Submitted By: R.bhanu Prasad, M.c.a(5th Semister), Reg.no: Y8mc20006.

Seminar On

AUTONOMI C computing

Submitted by: R.BHANU PRASAD, M.C.A(5th Semister), Reg.No: Y8MC20006.

Department of C S E ANU College, 1

Guntur. Abstract Autonomic computing is the technology that is building selfmanaging IT infrastructures—hardware and software that can configure, heal, optimize, and protect itself. By taking care of many of the increasingly complex management requirements of IT systems, autonomic computing allows human and physical resources to concentrate on actual business issues. Complicated tasks associated with the ongoing maintenance and management of computing systems, autonomic computing technology will allow IT workers to focus their talents on complex, big-picture projects that require a higher level of thinking and planning. The ultimate benefit of autonomic computing is freeing IT professionals to drive creativity, innovation, and opportunity.

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Contents 1. Introduction • Definition • Origin • Goals 2. Autonomic Systems 3. Autonomic networking 4. Control Loops 5. Availability 6. Autonomic computing Vs AI 7. Conclusion 8. Reference

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1.Introduction Autonomic Computing is an initiative started by IBM in 2001. Its ultimate aim is to develop computer systems capable of selfmanagement, to overcome the rapidly growing complexity of computing systems management, and to reduce the barrier that complexity poses to further growth. In other words, autonomic computing refers to the selfmanaging characteristics of distributed computing resources, adapting to unpredictable changes whilst hiding intrinsic complexity to operators and users. An autonomic system makes decisions on its own, using highlevel policies; it will constantly check and optimize its status and automatically adapt itself to changing conditions. As widely reported in literature, an autonomic computing framework might be seen composed by 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 planer/adapter for exploiting policies based on self- and environment awareness.

Driven by such vision, a variety of architectural frameworks based on “self-regulating” autonomic components has been recently proposed. A very similar trend has recently characterized significant research work in the area of multi-agent systems. However, most of these approaches are typically conceived with centralized or cluster-based server architectures in mind and mostly address the need of reducing management costs rather than the need of enabling complex software systems or providing innovative services.

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Definition Autonomic computing is an approach to self-managed computing systems with a minimum of human interference. The term derives from the body's autonomic nervous system, which controls key functions without conscious awareness or involvement. Autonomic computing is an emerging area of study and a Grand Challenge for the entire I/T community to address in earnest. The term autonomic computing derives from the body's autonomic nervous system, controlling functions like heart rate, breathing rate, and oxygen levels without a person's conscious awareness or involvement. 2 Origin Autonomic computing is the evolution of a long tradition of understanding and creating self-regulating systems. It's risen to the top of the I/T agenda because of the immediate need to solve the skills shortage and the rapidly increasing size and complexity of the world's computing infrastructure.

Self-management means different things in different fields: The number of computing devices in use is forecast to grow at 38% per annum and the average complexity of each is increasing . Currently this volume and complexity is managed by highly skilled humans; but the demand for skilled IT personnel is already outstripping supply, with labour costs exceeding equipment costs by a ratio of up to 18:1 . Computing systems have brought great benefits of speed and automation but there is now an overwhelming economic need to automate their maintenance.

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In ‘The Vision of Autonomic Computing, Kephart and Chess warn that the dream of interconnectivity of computing systems and devices could become the “nightmare of pervasive computing” in which architects are unable to anticipate, design and maintain the complexity of interactions. They state the essence of autonomic computing is system self-management, freeing administrators from low-level task management while delivering more optimal system behavior. 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 largescale computer networks for communication and computation. The distributed applications running on these computer networks are diverse and deal with many different tasks, ranging from internal control processes to presenting web content and to customer support. Additionally, Mobile computing is pervading these networks at an increasing speed: employees need to communicate with their companies while they are not in their office. They do so by using laptops, PDAs, or mobile phones with diverse forms of wireless technologies to access their companies' data. This creates an enormous complexity in the overall computer network which is hard to control manually by one or more human operators. Manual control is time-consuming, expensive, and errorprone. The manual effort needed to control a growing networked computer system tends to increase very quickly. 80% of such problems in infrastructure happen at the client specific application and database layer. Most 'autonomic' service providers guarantee only up to the basic plumbing layer (power, hardware, operating system, network and basic database parameters). 1.3

