and aggregation of geographically distributed heterogeneous resources for solving large-scale problems in science, engineering, and commerce. The resources in the Grid are heterogeneous and geographically distributed. Availability, usage and cost policies vary depending on the particular user, time, priorities and goals. It enables the regulation of supply and demand for resources. It provides an incentive for resource owners to participate in the Grid; and motivates the users to trade-off between deadline, budget, and the required level of quality of service. The thesis demonstrates the capability of economic-based systems for wide-area parallel and distributed computing by developing users’ quality-of-service requirements-based scheduling strategies, algorithms, and systems. It demonstrates their effectiveness by performing scheduling experiments on the World-Wide Grid for solving parameter sweep—task and data parallel—applications. This paper focuses on introduction, grid definition.It covers about grid characteristics, types of grids and an example describing a community grid model. It gives an overview of grid tools, various components, advantages followed by conclusion. 1. INTRODUCTION: This The Grid unites servers and storage into a single system that acts as a single computer - all your applications tap into all your computing power. Hardware resources are fully utilized and spikes in demand are met with ease. This Web site sponsored by Oracle brings you the resources you need to evaluate your organization's adoption of grid technologies. The Grid is ready when you are. 2. THE GRID: The Grid is the computing and data management infrastructure that will provide the electronic underpinning for a global society in business, government, research, science and entertainment, integrate networking, communication, computation and information to provide a virtual platform for computation and data management in the same way that the Internet integrates resources to form a virtual platform for information. The Grid is the computing and data management infrastructure that will provide the electronic. Grid infrastructure will provide us with the ability to dynamically link together resources as an ensemble to support the execution of large-scale, resource-intensive, and distributed applications. Grid is a type of parallel and distributed system that enables the sharing, selection, and aggregation of geographically distributed "autonomous" resources dynamically at runtime depending on their availability, capability, performance, cost, and users' quality-of-service requirements. What Grid Can Do? • Exploiting underutilized resources: In most organizations, there are large amounts of underutilized computing resources. Most desktop machines are busy less than 5 percent of the time. In some organizations, even the server machines can often be relatively idle. Grid computing provides a framework for exploiting the underutilized resources and thus has the possibility of substantially increasing the efficiency of resource usage. Another function of the grid is to better balance resource utilization. An organization may have occasional unexpected peaks of activity that demand more resources. If the applications are grid-enabled, they can be moved to underutilized machines during such peaks. In fact, some grid implementations can migrate partially completed jobs. In general, a grid can provide a consistent way to balance the loads on a wider federation of resources. This applies to CPU, storage, and many other kinds of resources that may be available on a grid. • Parallel CPU capacity The potential for massive parallel CPU capacity is one of the most attractive features of a grid. In addition to pure scientific needs, such computing power is driving a new evolution in industries such as the bio-medical field, financial modeling, oil exploration, motion picture animation, and many others. The common attribute among such uses is that the applications have been written to use algorithms that can be partitioned into independently running parts. A CPU intensive grid application can be thought of as many smaller “sub jobs,” each executing on a different machine in the grid. To the extent that these sub jobs do not need to communicate with each other, the more “scalable” the application becomes. A perfectly scalable application will, for example, finish 10 times faster if it uses 10 times the number of processors. Barriers often exist to perfect scalability. The first barrier depends on the algorithms used for splitting the application among many CPUs. If the algorithm can only be split into a limited number of independently running parts, then that forms a scalability barrier. The second barrier appears if the parts are not completely independent; this can cause contention, which can limit scalability. For example, if all of the sub jobs need to read and write from one common file or Database, the access limits of that file or database will become the limiting factor in the application’s scalability.
20-03-2011, 10:10 AM
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22-03-2011, 10:03 AM
Presented by:
Priyanka Sainik Grid computing is a term referring to the combination of computer resources from multiple administrative domains to reach a common goal. The grid can be thought of as a distributed system with non-interactive workloads that involve a large number of files. What distinguishes grid computing from conventional high performance computing systems such as cluster computing is that grids tend to be more loosely coupled, heterogeneous, and geographically dispersed. Although a grid can be dedicated to a specialized application, it is more common that a single grid will be used for a variety of different purposes. Grids are often constructed with the aid of general-purpose grid software libraries known as middleware. Grid size can vary by a considerable amount. Grids are a form of distributed computing whereby a “super virtual computer” is composed of many networked loosely coupled computers acting together to perform very large tasks. Furthermore, “distributed” or “grid” computing, in general, is a special type of parallel computing that relies on complete computers (with onboard CPUs, storage, power supplies, network interfaces, etc.) connected to a network (private, public or the Internet) by a conventional network interface, such as Ethernet. This is in contrast to the traditional notion of a supercomputer, which has many processors connected by a local high-speed computer bus.
