21-07-2012, 10:10 AM
AUTONOMIC COMPUTING
AN AUTONOMIC COMPUTING[.docx (Size: 80.76 KB / Downloads: 58)
Abstract :
To deal with the increasing complexity of large-scale computing systems, computers and applications must learn to manage themselves in accordance with high-level guidance from human, a vision that has been referred to as Autonomic Computing(AC). Reducing visible complexity is the heart of the AC.AC has always been about enabling systems to take on more of their own management functions. This is accomplished through automation. So basically the top three AC concepts are reducing complexity, enabling automation and realistic implementation. Self-Management systems are the main objective of AC, and it is needed to increase the running system's reliability, stability, and performance. Reliability of the system is increased by designing systems to be self-protecting and self-healing, and autonomy and performance of the system is increased by enabling systems to adapt to changing circumstances, using self-configuring and self-optimizing mechanisms. This paper makes an overview on concept of AC, about autonomic systems a detail discussion on the four main components of AC that is self configuration, self healing, self protection and self optimization. Then it deals with the autonomic deployment model wherein the five levels of AC are discussed, we have also touched upon the architecture of AC, a brief discussion on AC and current computing.Finally, the various projects related to autonomic computing are discussed.
Introduction
Autonomic computing is a self-managing computing model named after, and patterned on, the human body's autonomic nervous system. An autonomic computing system would control the functioning of computer applications and systems without input from the user, in the same way that the autonomic nervous system regulates body systems without conscious input from the individual. The goal of autonomic computing is to create systems that run themselves, capable of high-level functioning while keeping the system's complexity invisible to the user.
Autonomic computing is one of the building blocks of pervasive computing an anticipated future computing model in which tiny - even invisible - computers will be all around us, communicating through increasingly interconnected networks.
Need for Autonomic Computing
Average complexity of each device is increasing.A general problem of modern distributed computing systems is that their complexity, and in particular the complexity of their management, is becoming a significant limiting factor in their further development. Large companies and institutions are employing large-scale computer networks for communication and computation. The distributed applications running on these computer networks are diverse and deal with many tasks, ranging from internal control processes to presenting web content and to customer support.
This creates an enormous complexity in the overall computer network which is hard to control manually by human operators. Manual control is time-consuming, expensive, and error-prone. The manual effort needed to control a growing networked computer-system tends to increase very quickly.Although computing systems have brought great benefits of speed and automation but there is now an overwhelming economic need to automate their maintenance, which is done by autonomic Forecasts suggest that the number of computing devices in use will grow at 38% per year and the computing.
Autonomic systems
A possible solution could be to enable modern, networked computing systems to manage themselves without direct human intervention. The Autonomic Computing Initiative (ACI) aims at providing the foundation for autonomic systems.
In a self managing autonomic system, the human operator takes on a new role: instead of controlling the system directly, he/she defines general policies and rules that guide the self-management process. For this process, IBM defined the following four functional areas:
Self configuring: An autonomous computing system must be able to install and set up software automatically. To do so, it will utilize dynamic software configuration techniques, which means applying technical and administrative direction and surveillance to identify and document the functional and physical characteristics of a configurable item. Also to control changes to those characteristics, to record and report change processing and implementation status, and to verify compliance with specified service levels. Also, downloading new versions of software and installing regular service packs are required. When working with other autonomous components, an autonomous system will update new signatures for virus protection and security levels. Self-configuration will use adaptive algorithms to determine the optimum configurations.
Autonomic Architecture
In an autonomic computing architecture, the basic management element is a control loop, depicted in below Figure. This acts as manager of the resource through monitoring, analysis, and actions taken on a set of predefined system policies. These control loops, or managers, can communicate and eventually will negotiate with each other and other types of resources within and outside of the autonomic computing architecture.
This control loops collects information from the system and makes decisions based on that data and then issues instructions to make adjustments to the system. An intelligent control loop can provide functionality of autonomous computing, such as the following
Autonomic Architecture
In an autonomic computing architecture, the basic management element is a control loop, depicted in below Figure. This acts as manager of the resource through monitoring, analysis, and actions taken on a set of predefined system policies. These control loops, or managers, can communicate and eventually will negotiate with each other and other types of resources within and outside of the autonomic computing architecture.
This control loops collects information from the system and makes decisions based on that data and then issues instructions to make adjustments to the system. An intelligent control loop can provide functionality of autonomous computing.