Goals

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The goal is to realize the promise of I/T: increasing productivity while minimizing complexity for users. It's time to design and build computing systems capable of running themselves, adjusting to varying circumstances, and preparing their resources to handle most efficiently the workloads we put upon them. Most immediately, the automated management of computing systems. But that capability will provide the basis for much more: from seamless e-sourcing and grid computing to dynamic e-business and the ability to translate business decisions that managers make to the I/T processes and policies that make those decisions a reality. Ultimately, autonomic computing is a challenge that must be met before the industry can deliver 'the next big thing.'

Autonomic system (computing) An autonomic system is a system that operates and serves its purpose by managing itself without external intervention even in case of environmental changes. Contents • • • •

2.1 Conceptual model 2.2 Characteristics 2.3 Relation to other definitions 2.4 Autonomicity and evolvability

Conceptual model A fundamental building block of an autonomic system is the sensing capability (Sensors Si), which enables the system to observe its external operational context. Inherent to an autonomic system is the knowledge of the Purpose (intension) and the Know-how to operate itself (e.g., boot-strapping, configuration knowledge, interpretation of sensory data, etc.) without external intervention. The actual operation of the autonomic system is dictated by the Logic, which is responsible for 7

making the right decisions to serve its Purpose, and influence by the observation of the operational context (based on the sensor input). This definition/model highlights the fact that the operation of an autonomic system is purpose-driven. This includes its mission (e.g., the service it is supposed to offer), the policies (e.g., that define the basic behaviour), and the “survival instinct”. If seen as a control system this would be encoded as a feedback error function or in a heuristically assisted system as an algorithm combined with set of heuristics bounding its operational space. Characteristics Even though the purpose and thus the behaviour of autonomic systems vary from system to system, every autonomic system should be able to exhibit a minimum set of properties to achieve its purpose: • Automatic This essentially means being able to self-control its internal functions and operations. As such, an autonomic system must be selfcontained and able to start-up and operate without any manual intervention or external help. Again, the knowledge required to bootstrap the system (Know-how) must be inherent to the system.

• Adaptive An autonomic system must be able to change its operation (i.e., its configuration, state and functions). This will allow the system to cope with temporal & spatial changes in its operational context either long term (environ-ment customisation/optimisation) or short term (exceptional conditions such as malicious attacks, faults, etc.). • Aware

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An autonomic system must be able to monitor (sense) its operational context as well as its internal state in order to be able to assess if its current operation serves its purpose. Awareness will control adaptation of its operational behaviour in response to context or state changes. Relation to other definitions The above definition is still inline with IBM’s basic vision of Autonomic Computing. However, in contrast to the herein proposed definition, which defines the properties that characterise an autonomic system, IBM’s four “autonomic self-properties”, namely self-healing, self-configuration, self-optimisation and self-protection, define a set of functionalities or features that an autonomic system must provide. For example, according to our definition a system that can operate on its own while serving its purpose is autonomic, irrespective of whether it implements those functionalities. However, it should be the nature and purpose of an autonomic system that defines which functions are required! As a result, it can be argued that the above definition is more precise (in describing a system) and at the same time more general. The above model is also consistent with the definition of a control system as well that of an AI agent. This is reasonable since practically a control system or an AI agent may easily implement an autonomic system, if it exhibits the aforementioned properties. Autonomicity and evolvability A short remark regarding the relation of autonomicity and evolvability: It has been often argued in discussions that an autonomic system ought to be evolvable (for example, through some type of artificial learning methods). However, similarly to the above discussion regarding IBM’s autonomic computing features, it can be argued that learning and evolvability may be a useful feature in an autonomic 9

system, but whether it is required or not depends on the actual purpose of the autonomic system, and should thus not be considered an essential property of an autonomic system.