25-03-2011, 03:34 PM
Grid computing(3).doc (Size: 54 KB / Downloads: 70) TREND ON ADVANCED GRID COMPUTING 1. ABSTRACT Grid computing is a method of harnessing the power of many computers in a network to solve problems requiring a large number of processing cyc1es and involving huge amounts of data. The grid computing helps in exploiting underutilized resources, achieving parallel CPU capacity; provide virtual resources for collaboration and reliability. Although commercial and research organizations might have collaborative or monetary reasons to share resources, they are unlikely to adopt such a distributed infrastructure until they can rely on the confidentiality of the communication, the integrity of their data and resources, and the privacy of the user information. In other words, large-scale deployment of grids will occur when users can count on their security. Most organizations today deploy firewalls around their computer networks to protect their sensitive proprietary data. But the central idea of grid computing-to enable resource sharing makes mechanisms such as firewalls difficult to use. On the grid, participants form virtual organizations dynamically, and the trust established prior to such collaborations often takes place at the organizational rather than the individual level. Thus, expressing restrictive policies on a user-by-user basis often proves difficult. Also, frequently a single transaction takes place across many grid nodes that are dynamic and unpredictable. Finally, unlike the Internet, a grid gives outsiders complete access to a resource, thus increasing the security risk. Grid security is a multidimensional problem. Organizations participating in grids must use appropriate policies, such as firewalls, to harden their Infrastructures while enabling interaction with outside resources. In this paper, we briefly describe the reasons for using grid computing and analyze the unique security requirements of large-scale grid computing. We propose a security policy for grid systems that addresses requirements for single sign-on, interoperability with local policies, and dynamically varying resource requirements. This policy focuses on authentication of users, resources, and processes and supports user-to resource, resource to user, process-to-resource, and Process to process authentication. We also describe security architecture and associated protocols that implement this policy. 2. INTRODUCTION Grid Computing is a method of harnessing the power of many computers in a network to solve problems requiring a large number of processing cycles and involving huge amounts of data. Grid applications are distinguished from traditional client server applications by their simultaneous use of large numbers of resources, dynamic resource requirements, use of resources from multiple administrative domains, complex communication structures and stringent performance requirements, among others. While scalability, performance and heterogeneity are desirable goals for any distributed system, the characteristics of computational grids lead to security problems that are not addressed by existing security technologies for distributed systems. For example parallel computations that acquire multiple computational resources introduce the need to establish security relationships not simply between a client and a server, but among potentially hundreds of processes that collectively span many administrative domains. Further more, the dynamic nature of grid can make it impossible to establish trust relationships between sites prior to application execution. Finally, by inter domain security solutions used for grids must be able to inter operate with, rather than replace, the diverse intra domain access control technologies inevitable encountered in individual domains. In this paper, we describe new techniques that overcome many of the cited difficulties. We propose a security policy for grid systems that addresses requirements for single sign-on, inter operability with local policies, and dynamically varying resource requirements. This policy focuses on authentication of users, resources, and processes and supports user-to-resource, resource-to-user, process-to-resource, and process-to-process authentication. 2. Reasons for using Grid Computing When you deploy a grid, it will be to meet a set of customer requirements. To better match grid computing capabilities to those requirements, it is useful to keep in mind the reasons for using grid computing. Exploiting underutilized resources The easiest use of grid computing is to run an existing application on a different machine. The machine on which the application is normally run might be unusually busy due to an unusual peak in activity. The job in question could be run on an idle machine elsewhere on the grid. There are at least two prerequisites for this scenario. First, the application must be executable remotely and without undue overhead. Second, the remote machine must meet any special hardware, software, or resource requirements imposed by the application. For example, a batch job that spends a significant amount of time processing a set of input data to produce an output set is perhaps the most ideal and simple use for a grid. If the quantities of input and output are large, more thought and planning might be required to efficiently use the grid for such a job. It would usually not make sense to use a word processor remotely on a grid because there would probably be greater delays and more potential points of failure. In most organizations, there are large amounts of underutilized computing resources. Most desktop machines are busy less than 5 percent of the time. In some organizations, even the server machines can often be relatively idle. Grid computing provides a framework for exploiting these underutilized resources and thus has the possibility of substantially increasing the efficiency of resource usage. The processing resources are not the only ones that may be underutilized. Often, machines may have enormous unused disk drive capacity. Grid Computing, more specifically, a “data grid”, can be used to aggregate this unused storage into a much larger virtual data store, possibly configured to achieve improved performance and reliability over that of any single machine. If a batch job needs to read a large amount of data, this data could be automatically replicated at various strategic points in the grid. Thus, if the job must be executed on a remote machine in the grid, the data is already there and does not need to be moved to that remote point. This offers clear performance benefits. Also, such copies of data can be used as backups when the primary copies are damaged or unavailable. Parallel CPU capacity The potential for massive parallel CPU capacity is one of the most attractive features of a grid. In addition to pure scientific needs, such computing power is driving a new evolution in industries such as the bio-medical field, financial modeling, oil exploration, motion picture animation, and many others. The common attribute among such uses is that the applications have been written to use algorithms that can be partitioned into independently running parts. A CPU intensive grid application can be thought of as many smaller “sub jobs,” each executing on a different machine in the grid. To the extent that these sub jobs do not need to communicate with each other, the more “scalable” the application becomes. A perfectly scalable application will, for example, finish 10 times faster if it uses 10 times the number of processors. Barriers often exist to perfect scalability. The first barrier depends on the algorithms used for splitting the application among many CPUs. If the algorithm can only be split into a limited number of independently running parts, then that forms a scalability barrier. The second barrier appears if the parts are not completely independent; this can cause contention, which can limit scalability. For example, if all of the sub jobs need to read and write from one common file or database, the access limits of that file or database will become the limiting factor in the application’s scalability. Other sources of inter-job contention in a parallel grid application include message communications latencies among