19-09-2013, 04:44 PM
Autonomic Computing
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ABSTRACT
Autonomic Computing refers to the self-managing characteristics of distributed computing resources, adapting to unpredictable changes while hiding intrinsic complexity to operators and users. Started by IBM in 2001, this initiative ultimately aims to develop computer systems capable of self-management, to overcome the rapidly growing complexity of computing systems management, and to reduce the barrier that complexity poses to further growth.
An autonomic system makes decisions on its own, using high-level policies; it will constantly check and optimize its status and automatically adapt itself to changing conditions. An autonomic computing framework is composed of autonomic components (AC) interacting with each other. An AC can be modeled in terms of two main control loops (local and global) with sensors (for self-monitoring), effectors (for self-adjustment), knowledge and planner/adapter for exploiting policies based on self- and environment awareness.
INTRODUCTION
Autonomic computing is the ability of an IT infrastructure to adapt to change in accordance with business policies and objectives. Quite simply, it is about freeing IT professionals to focus on higher-value tasks by making technology work smarter, with business rules guiding systems to be self-configuring, self-healing, self-optimizing, and self-protecting.
1. What is autonomic computing?
1.The term “autonomic” comes from an analogy to the autonomic central nervous system in the human body, which adjusts to many situations automatically without any external help. We walk up a flight of stairs and our heart rate increases. If it is hot, we perspire. If it is cold, we shiver. We do not tell ourselves to do these things, they just happen.
2.Similarly, the way to handle the problem of managing a complex IT infrastructure is to create computer systems and software that can respond to changes in the IT (and ultimately, the business) environment, so the systems can adapt, heal and protect themselves.
2.Self-healing
Self-healing IT environments can detect problematic operations (either proactively through predictions or otherwise) and then initiate corrective action without disrupting system applications. Corrective action could mean that a product alters its own state or influences changes in other elements of the environment. Day-to-day operations do not falter or fail because of events at the component level. The IT environment as a whole becomes more resilient as changes are made to reduce or help eliminate the business impact of failing components.
3.Self-optimizing
Self-optimization refers to the ability of the IT environment to efficiently maximize resource allocation and utilization to meet end users’ needs with minimal intervention. In the near term, self-optimization primarily addresses the complexity of managing system performance. In the long term, self-optimizing components may learn from experience and automatically and proactively tune themselves in the context of an overall business objective.
4.Self-protecting
A self-protecting environment allows authorized people to access the right data at the right time and can take appropriate actions automatically to make itself less vulnerable to attacks on its run-time infrastructure and business data. A self-protecting IT environment can detect hostile or intrusive behavior as it occurs and take autonomous actions to make itself less vulnerable to unauthorized access and use, viruses, denial-of-service attacks, and general failures.
Autonomic computing concepts
1. In an autonomic environment, components work together, communicating with each other and with high-level management tools. They can manage or control themselves and each other.
2. Components can manage themselves to some extent, but from an overall system standpoint, some decisions need to be made by higher level components that can make the appropriate trade-offs based on policies that are in place.
3. Let us start by looking at how a single entity is managed in an autonomic environment. The following figure represents the control loop that is the core of the autonomic architecture.
Autonomic manager knowledge
Data used by the autonomic manager’s four components are stored as shared knowledge. The shared knowledge includes things like topology information, system logs, performance metrics, and policies. The knowledge used by a particular autonomic manager could be created by the monitor part, based on the information collected through sensors, or passed into the autonomic manager through its effectors. An example of the former occurs when the monitor part creates knowledge based on recent activities by logging the notification it receives from a managed resource into a system log. An example of the latter is policy.
Policies for autonomic managers
While a detailed discussion of how policies would be implemented is outside the scope of this red book, for architectural completeness, a brief explanation is provided. An autonomic computing system requires a uniform method for defining the policies that govern the decision making for autonomic managers. A policy specifies the criteria that an autonomic manager uses to accomplish a course of action. Policies are a key part of the knowledge used by autonomic managers to make decisions, essentially controlling the planning portion of the autonomic manager. By defining policies in a standard way, they can be shared across autonomic managers to enable entire systems to be managed by a common set of policies.
Solution install
1.Today, there are a myriad of installation, configuration, and maintenance mechanisms for software solutions. Having various mechanisms creates difficulties for customers installing software in complex systems environments due to the differences and idiosyncrasies of many system administration tools and distribution packaging formats. These problems are further compounded in a Web services environment, where application functionality can be composed dynamically. From an autonomic systems perspective, lack of solution knowledge inhibits important elements of self-configuring, self-healing, self-optimizing, and self-protecting.