Autonomic Networking Autonomic Networking follows the concept of Autonomic Computing, an initiative started by IBM in 2001. Its ultimate aim is to create self-managing networks to overcome the rapidly growing complexity of the Internet and other networks and to enable their further growth, far beyond the size of today. Contents • • •

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3.1 Increasing size and complexity 3.2 Autonomic nervous system 3.3 Components of autonomic networking o 3.3.1 Autognostics o 3.3.2 Configuration management o 3.3.3 Policy management o 3.3.4 Autodefense o 3.3.5 Security o 3.3.6 Connection fabric 3.4 Principles of autonomic networking o 3.4.1 Compartmentalization o 3.4.2 Function re-composition o 3.4.3 Atomization

Increasing size and complexity The ever-growing management complexity of the Internet caused by its rapid growth is seen by some experts as a major problem that limits its usability in the future.

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What's more, increasingly popular smartphones, PDAs, networked audio and video equipment, and game consoles need to be interconnected. Pervasive Computing not only adds features, but also burdens existing networking infrastructure with more and more tasks that sooner or later will not be manageable by human intervention alone. Another important aspect is the price of manually controlling huge numbers of vitally important devices of current network infrastructures.

Autonomic nervous system The autonomic nervous system (ANS) is the part of the nervous system of the higher life forms that is not consciously controlled. It regulates bodily functions and the activity of specific organs. As proposed by IBM, future communication systems might be designed in a similar way to the ANS. Components of autonomic networking As autonomics conceptually derives from biological entities such as the human autonomic nervous system, each of the areas can be metaphorically related to functional and structural aspects of a living being. In the human body, the autonomic system facilitates and regulates a variety of functions including respiration, blood pressure and circulation, and emotive response. The autonomic nervous system is the interconnecting fabric that supports feedback loops between internal states and various sources by which internal and external conditions are monitored.

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Autognostics includes a range of self-discovery, awareness, and analysis capabilities that provide the autonomic system with a view on high-level state. In metaphor, this represents the perceptual sub-systems that gather, analyze, and report on internal and external states and conditions – for example, this might be viewed as the eyes, visual cortex and perceptual organs of the system. Autognostics, or literally "self-knowledge", provides the autonomic system with a basis for response and validation. A rich autognostic capability may include many different "perceptual senses". For example, the human body gathers information via the usual five senses, the so-called sixth sense of proprioception (sense of body position and orientation), and through emotive states that represent the gross wellness of the body. As conditions and states change, they are detected by the sensory monitors and provide the basis for adaptation of related systems. Implicit in such a system are imbedded models of both internal and external environments such that relative value can be assigned to any perceived state - perceived physical threat (e.g. a snake) can result in rapid shallow breathing related to fight-flight response, a phylogenetically effective model of interaction with recognizable threats. In the case of autonomic networking, the state of the network may be defined by inputs from: •

• • • •

individual network elements such as switches and network interfaces including o specification and configuration o historical records and current state traffic flows end-hosts application performance data logical diagrams and design specifications

Most of these sources represent relatively raw and unprocessed views that have limited relevance. Post-processing and various forms of 12

analysis must be applied to generate meaningful measurements and assessments against which current state can be derived. The autognostic system interoperates with: • • •

configuration management - to control network elements and interfaces policy management - to define performance objectives and constraints autodefense - to identify attacks and accommodate the impact of defensive responses Configuration management is responsible for the interaction with network elements and interfaces. It includes an accounting capability with historical perspective that provides for the tracking of configurations over time, with respect to various circumstances. In the biological metaphor, these are the hands and, to some degree, the memory of the autonomic system.

On a network, remediation and provisioning are applied via configuration setting of specific devices. Implementation affecting access and selective performance with respect to role and relationship are also applied. Almost all the "actions" that are currently taken by human engineers fall under this area. With only a few exceptions, interfaces are set by hand, or by extension of the hand, through automated scripts. Implicit in the configuration process is the maintenance of a dynamic population of devices under management, a historical record of changes and the directives which invoked change. Typical to many accounting functions, configuration management should be capable of operating on devices and then rolling back changes to recover previous configurations. Where change may lead to unrecoverable states, the subsystem should be able to qualify the consequences of changes prior to issuing them.

