07-11-2012, 05:49 PM
A SEMINAR ON GREEN CLOUD COMPUING REPORT
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ABSTRACT
Cloud computing is offering utility oriented IT services to users worldwide. It enables hosting of applications from consumer, scientific and business domains. However data centers hosting cloud computing applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. With energy shortages and global climate change leading our concerns these days, the power consumption of data centers has become a key issue. Therefore, we need green cloud computing solutions that can not only save energy, but also reduce operational costs. The vision for energy efficient management of cloud computing environments is presented here. A green scheduling algorithm which works by powering down servers when they are not in use is also presented.
Green Cloud computing is envisioned to achieve not only efficient processing and utilisation of computing infrastructure, but also minimise energy consumption. This is essential for ensuring that the future growth of Cloud computing is sustainable. Otherwise, Cloud computing with increasingly pervasive front-end client devices interacting with back-end data centres will cause an enormous escalation of energy usage.
Introduction
In 1969, Leonard Kleinrock , one of the chief scientists of the original Advanced Research Projects Agency Network (ARPANET) which seeded the Internet, said: “As of now, computer networks are still in their infancy, but as they grow up and become sophisticated, we will probably see the spread of „computer utilities‟ which, like present electric and telephone utilities, will service individual homes and offices across the country.” This vision of computing utilities based on a service provisioning model anticipated the massive transformation of the entire computing industry in the 21st century whereby computing services will be readily available on demand, like other utility services available in today’s society. Similarly, users (consumers) need to pay providers only when they access the computing services. In addition, consumers no longer need to invest heavily or encounter difficulties in building and maintaining complex IT infrastructure.
In such a model, users access services based on their requirements without regard to where the services are hosted. This model has been referred to as utility computing, or recently as Cloud computing . The latter term denotes the infrastructure as a “Cloud” from which businesses and users can access applications as services from anywhere in the world on demand. Hence, Cloud computing can be classified as a new paradigm for the dynamic provisioning of computing services supported by state-of-the-art data centers that usually employ Virtual Machine (VM) technologies for consolidation and environment isolation purposes . Many computing service providers including Google, Microsoft, Yahoo, and IBM are rapidly deploying data centers in various locations around the world to deliver Cloud computing services.
Green Computing
Green computing is defined as the study and practice of designing , manufacturing, using, and disposing of computers, servers, and associated subsystems—such as monitors, printers, storage devices, and networking and communications systems—efficiently and effectively with minimal or no impact on the environment." The goals of green computing are similar to green chemistry; reduce the use of hazardous materials, maximize energy efficiency during the product's lifetime, and promote the recyclability or biodegradability of defunct products and factory waste. Research continues into key areas such as making the use of computers as energy-efficient as possible, and designing algorithms and systems for efficiency-related computer technologies.
Need of green computing in clouds
Modern data centers, operating under the Cloud computing model are hosting a variety of applications ranging from those that run for a few seconds (e.g. serving requests of web applications such as e-commerce and social networks portals with transient workloads) to those that run for longer periods of time (e.g. simulations or large data set processing) on shared hardware platforms. The need to manage multiple applications in a data center creates the challenge of on-demand resource provisioning and allocation in response to time-varying workloads. Normally, data center resources are statically allocated to applications, based on peak load characteristics, in order to maintain isolation and provide performance guarantees. Until recently, high performance has been the sole concern in data center deployments and this demand has been fulfilled without paying much attention to energy consumption. The average data center consumes as much energy as 25,000 households [20]. As energy costs are increasing while availability dwindles, there is a need to shift focus from optimising data center resource management for pure performance to optimising for energy efficiency while maintaining high service level performance. According to certain reports,the total estimated energy bill for data centers in 2010 is $11.5 billion and energy costs in a typical data center double every five years.
Making cloud computing more green
Mainly three approaches have been tried out to make cloud computing environments more environmental friendly. These approaches have been tried out in the data centres under experimental conditions. The practical application of these methods are still under study. The methods are:
• Dynamic Voltage frequency scaling technique(DVFS):- Every electronic circutory will have an operating clock associated with it. The operatin frequency of this clock is adjusted so that the supply voltage is regulated. Thus, this method heavily depends on the hardware and is not controllabale according to the varying needs. The power savings are also low compared to other approaches. The power savings to cost incurred ratio is also low.
• Resource allocation or virtual machine migration techniques:- In a cloud computing environment,every physical machine hosts a number of virtual machines upon which the applications are run. These virtual machines can be transfered across the hosts according to the varying needs and avaialble resources.The VM migration method focusses on transferring VMs in such a way that the power increase is least. The most power efficient nodes are selected and the VMs are transfered across to them. This method is dealt in detail later.
• Algorithmic approaches:- It has been experimently determined that an ideal server consumes about 70% of the power utilised by a fully utilised server. (See figure 3).
VM Migration
The problem of VM allocation can be divided in two: the first part is admission of new requests for VM provisioning and placing the VMs on hosts, whereas the second part is optimization of current allocation of VMs.
Optimization of current allocation of VMs is carried out in two steps: at the first step we select VMs that need to be migrated, at the second step chosen VMs are placed on hosts using MBFD algorithm. We propose four heuristics for choosing VMs to migrate. The first heuristic, Single Threshold (ST), is based on the idea of setting upper utilization threshold for hosts and placing VMs while keeping the total utilization of CPU below this threshold. The aim is to preserve free resources to prevent SLA violation due to consolidation in cases when utilization by VMs increases. At each time frame all VMs are reallocated using MBFD algorithm with additional condition of keeping the upper utilization threshold not violated. The new placement is achieved by live migration of VMs .
Experimental Setup
As the targeted system is a generic Cloud computing environment, it is essential to evaluate it on a large-scale virtualised data center infrastructure. However, it is difficult to conduct large-scale experiments on a real infrastructure, especially when it is necessary to repeat the experiment with the same conditions (e.g. when comparing different algorithms). Therefore, simulations have been chosen as a way to evaluate the proposed heuristics. The CloudSim toolkit has been chosen as a simulation platform as it is a modern simulation framework aimed at Cloud computing environments. In contrast to alternative simulation toolkits (e.g. SimGrid, GandSim), it supports modeling of on-demand virtualization enabled resource and application management. It has been extended in order to enable power-aware simulations as the core framework does not provide this capability. Apart from the power consumption modeling and accounting, the ability to simulate service applications with variable over time workload has been incorporated.
There are a few assumptions that have been made to simplify the model of the system and enable simulation-driven evaluation. The first assumption is that the overhead of VM migration is considered as negligible. Modeling the cost of migration of VMs is another research problem and is being currently investigated . However, it has been shown that application of live migration of VMs can provide reasonable performance overhead. Moreover, with advancements of virtualization technologies, the efficiency of VM migration is going to be improved. Another assumption is that due to unknown types of applications running on VMs, it is not possible to build the exact model of such a mixed workload . Therefore, rather than simulating particular applications, the utilization of CPU by a VM is generated as a uniformly distributed random variable. In the simulations we have defined that SLA violation occurs when a VM cannot get amount of MIPS that are requested. This can happen in cases when VMs sharing the same host require higher CPU performance that cannot be provided due to consolidation. To compare efficiency of the algorithms we use a characteristic called SLA violation percentage, or simply SLA violation, which is defined as a percentage of SLA violation events relatively to the total number of measurements.