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Abstract
Autonomic computing systems have the ability to manage
themselves and dynamically adapt to change in accordance with business
policies and objectives. Self-managing systems can perform management
activities based on situations they observe or sense in the IT
environment. Rather than IT professionals initiating management
activities, the system observes something about itself and acts
accordingly. This allows the IT Professional to focus on high-value
tasks while the technology manages the more mundane operations.
Chapter 1
Introduction
The high-tech industry has spent decades creating computer systems with
ever mounting degrees of complexity to solve a wide variety of business
problems. Ironically, complexity itself has become part of the problem.
As networks and distributed systems grow and change, they can become
increasingly hampered by system deployment failures, hardware and
software issues, not to mention human error. Such scenarios in turn
require further human intervention to enhance the performance and
capacity of IT components. This drives up the overall IT costs”even
though technology component costs continue to decline. As a result,
many IT professionals seek ways to improve their return on investment
in their IT infrastructure, by reducing the total cost of ownership of
their environments while improving the quality of service for users. Software developers have fully exploited a four to six order-of-
magnitude increase in computational power producing ever more
sophisticated software applications and environments. There has been
exponential growth in the number and variety of systems and components.
The value of database technology and the Internet has fueled
significant growth in storage subsystems, which are now capable of
holding petabytes of structured and unstructured information. Networks
have interconnected our distributed, heterogeneous systems. Our
information society creates unpredictable and highly variable workloads
on those networked systems. And today, those increasingly valuable,
complex systems require more and more skilled IT professionals to
install, configure, operate, tune and maintain.
Autonomic computingSelf managing computing helps address
these complexity issues by using technology to manage technology. The
idea is not new many of the major players in the industry have
developed and delivered products based on this concept. Self managing
computing is also known as autonomic computing.
The term autonomic is derived from human biology. The autonomic
nervous system monitors your heartbeat, checks your blood sugar level
and keeps your body temperature close to 98.6°F, without any conscious
effort on your part. In much the same way, autonomic computingself
managing computing components anticipate computer system needs and
resolve problems with minimal human intervention.
However, there is an important distinction between autonomic activity
in the human body and autonomic responses in computer systems. Many of
the decisions made by autonomic elements in the body are involuntary,
whereas autonomic elements in computer systems make decisions based on
tasks you choose to delegate to the technology. In other words,
adaptable policy rather than rigid hard coding determines the types of
decisions and actions autonomic elements make in computer systems. Autonomic computingSelf managing computing systems have the ability to
manage themselves and dynamically adapt to change in accordance with
business policies and objectives. Self-managing systems can perform
management activities based on situations they observe or sense in the
IT environment. Rather than IT professionals initiating management
activities, the system observes something about itself and acts
accordingly. This allows the IT professional to focus on high-value
tasks while the technology manages the more mundane operations.
Autonomic computingSelf managing computing can result in a significant
improvement in system management efficiency, when the disparate
technologies that manage the environment work together to deliver
performance results system wide. However, complete autonomic systems do not yet exist. This is
not a proprietary solution. It's a radical change in the way
businesses, academia, and even the government design, develop, manage
and maintain computer systems. Autonomic computingSelf managing
computing calls for a whole new area of study and a whole new way of
conducting business.
Autonomic computingSelf managing computing is the self-management of e
-business infrastructure, balancing what is managed by the IT
professional and what is managed by the system. It is the evolution of
e-business.
Chapter 2
2.1 What is autonomic computingself managing computing?
Autonomic computingSelf managing computing is about freeing IT
professionals to focus on high-value tasks by making technology work
smarter. This means letting computing systems and infrastructure take
care of managing themselves. Ultimately, it is writing business
policies and goals and letting the infrastructure configure, heal and
optimize itself according to those policies while protecting itself
from malicious activities. Autonomic computingSelf managing computing
systems have the ability to manage themselves and dynamically adapt to
change in accordance with business policies and objectives.
In an autonomic environment the IT infrastructure and its components
are Self-managing. Systems with self-managing components reduce the
cost of owning and operating computer systems. Self-managing systems
can perform management activities based on situations they observe or
sense in the IT environment. Rather than IT professionals initiating
management activities, the system observes something about itself and
acts accordingly. This allows the IT professional to focus on high-
value tasks while the technology manages the more mundane operations.
IT infrastructure components take on the following characteristics:
self-configuring, self-healing, self-optimizing and self-protecting.
Self-configuring
Systems adapt automatically to dynamically changing environments. When
hardware and software systems have the ability to define themselves
on-the fly, they are self-configuring. This aspect of self-managing
means that new features, software, and servers can be dynamically added
to the enterprise infrastructure with no disruption of services.