the jobs, network communication capacities, synchronization protocols, input-output bandwidth to devices and storage devices, and latencies interfering with real-time requirements. Other sources of inter-job content in parallel grid application include message communications latencies among the jobs. Virtual resources and Virtual Organizations for Collaborations Another important grid computing contribution is to enable and simplify collaboration among a wider audience. In the past, distributed computing promised this collaboration and achieved it to some extent. Grid computing takes these capabilities to an even wider audience, while offering important standards that enable very heterogeneous systems to work together to form the image of a large virtual computing system offering a variety of virtual resources. The users of the grid can be organized dynamically into a number of virtual organizations, each with different policy requirements. These virtual organizations can share their resources collectively as a larger grid. Sharing starts with data in the form of files or databases. A “data grid” can expand data capabilities in several ways. First, files or databases can seamlessly span many systems and thus have larger capacities than on any single system. Such spanning can improve data transfer rates through the use of striping techniques. Data can be duplicated throughout the grid to serve as a backup and can be hosted on or near the machines most likely to need the data, in conjunction with advanced scheduling techniques. Sharing is not limited to files, but also includes many other resources, such as equipment, software, services, licenses, and others. These resources are “virtualized” to give them a more uniform interoperability among heterogeneous grid participants. Reliability High-end conventional computing systems use expensive hardware to increase reliability. They are built using chips with redundant circuits that vote on results, and contain much logic to achieve graceful recovery from an assortment of hardware failures. The machines also use duplicate processors with hot plug ability so that when they fail, one can be replaced without turning the other off. Power supplies and cooling systems are duplicated. The systems are operated on special power sources that can start generators if utility power is interrupted. All of this builds a reliable system, but at a great cost, due to the duplication of high-reliability components. In the future, we will see a complementary approach to reliability that relies on software and hardware. A grid is just the beginning of such technology. The systems in a grid can be relatively Inexpensive and geographically dispersed. Thus, if there is a power or other kind of failure at one location, the other parts of the grid are not likely to be affected. Grid management software can automatically resubmit jobs to other machines on the grid when a failure is detected. In critical, real-time situations, multiple copies of the important jobs can be run on different machines throughout the grid. Their results can be checked for any kind of inconsistency, such as computer failures, data corruption, or tampering. Such grid systems will utilize “autonomic computing.” This is a type of software that automatically heals problems in the grid, perhaps even before an operator or manager is aware of them. In principle, most of the reliability attributes achieved using hardware in today’s high availability systems can be achieved using software in a grid setting in the future. Resource balancing A grid federates a large number of resources contributed by individual machines into a greater total virtual resource. For applications that are grid-enabled, the grid can offer a resource balancing effect by scheduling grid jobs on machines with low utilization. This feature can prove invaluable for handling occasional peak loads of activity in parts of a larger organization. This can happen in two ways: An unexpected peak can be routed to relatively idle machines in the grid and if the grid is already fully utilized, the lowest priority work being performed on the grid can be temporarily suspended or even cancelled and performed again later to make room for the higher priority work. Without a grid infrastructure, such balancing decisions are difficult to prioritize and execute. Occasionally, a project may suddenly rise in importance with a specific deadline. A grid cannot perform a miracle and achieve a deadline when it is already too close. However, if the size of the job is known, if it is a kind of job that can be sufficiently split into sub jobs, and if enough resources are available after preempting lower priority work, a grid can bring a very large amount of processing power to solve the problem. In such situations, a grid can, with some planning, succeed in meeting a surprise deadline. 3. Security in Grid Computing a. The Grid Security Problem We introduce of grid security problem with and example illustrated in figure1. We imagine a scientist, a member of a multi-institutional scientific collaboration, who receives e-mail from a colleague regarding a new data set. He starts an analysis program, which dispatches code to the remote location where the data is stored (site C). Once started, the analysis program determines that it needs to run a simulation in order to compare the experimental results with predictions. Hence, it contacts a resource broker service maintained by the collaboration (at site D), in order to locate the resources that can be used for the simulation. The resource broker in turn initiates Computation on computers at two sites (E and G).These computers access parameter values store on a File system at another site (F) and also communicate among themselves and with broker, the original site, And the user. We imagine a scientist, a member of a multi-institutional scientific collaboration, who receives e-mail from a colleague regarding a new data set. He starts an analysis program, which dispatches code to the remote location where the data is stored (site C). Once started, the analysis program determines that it needs to run a simulation in order to compare the experimental results with predictions. Hence, it contacts a resource broker service maintained by the collaboration (at site D), in order to locate idle resources that can be used for the simulation. The resource broker in turn initiates computation on computers at two sites (E and G). These computers access parameter values stored on a file system at yet another site (F) and also communicate among themselves (perhaps using specified protocols, such as multicast) and with the broker, the original site, and the user. This example illustrates many of the distinctive characteristics of the grid computing environment: 1. The user population is large and dynamic. Participants in such virtual organizations as this scientific collaboration will include members of many institutions and will change frequently. 2. The resource pool is large and dynamic. Because individual institutions and users decide whether and when to contribute resources, the quantity and location of available resources can change rapidly. 3. A computation may require, start processes on, and release resources dynamically during its execution. Even in our simple example, the computation required resources at five sites. In other words, throughout its lifetime, a computation is composed of a dynamic group of processes running on different resources and sites. 4. The processes constituting a computation may communicate by using a variety of mechanisms, including unicast and multicast. While these processes form a single logical entity, low-level communication connection may be created and destroyed dynamically during program execution. 5. Resources may require different authentication and authorization mechanisms and policies, which we will have limited ability to change. In figure 1, we indicate this situation by showing the local access control policies that apply at the different sites. 6. An individual user will be associated with different local name spaces, credentials, or accounts, at different sites, for the purposes of accounting and access control. 7. Resources and users may be located in different countries.