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As directives for change must originate from other sub-systems, the shared language for such directives must be abstracted from the details of the devices involved. The configuration management subsystem must be able to translate unambiguously between directives and hard actions or to be able to signal the need for further detail on a directive. An inferential capacity may be appropriate to support sufficient flexibility (i.e. configuration never takes place because there is no unique one-to-one mapping between directive and configuration settings). Where standards are not sufficient, a learning capacity may also be required to acquire new knowledge of devices and their configuration. Configuration management interoperates with all of the other subsystems including: • • • •

autognostics - receives direction for and validation of changes policy management - implements policy models through mapping to underlying resources security - applies access and authorization constraints for particular policy targets autodefense - receives direction for changes Policy management includes policy specification, deployment, reasoning over policies, updating and maintaining policies, and enforcement. Policy management is required for:

• • • •

constraining different kinds of behavior including security, privacy, resource access, and collaboration configuration management describing business processes and defining performance defining role and relationship, and establishing trust and reputation

It provides the models of environment and behavior that represent effective interaction according to specific goals. In the human nervous system metaphor, these models are implicit in the evolutionary "design" of biological entities and specific to the goals of survival and 14

procreation. Definition of what constitutes a policy is necessary to consider what is involved in managing it. A relatively flexible and abstract framework of values, relationships, roles, interactions, resources, and other components of the network environment is required. This sub-system extends far beyond the physical network to the applications in use and the processes and end-users that employ the network to achieve specific goals. It must express the relative values of various resources, outcomes, and processes and include a basis for assessing states and conditions. Unless embodied in some system outside the autonomic network or implicit to the specific policy implementation, the framework must also accommodate the definition of process, objectives and goals. Business process definitions and descriptions are then an integral part of the policy implementation. Further, as policy management represents the ultimate basis for the operation of the autonomic system, it must be able to report on its operation with respect to the details of its implementation. The policy management sub-system interoperates (at least) indirectly with all other sub-systems but primarily interacts with: • • •

autognostics - providing the definition of performance and accepting reports on conditions configuration management - providing constraints on device configuration security - providing definitions of roles, access and permissions

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Autodefense represents a dynamic and adaptive mechanism that responds to malicious and intentional attacks on the network infrastructure, or use of the network infrastructure to attack IT resources. As defensive measures tend to impede the operation of IT, it is optimally capable of balancing performance objectives with typically over-riding threat management actions. In the biological metaphor, this sub-system offers mechanisms comparable to the immune system. This sub-system must proactively assess network and application infrastructure for risks, detect and identify threats, and define effective both proactive and reactive defensive responses. It has the role of the warrior and the security guard insofar as it has roles for both maintenance and corrective activities. Its relationship with security is close but not identical – security is more concerned with appropriately defined and implemented access and authorization controls to maintain legitimate roles and process. Autodefense deals with forces and processes, typically malicious, outside the normal operation of the system that offer some risk to successful execution. Autodefense requires high-level and detailed knowledge of the entire network as well as imbedded models of risk that allow it to analyze dynamically the current status. Corrections to decrease risk must be considered in balance with performance objectives and value of process goals – an overzealous defensive response can immobilize the system (like the immune system inappropriately invoking an allergic reaction). The detection of network or application behaviors that signal possible attack or abuse is followed by the generation of an appropriate response – for example, ports might be temporarily closed or packets with a specific source or destination might be filtered out. Further assessment generates subsequent changes either relaxing the defensive measures or strengthening them. Autodefense interoperates closely with:

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• •

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security - receives definition of roles and security constraints, and defines risk for proactive mitigation configuration management - receives details of network for analysis and directs changes in elements in response to anticipated or detected attack autognostics - receives notification of detected behaviors