AN AUTONOMIC COMPUTING[.docx (Size: 80.76 KB / Downloads: 58)
Abstract :
To deal with the increasing complexity of large-scale computing systems, computers and applications must learn to manage themselves in accordance with high-level guidance from human, a vision that has been referred to as Autonomic Computing(AC). Reducing visible complexity is the heart of the AC.AC has always been about enabling systems to take on more of their own management functions. This is accomplished through automation. So basically the top three AC concepts are reducing complexity, enabling automation and realistic implementation. Self-Management systems are the main objective of AC, and it is needed to increase the running system's reliability, stability, and performance. Reliability of the system is increased by designing systems to be self-protecting and self-healing, and autonomy and performance of the system is increased by enabling systems to adapt to changing circumstances, using self-configuring and self-optimizing mechanisms. This paper makes an overview on concept of AC, about autonomic systems a detail discussion on the four main components of AC that is self configuration, self healing, self protection and self optimization. Then it deals with the autonomic deployment model wherein the five levels of AC are discussed, we have also touched upon the architecture of AC, a brief discussion on AC and current computing.Finally, the various projects related to autonomic computing are discussed.
Introduction
Autonomic computing is a self-managing computing model named after, and patterned on, the human body's autonomic nervous system. An autonomic computing system would control the functioning of computer applications and systems without input from the user, in the same way that the autonomic nervous system regulates body systems without conscious input from the individual. The goal of autonomic computing is to create systems that run themselves, capable of high-level functioning while keeping the system's complexity invisible to the user.
Autonomic computing is one of the building blocks of pervasive computing an anticipated future computing model in which tiny - even invisible - computers will be all around us, communicating through increasingly interconnected networks.
Need for Autonomic Computing
Average complexity of each device is increasing.A general problem of modern distributed computing systems is that their complexity, and in particular the complexity of their management, is becoming a significant limiting factor in their further development. Large companies and institutions are employing large-scale computer networks for communication and computation. The distributed applications running on these computer networks are diverse and deal with many tasks, ranging from internal control processes to presenting web content and to customer support.
This creates an enormous complexity in the overall computer network which is hard to control manually by human operators. Manual control is time-consuming, expensive, and error-prone. The manual effort needed to control a growing networked computer-system tends to increase very quickly.Although computing systems have brought great benefits of speed and automation but there is now an overwhelming economic need to automate their maintenance, which is done by autonomic Forecasts suggest that the number of computing devices in use will grow at 38% per year and the computing.
Autonomic systems
A possible solution could be to enable modern, networked computing systems to manage themselves without direct human intervention. The Autonomic Computing Initiative (ACI) aims at providing the foundation for autonomic systems.
In a self managing autonomic system, the human operator takes on a new role: instead of controlling the system directly, he/she defines general policies and rules that guide the self-management process. For this process, IBM defined the following four functional areas:
Self configuring: An autonomous computing system must be able to install and set up software automatically. To do so, it will utilize dynamic software configuration techniques, which means applying technical and administrative direction and surveillance to identify and document the functional and physical characteristics of a configurable item. Also to control changes to those characteristics, to record and report change processing and implementation status, and to verify compliance with specified service levels. Also, downloading new versions of software and installing regular service packs are required. When working with other autonomous components, an autonomous system will update new signatures for virus protection and security levels. Self-configuration will use adaptive algorithms to determine the optimum configurations.
Autonomic Architecture
In an autonomic computing architecture, the basic management element is a control loop, depicted in below Figure. This acts as manager of the resource through monitoring, analysis, and actions taken on a set of predefined system policies. These control loops, or managers, can communicate and eventually will negotiate with each other and other types of resources within and outside of the autonomic computing architecture.
This control loops collects information from the system and makes decisions based on that data and then issues instructions to make adjustments to the system. An intelligent control loop can provide functionality of autonomous computing, such as the following
Autonomic Architecture
In an autonomic computing architecture, the basic management element is a control loop, depicted in below Figure. This acts as manager of the resource through monitoring, analysis, and actions taken on a set of predefined system policies. These control loops, or managers, can communicate and eventually will negotiate with each other and other types of resources within and outside of the autonomic computing architecture.
This control loops collects information from the system and makes decisions based on that data and then issues instructions to make adjustments to the system. An intelligent control loop can provide functionality of autonomous computing.