26-03-2011, 09:24 AM
presented by:
Faisal N. Abu-Khzam & Michael A. Langston grid-computing.ppt (Size: 243.5 KB / Downloads: 150) GRID COMPUTING What is Grid Computing? Computational Grids – Homogeneous (e.g., Clusters) – Heterogeneous (e.g., with one-of-a-kind instruments) Cousins of Grid Computing Methods of Grid Computing Computational Grids A network of geographically distributed resources including computers, peripherals, switches, instruments, and data. Each user should have a single login account to access all resources. Resources may be owned by diverse organizations. Computational Grids Grids are typically managed by gridware. Gridware can be viewed as a special type of middleware that enable sharing and manage grid components based on user requirements and resource attributes (e.g., capacity, performance, availability…) Cousins of Grid Computing Parallel Computing Distributed Computing Peer-to-Peer Computing Many others: Cluster Computing, Network Computing, Client/Server Computing, Internet Computing, etc... Distributed Computing People often ask: Is Grid Computing a fancy new name for the concept of distributed computing? In general, the answer is “no.” Distributed Computing is most often concerned with distributing the load of a program across two or more processes. PEER2PEER Computing Sharing of computer resources and services by direct exchange between systems. Computers can act as clients or servers depending on what role is most efficient for the network. Methods of Grid Computing Distributed Supercomputing High-Throughput Computing On-Demand Computing Data-Intensive Computing Collaborative Computing Logistical Networking Distributed Supercomputing Combining multiple high-capacity resources on a computational grid into a single, virtual distributed supercomputer. Tackle problems that cannot be solved on a single system. High-Throughput Computing Uses the grid to schedule large numbers of loosely coupled or independent tasks, with the goal of putting unused processor cycles to work. On-Demand Computing Uses grid capabilities to meet short-term requirements for resources that are not locally accessible. Models real-time computing demands. Data-Intensive Computing The focus is on synthesizing new information from data that is maintained in geographically distributed repositories, digital libraries, and databases. Particularly useful for distributed data mining. Collaborative Computing Concerned primarily with enabling and enhancing human-to-human interactions. Applications are often structured in terms of a virtual shared space. Logistical Networking Global scheduling and optimization of data movement. Contrasts with traditional networking, which does not explicitly model storage resources in the network. Called "logistical" because of the analogy it bears with the systems of warehouses, depots, and distribution channels. Who Needs Grid Computing? A chemist may utilize hundreds of processors to screen thousands of compounds per hour. Teams of engineers worldwide pool resources to analyze terabytes of structural data. Meteorologists seek to visualize and analyze petabytes of climate data with enormous computational demands. An Illustrative Example Tiffany Moisan, a NASA research scientist, collected microbiological samples in the tidewaters around Wallops Island, Virginia. She needed the high-performance microscope located at the National Center for Microscopy and Imaging Research (NCMIR), University of California, San Diego. She sent the samples to San Diego and used NPACI’s Telescience Grid and NASA’s Information Power Grid (IPG) to view and control the output of the microscope from her desk on Wallops Island. Thus, in addition to viewing the samples, she could move the platform holding them and make adjustments to the microscope. The microscope produced a huge dataset of images. This dataset was stored using a storage resource broker on NASA’s IPG. Moisan was able to run algorithms on this very dataset while watching the results in real time. Grid Users Grid developers Tool developers Application developers End Users System Administrators Grid Developers Very small group. Implementers of a grid “protocol” who provides the basic services required to construct a grid. Tool Developers Implement the programming models used by application developers. Implement basic services similar to conventional computing services: – User authentication/authorization – Process management – Data access and communication Tool Developers Also implement new (grid) services such as: – Resource locations – Fault detection – Security – Electronic payment Application Developers Construct grid-enabled applications for end-users who should be able to use these applications without concern for the underlying grid. Provide programming models that are appropriate for grid environments and services that programmers can rely on when developing (higher-level) applications. System Administrators Balance local and global concerns. Manage grid components and infrastructure. Some tasks still not well delineated due to the high degree of sharing required. Some Highly-Visible Grids The NSF PACI/NCSA Alliance Grid. The NSF PACI/SDSC NPACI Grid. The NASA Information Power Grid (IPG). The Distributed Terascale Facility (DTF) Project. DTF Currently being built by NSF’s Partnerships for Advanced Computational Infrastructure (PACI) A collaboration: NCSA, SDSC, Argonne, and Caltech will work in conjunction with IBM, Intel, Quest Communications, Myricom, Sun Microsystems, and Oracle. DTF Expectations A 40-billion-bits-per-second optical network (Called TeraGrid) is to link computers, visualization systems, and data at four sites. Performs 11.6 trillion calculations per second. Stores more than 450 trillion bytes of data.