It also may receive definition of relative value of various resources and processes from policy management in order to develop responses consistent with policy. Security provides the structure that defines and enforces the relationships between roles, content, and resources, particularly with respect to access. It includes the framework for definitions as well as the means to implement them. In metaphor, security parallels the complex mechanisms underlying social interactions, defining friends, foes, mates and allies and offering access to limited resources on the basis of assessed benefit. Several key means are employed by security – they include the well-known 3 As of authentication, authorization, and access (control). The basis for applying these means requires the definition of roles and their relationships to resources, processes and each other. High-level concepts like privacy, anonymity and verification are likely imbedded in the form of the role definitions and derive from policy. Successful security reliably supports and enforces roles and relationships. Autodefense has a close association with security – maintaining the assigned roles in balance with performance exposes the system to potential violations in security. In those cases, the system must compensate by making changes that may sacrifice balance on a temporary basis and indeed may violate the operational terms of security itself. Typically the two are viewed as inextricably intertwined – effective security somewhat hopefully negating any need for a defensive response. Security’s revised role is to mediate between the competing demands from policy for maximized performance and minimized risk 17

with auto defense recovering the balance when inevitable risk translates to threat. Federation represents one of the key challenges to be solved by effective security. The security sub-system interoperates directly with: • • •

policy management - receiving high-level directives related to access and priority configuration management - sending specifics for access and admission control autodefense - receiving over-riding directives under threat and sending security constraint details for risk assessment Connection fabric supports the interaction with all the elements and sub-systems of the autonomic system. It may be composed of a variety of means and mechanisms, or may be a single central framework. The biological equivalent is the central nervous system itself – although referred to as the autonomic system, it actually is only the communication conduit between the human body’s faculties.

3.4 Principles of autonomic networking Consequently, it is currently under research by many research projects, how principles and paradigms of Mother Nature might be applied to networking. Compartmentalization Instead of a layering approach, autonomic networking targets a more flexible structure termed compartmentalization. Function re-composition The goal is to produce an architectural design that enables flexible, dynamic, and fully autonomic formation of large-scale

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networks in which the functionalities of each constituent network node are also composed in an autonomic fashion. Atomization Functions should be divided into atomic units to allow for maximal re-composition freedom.

Control loops

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A basic concept that shall be applied in Autonomic Systems are closed control loops. This well-known concept stems from Process Control Theory. Essentially, a closed control loop in a self-managing system monitors some resource (software or hardware component) and autonomously tries to keep its parameters within a desired range. According to IBM, hundreds or even thousands of these control loops are expected to work in a large-scale self-managing computer system.

2.Availability Truly autonomic systems are years away, although in the nearer term, autonomic functionality will appear in servers, storage and software. Certain aspects of autonomic systems are already available. For instance, IBM's z900 eServers have a self-managing operating system known as Intelligent Resource Director (IRD). 19

More than 400 product features in 36 distinct IBM products have autonomic computing capabilities. Each of these capabilities are based in part on self-configuring, healing, optimizing or protecting technologies. They span the entire IBM product and services portfolio and we have autonomic capabilities for all sizes of businesses, including small and medium sized business.

3.Autonomic computing Vs AI Artificial Intelligence is a critical discipline that will help bring about autonomic computing. AI-related research, some involving new ways to apply control theory and control laws, can provide insight into how to run complex systems that optimize to their environments. But to be clear, autonomic computing does not require the duplication of conscious human thought as an ultimate goal. In our opinion, this is not the primary issue that needs addressing now.

4.Conclusion By taking care of many of the increasingly complex management requirements of IT systems, autonomic computing allows human and physical resources to concentrate on actual business issues. Autonomic systems are being created in this manner to recognize external threats or internal problems and then take measures to automatically prevent or correct those issues before humans even know there is a problem. These systems are also being designed to manage and proactively improve their own performance, all of which frees IT staff to focus their real intelligence on big picture projects is the ultimate goal.

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8.References Xiaolong Jin and Jiming Liu, "From Individual Based Modeling to Autonomy Oriented Computation", in Matthias Nickles, Michael Rovatsos, and Gerhard Weiss (editors), Agents and Computational Autonomy: Potential, Risks, and Solutions, pages 151–169, Lecture Notes in Computer Science, vol. 2969, Springer, Berlin, 2004. ISBN 978-3-540-22477-8. 2. ‘Trends in technology’, survey, Berkeley University of California, USA, March 2002 3. IEEE Computer Magazine, Jan 2003 1.

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