02-04-2011, 04:21 PM
Presented by:
E.Himaja Y.N.Sowjanya GRID COMPUTING.doc (Size: 139 KB / Downloads: 63) ABSTRACT Today we are in the Internet world and everyone prefers to enjoy fast access to the Internet. But due to multiple downloading, there is a chance that the system hangs up or slows down the performance that leads to the restarting of the entire process from the beginning. This is one of the serious problems that need the attention of the researchers. So we have taken this problem for our research and in this paper we are providing a layout for implementing our proposed Grid Model that can access the Internet very fast. By using our Grid we can easily download any number of files very fast depending on the number of systems employed in the Grid. We have used the concept of Grid Computing for this purpose. The Grid formulated by us uses the standard Globus Architecture, which is the only Grid Architecture currently used world wide for developing the Grid. And we have proposed an algorithm for laying our Grid Model that we consider as a blueprint for further implementation. When practically implemented, our Grid provides the user to experience the streak of lightening over the Internet while downloading multiple files. Key words: Grid Security Interface (GSI), Global Access to Secondary Storage (GASS), Monitoring and Discovery Service (MDS), Globus Resource Allocation Manager (GRAM). INTRODUCTION : What's Grid computing? Grid Computing is a technique in which the idle systems in the Network and their “ wasted “ CPU cycles can be efficiently used by uniting pools of servers, storage systems and networks into a single large virtual system for resource sharing dynamically at runtime. These systems can be distributed across the globe; they're heterogeneous (some PCs, some servers, maybe mainframes and supercomputers); somewhat autonomous (a Grid can potentially access resources in different organizations). Although Grid computing is firmly ensconced in the realm of academic and research activities, more and more companies are starting to turn to it for solving hard-nosed, real-world problems. IMPORTANCE OF GRID COMPUTING: Grid computing is emerging as a viable technology that businesses can use to wring more profits and productivity out of IT resources -- and it's going to be up to you developers and administrators to understand Grid computing and put it to work. It's really more about bringing a problem to the computer (or Grid) and getting a solution to that problem. Grid computing is flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources. Grid computing enables the virtualization of distributed computing resources such as processing, network bandwidth, and storage capacity to create a single system image, granting users and applications seamless access to vast IT capabilities. Just as an Internet user views a unified instance of content via the World Wide Web, a Grid user essentially sees a single, large, virtual computer. Grid computing will give worldwide access to a network of distributed resources - CPU cycles, storage capacity, devices for input and output, services, whole applications, and more abstract elements like licenses and certificates. For example, to solve a compute-intensive problem, the problem is split into multiple tasks that are distributed over local and remote systems, and the individual results are consolidated at the end. Viewed from another perspective, these systems are connected to one big computing Grid. The individual nodes can have different architectures, operating systems, and software versions. Some of the target systems can be clusters of nodes themselves or high performance servers. TYPES OF GRID: The three primary types of grids and are summarized below: Computational Grid A computational grid is focused on setting aside resources specifically for computing power. In this type of grid, most of the machines are high-performance servers. Scavenging grid A scavenging grid is most commonly used with large numbers of desktop machines. Machines are scavenged for available CPU cycles and other resources. Owners of the desktop machines are usually given control over when their resources are available to participate in the grid. Data Grid A data grid is responsible for housing and providing access to data across multiple organizations. Users are not concerned with where this data is located as long as they have access to the data. OUR PROPOSED GRID MODEL: We are using the Scavenging Grid for our implementation as large numbers of desktop machines are used in our Grid and later planning to extend it by using both Scavenging and data Grid. Figure1 gives an idea about the Grid that we have proposed. PROBLEMS DUE TO MULTIPLE DOWNLOADING: While accessing Internet most of us might have faced the burden of multiple downloading and in particular with downloading huge files i.e., there can be a total abrupt system failure while a heavy task is assigned to the system. The system may hang up and may be rebooted while some percentage of downloading might have been completed. This rebooting of the system leads to download of the file once again from the beginning, which is one of the major problems everyone is facing today. Let us consider N numbers of files of different sizes (in order of several MBs) are being downloaded on a single system (a PC). This will take approximately some minutes or even some hours to download it by using an Internet connection of normal speed with a single CPU. This is one of the tedious tasks for the user to download multiple files at the same time. Our Grid plays a major role here. CONCEPT OF OUR PROPOSED GRID: In order to avoid this problem we have formulated our own Grid for such an access to the Internet via an Intranet (LAN). By using our Grid these large numbers of files are distributed evenly to all the systems in the Network by using our Grid. For example we have taken into account of a small LAN that consists of around 20 systems out of which 10 systems are idle and 5 systems are using less amount of CPU(for our consideration) and their CPU cycles are wasted. And our work begins here, as we are going to efficiently utilize those “wasted CPU cycles” into “working cycles”.
09-04-2011, 10:38 AM
grid computing.doc (Size: 95.5 KB / Downloads: 49) INTRODUCTION Grid computing is a term referring to the combination of computer resources from multiple administrative domains to reach a common goal. The grid can be thought of as a distributed system with non-interactive workloads that involve a large number of files. What distinguishes grid computing from conventional high performance computing systems such as cluster computing is that grids tend to be more loosely coupled, heterogeneous, and geographically dispersed. Although a grid can be dedicated to a specialized application, it is more common that a single grid will be used for a variety of different purposes. Grids are often constructed with the aid of general-purpose grid software libraries known as middleware. Grid size can vary by a considerable amount. Grids are a form of distributed computing whereby a “super virtual computer” is composed of many networked loosely coupled computers acting together to perform very large tasks. Furthermore, “distributed” or “grid” computing, in general, is a special type of parallel computing that relies on complete computers (with onboard CPUs, storage, power supplies, network interfaces, etc.) connected to a network (private, public or the Internet) by a conventional networkinterface, such as Ethernet. This is in contrast to the traditional notion of a supercomputer, which has many processors connected by a local high-speed compute bus Grids enable the sharing, selection, and aggregation of a wide variety of resources including supercomputers, storage systems, data sources, and specialized devices that are geographically distributed and owned by different organizations for solving large-scale computational and data intensive problems in science, engineering, and commerce. Thus creating virtual organizations and enterprises as a temporary alliance of enterprises or organizations that come together to share resources and skills, core competencies, or resources in order to better respond to business opportunities or large-scale application processing requirements, and whose cooperation is supported by computer networks. PC GRID COMPUTING The cocept of grid derived from electric power grid and the terms refer to an environment in which various information processing resources(computer,storage,displays,experimental and observations equipment etc) distributed across the network are used as virtual computer.Grid computing aims to provide the necessary aount of processing resource to its operator,on demand. Its potential benefits as follows: • Collection of distributed processing resources for centralized use. • Effective utilsation of idle resource. • Load balancing to eliminate the need to maintain the processing capacity to meet the peak load. • Ensured fault tolerance for improved reliability DIVISION OF GRID ACCORDING TO CONFIGURATION 1.COMPUTING GRID A network of distributed high performance computers(e.g supercomputers) working like asingle huge computer. 2.PC GRID COMPUTING A concept similar to the computing grid.Collecting the idle CPU power of numerous PC’s to perform large -scale processing. 3.DATA GRID COMPUTING Making a grid of disk devices and file system that is remotely accessible through the network and works like a large external storage devices. 4.SENSOR GRID A group of myriad of distributed and network sensors from which data can be collected for specific purpose such as global environment monitoring system. MECHANISM OF OPERATION A PC Grid computing work as follows: 1.All participating PC owners download special software from the web server and install it on their PC’s. 2.The special software request to the central server the application programs and the data that each PC is to process as part of the grid. 3.The central server transmits the parallel processing programs and the data to the PC’s divided into packages of appropriate size. 4.The PC’s run the received programs and data during their idle CPU time as their lowest priority task. 5.When the processing is complete ,the special software returns the results to the central server and request new data.(step 3 to 5 are pepat until the entire project is finished) 6.The central server collects and compile the results returned from participating PC’s into the final results. CLASSIFICATION BY STRUCTURE • OPEN STRUCTURE This is the most common type of PC grid computing. This type of PC grid computing is connected to the internet and comprised of Pc owned by individuals who are willing to offer their Pc’s idle processing power. Such project sometimes involves a great many PC’s from a broad range of individuals. Since participation is essentially on voluntary basis, providing an incentive is the key to success. Recognizing that sending goods or real money to individual participants is not practical because of huge delivery cost, project operators are finding other cost-effective ways of rewarding the participants, such as sending electronic money, electronic mileage points or other incentive points over the network, or lottery systems. Instead of giving such financial incentives ,other grid project operators choose to appeal to people’s volunteer spirit by emphazing the contribution to social welfare, the search for truth and contribution to human advancement. Such programs are sometimes called volunteer computing because participants offer their PCs extra power for free. A famous example is SETI@home, a project operated by university of California, Berkeley. It aims to search for extraterrestrial intelligence based on data collected with a Radio Telescope. More than five millions PC’s voluntarily participate in this project from around the world.The computing power is said to reach 100 TFlops which is almost comparable to the performance of IBM’s Blue Gene/L(140 TFlpos) the worlds largest supercomputer. There are many other open grid project as shown in table. CLOSED STRUCTURE PC grids in closed structure are constructed by business enterprises and other organization, based on their existing PC’s. Organisation can have high computing power at low cost, while effective using existing resources. The benefits of creating this type of PC grid include following. Once the organisation decide to launch a project, there is no need to consider incentives for participants: the state of participating PC’s can be monitored and managed with relative ease; and since the each participants ID is known, security risk are risks are better controlled than open structure. To construct a grid using PCs within a single building, an organization can purchase a software package that easily integrates PCs connected through a LAN into a grid. SEMI-OPEN STRUCTURE Where multiple organizations that may own many PCs,these organisation can build a single network that extends beyond their boundaries to achieve high computing power. Such a grids would have a semi-open structure and allow public organizations (municipal offices, schools etc.) and local business to jointly provide the local community with shared computing resources. This is a PC grid that makes computing with shared computing resources. This is a PC grid that makes computing resources available at low cost to local small- to- medium companies that hardly afford to use supercomputers .Even large companies can benefit from such a grid because owning expensive super-computers is not always an option. Universities and research institutes in the region also enjoy this benefit. When many regions are making a variety of efforts to enhance their information infrastructure, regionally based grid computing scheme would strengthen these efforts. This is the scheme of ‘of the region, by the region , for the region’. An example of this type of grid project is a field experiment conducted in Gifu Prefecture in February 2005.Led by Gifu National college of Technology, universities, high schools, education boards, research institutes and other organizations in the prefecture participated in the project, offering over 1000 PCs. The experiment was designed to solve” the traveling salesman problem for 80 muncipalities in Gifu by using by using parallel by using parallel genetic algorithms. After the experiment the institutions involved expressed their expectations for the future if anundant computing resource were to become easily available, including reaserch project that would otherwise not be feasible, such as highly complex simulation .On the other hand, the experiment exposed social issues, such as whether each organisations rules permits its PCs time to be used for the purpose of other than the original intent and how to compensate for the difference in security policy among participating organizations.
21-04-2011, 03:13 PM
Grid Computing.doc (Size: 337 KB / Downloads: 56) ABSTRACT Grid computing can mean different things to different individuals. The grand vision is often presented as an analogy to power grids where users (or electrical appliances) get access to electricity through wall sockets with no care or consideration for where or how the electricity is actually generated. In this view of grid computing, computing becomes pervasive and individual users (or client applications) gain access to computing resources (processors, storage, data, applications, and so on) as needed with little or no knowledge of where those resources are located or what the underlying technologies, hardware, operating system, and so on are. Though this vision of grid computing can capture one’s imagination and may indeed someday become a reality, there are many technical, business, political, and social issues that need to be addressed. If we consider this vision as an ultimate goal, there are many smaller steps that need to be taken to achieve it. These smaller steps each have benefits of their own. Therefore, grid computing can be seen as a journey along a path of integrating various technologies and solutions that move us closer to the final goal. Its key values are in the underlying distributed computing infrastructure technologies that are evolving in support of cross-organizational application and resource sharing—in a word, virtualization—virtualization across technologies, platforms, and organizations. This kind of virtualization is only achievable through the use of open standards. Open standards help ensure that applications can transparently take advantage of whatever appropriate resources can be made available to them. An environment that provides the ability to share and transparently access resources across a distributed and heterogeneous environment not only requires the technology to virtualize certain resources, but also technologies and standards in the areas of scheduling, security, accounting, systems management, and so on. CHAPTER 1 What grid Computing is Grid computing can mean different things to different individuals. The grand vision is often presented as an analogy to power grids where users (or electrical appliances) get access to electricity through wall sockets with no care or consideration for where or how the electricity is actually generated. In this view of grid computing, computing becomes pervasive and individual users (or client applications) gain access to computing resources (processors, storage, data, applications, and so on) as needed with little or no knowledge of where those resources are located or what the underlying technologies, hardware, operating system, and so on are. Though this vision of grid computing can capture one’s imagination and may indeed someday become a reality, there are many technical, business, political, and social issues that need to be addressed. If we consider this vision as an ultimate goal, there are many smaller steps that need to be taken to achieve it. These smaller steps each have benefits of their own. Therefore, grid computing can be seen as a journey along a path of integrating various technologies and solutions that move us closer to the final goal. Its key values are in the underlying distributed computing infrastructure technologies that are evolving in support of cross-organizational application and resource sharing—in a word, virtualization—virtualization across technologies, platforms, and organizations. This kind of virtualization is only achievable through the use of open standards. Open standards help ensure that applications can transparently take advantage of whatever appropriate resources can be made available to them. An environment that provides the ability to share and transparently access resources across a distributed and heterogeneous environment not only requires the technology to virtualize certain resources, but also technologies and standards in the areas of scheduling, security, accounting, systems management, and so on. Grid computing could be defined as any of a variety of levels of virtualization along a continuum. Exactly where along that continuum one might say that a particular solution is an implementation of grid computing versus a relatively simple implementation using virtual resources is a matter of opinion. But even at the simplest levels of virtualization, one could say that grid-enabling technologies are being utilized. This continuum is illustrated in Figure 1-1 on page 5. Starting in the lower left you see single system partitioning. Virtualization starts with being able to carve up a machine into virtual machines. As you move up this spectrum you start to be able to virtualize similar or homogeneous resources. Virtualization applies not only to servers and CPUs, but to storage, networks, and even applications. As you move up this spectrum you start to virtualize unlike resources. The next step is virtualizing the enterprise, not just in a data center or within a department but across a distributed organization, and then, finally, virtualizing outside the enterprise, across the Internet, where you might actually access resources from a set of OEMs and their suppliers or you might integrate information across a network of collaborators. Early implementations of grid computing have tended to be internal to a particular company or organization. However, cross-organizational grids are also being implemented and will be an important part of computing and business optimization in the future. The distinctions between interorganizational grids and interorganizational grids are not based in technological differences. Instead, they are based on configuration choices given: Security domains, degrees of isolation desired, type of policies and their scope, and contractual obligations between users and providers of the infrastructures. These issues are not fundamentally architectural in nature. It is in the industry’s best interest to ensure that there is not an artificial split of distributed computing paradigms and models across organizational boundaries and internal IT infrastructures. Grid computing involves an evolving set of open standards for Web services and interfaces that make services, or computing resources, available over the Internet. Very often grid technologies are used on homogeneous clusters, and they can add value on those clusters by assisting, for example, with scheduling or provisioning of the resources in the cluster. The term grid, and its related technologies, applies across this entire spectrum. If we focus our attention on distributed computing solutions, then we could consider one definition of grid computing to be distributed computing across virtualized resources. The goal is to create the illusion of a simple yet large and powerful virtual computer out of a collection of connected (and possibly heterogeneous) systems sharing various combinations of resources. CHAPTER 2 Benefits of grid computing When you deploy a grid, it will be to meet a set of business requirements. To better match grid computing capabilities to those requirements, it is useful to keep in mind some common motivations for using grid computing. 2.1 Exploiting underutilized resources: The potential for massive parallel CPU capacity is one of the most common visions and attractive features of a grid. In addition to pure scientific needs, such computing power is driving a new evolution in industries such as the bio-medical field, financial modeling, oil exploration, motion picture animation, and many others. The common attribute among such uses is that the applications have been written to use algorithms that can be partitioned into independently running parts. A CPU-intensive grid application can be thought of as many smaller subjobs, each executing on a different machine in the grid. To the extent that these subjobs do not need to communicate with each other, the more scalable the application becomes. A perfectly scalable application will, for example, finish in one tenth of the time if it uses ten times the number of processors. Barriers often exist to perfect scalability. The first barrier depends on the algorithms used for splitting the application among many CPUs. If the algorithm can only be split into a limited number of independently running parts, then that forms a scalability barrier. The second barrier appears if the parts are not completely independent; this can cause contention, which can limit scalability. For example, if all of the sub jobs need to read and write from one common file or database, the access limits of that file or database will become the limiting factor in the application’s scalability. Other sources of inter-job contention in a parallel grid application include message communications latencies among the jobs, network communication capacities, synchronization protocols, input-output bandwidth to stoage or other devices, and other delays interfering with real-time requirements. There are many factors to consider in grid-enabling an application. One must understand that not all applications can be transformed to run in parallel on a grid and achieve scalability. Furthermore, there are no practical tools for transforming arbitrary applications to exploit the parallel capabilities of a grid. There are some practical tools that skilled application designers can use to write a parallel grid application. However, automatic transformation of applications is a science in its infancy. This can be a difficult job and often requires mathematics and programming talents, if it is even possible in a given situation. New computation-intensive applications written today are being designed for parallel execution, and these will be easily grid-enabled, if they do not already follow emerging grid protocols and standards. 2.2 Parallel CPU Capacity: The potential for massive parallel CPU capacity is one of the most common visions and attractive features of a grid. In addition to pure scientific needs, such computing power is driving a new evolution in industries such as the bio-medical field, financial modeling, oil exploration, motion picture animation, and many others. The common attribute among such uses is that the applications have been written to use algorithms that can be partitioned into independently running parts. A CPU-intensive grid application can be thought of as many smaller subjobs, each executing on a different machine in the grid. To the extent that these subjobs do not need to communicate with each other, the more scalable the application becomes. A perfectly scalable application will, for example, finish in one tenth of the time if it uses ten times the number of processors. Barriers often exist to perfect scalability. The first barrier depends on the algorithms used for splitting the application among many CPUs. If the algorithm can only be split into a limited number of independently running parts, then that forms a scalability barrier. The second barrier appears if the parts are not completely independent; this can cause contention, which can limit scalability. For example, if all of the subjobs need to read and write from one common file or database, the access limits of that file or database will become the limiting factor in the application’s scalability. Other sources of inter-job contention in a parallel grid application include message communications latencies among the jobs, network communication capacities, synchronization protocols, input-output bandwidth to storage or other devices, and other delays interfering with real-time requirements. There are many factors to consider in grid-enabling an application. One must understand that not all applications can be transformed to run in parallel on a grid and achieve scalability. Furthermore, there are no practical tools for transforming arbitrary applications to exploit the parallel capabilities of a grid. There are some practical tools that skilled application designers can use to write a parallel grid application. However, automatic transformation of applications is a science in its infancy. This can be a difficult job and often requires mathematics and programming talents, if it is even possible in a given situation. New computation-intensive applications written today are being designed for parallel execution, and these will be easily grid-enabled, if they do not already follow emerging grid protocols and standards 2.3 Virtual resources and virtual organizations for Collaboration Another capability enabled by grid computing is to provide an environment for collaboration among a wider audience. In the past, distributed computing promised this collaboration and achieved it to some extent. Grid computing can take these capabilities to an even wider audience, while offering important standards that enable very heterogeneous systems to work together to form the image of a large virtual computing system offering a variety of resources, as illustrated in Figure 2-1 on page 11. The users of the grid can be organized dynamically into a number of virtual organizations, each with different policy requirements. These virtual organizations can share their resources collectively as a larger grid. Sharing starts with data in the form of files or databases. A data grid can expand data capabilities in several ways. First, files or databases can span many systems and thus have larger capacities than on any single system. Such spanning can improve data transfer rates through the use of striping techniques. Data can be duplicated throughout the grid to serve as a backup and can be hosted on or near the machines most likely to need the data, in conjunction with advanced scheduling techniques. Sharing is not limited to files, but also includes other resources, such as specialized devices, software, services, licenses, and so on. These resourcesare virtualized to give them a more uniform interoperability among heterogeneous grid participants. The participants and users of the grid can be members of several real and virtual organizations. The grid can help in enforcing security rules among them and implement policies, which can resolve priorities for both resources and users. |
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