Systems must be designed to provide this aspect at a feature level with
capabilities such as plug and play devices, configuration setup
wizards, and wireless server management. These features will allow
functions to be added dynamically to the enterprise infrastructure with
minimum human intervention. Self-configuring not only includes the
ability for each individual system to configure itself on the fly, but
also for systems within the enterprise to configure themselves into the
e-business infrastructure of the enterprise. The goal of autonomic
computingself managing computing is to provide self-configuration
capabilities for the entire IT infrastructure, not just individual
servers, software, and storage devices.
Self- healing
Systems discover, diagnose, and react to disruptions. For a system to
be self-healing, it must be able to recover from a failed component by
first detecting and isolating the failed component, taking it off line,
fixing or isolating the failed component, and reintroducing the fixed
or replacement component into service without any apparent application
disruption. Systems will need to predict problems and take actions to
prevent the failure from having an impact on applications. The self-
healing objective must be to minimize all outages in order to keep
enterprise applications up and available at all times. Developers of
system components need to focus on maximizing the reliability and
availability design of each hardware and software product toward
continuous availability.
Self-optimizing. Systems monitor and tune resources automatically. Self-optimization
requires hardware and software systems to efficiently maximize resource
utilization to meet end-user needs without human intervention. IBM
systems already include industry leading technologies such as logical
partitioning, dynamic workload management, and dynamic server
clustering. These kinds of capabilities should be extended across
multiple heterogeneous systems to provide a single collection of
computing resources that could be managed by a logical workload
manager across the enterprise. Resource allocation and workload
management must allow dynamic redistribution of workloads to systems
that have the necessary resources to meet workload requirements.
Similarly, storage, databases, networks, and other resources must be
continually tuned to enable efficient operations even in unpredictable
environments. Features must be introduced to allow the enterprise to
optimize resource usage across the collection of systems within their
infrastructure, while also maintaining their flexibility to meet the
ever-changing needs of the enterprise.
Self-protecting. Systems aanticipate, detect, identify, and protect themselves from
attacks from anywhere. Self-protecting systems must have the ability to
define and manage user access to all computing resources within the
enterprise, to protect against unauthorized resource access, to detect
intrusions and report and prevent these activities as they occur, and
to provide backup and recovery capabilities that are as secure as the
original resource management systems. Systems will need to build on top
of a number of core security technologies already available today.
Capabilities must be provided to more easily understand and handle user
identities in various contexts, removing the burden from
administrators.
2.2 Characteristics “ The Eight Elements
¢ To be autonomic, a system needs to know itself” and consist
of components that also possess a system identity.
¢ ¢ An autonomic system must configure and reconfigure itself under
varying and unpredictable conditions.
¢ An autonomic system never settles for the status quo”it always
looks for ways to optimize its workings.
¢ An autonomic system must perform something akin to healing”it
must be able to recover from routine and extraordinary events that
might cause some parts to malfunction.
¢ A virtual world is no less dangerous than the physical one, so
an autonomic computingself managing computing system must be an expert
in self-protection.
¢ An autonomic computingself managing computing system knows its
environment and the context surrounding its activity, and acts
accordingly.
¢ An autonomic system cannot exist in a hermetic environment (and
must adhere to open standards).
¢ Perhaps most critical for the user, an autonomic computingself
managing computing system will anticipate the optimized resources
needed to meet a userâ„¢s information needs while keeping its complexity
hidden.
Chapter 3
Path to Autonomic ComputingSelf managing computing
Delivering system wide autonomic environments is an
evolutionary process enabled by technology, but it is ultimately
implemented by each enterprise through the adoption of these
technologies and supporting processes. The path to autonomic
computingself managing computing can be thought of in five levels.
These levels, defined below, start at basic and continue through
managed, predictive, adaptive and finally autonomic.
1. 1. Basic level”a A starting point of IT environment. Each infrastructure element is
managed independently by IT professionals who set it up, monitor it and
eventually replace it. 2. 2. Managed level”
Ssystems management technologies can be used to collect
information from disparate systems onto fewer consoles, reducing the
time it takes for the administrator to collect and synthesize
information as the IT environment becomes more complex.
3. 3. Predictive level
”Nnew technologies are introduced to provide correlation among
several infrastructure elements. These elements can begin to recognize
patterns, predict the optimal configuration and provide advice on what
course of action the administrator should take.
4. 4. Adaptive level
”Aas these technologies improve and as people become more
comfortable with the advice and predictive power of these systems, we
can progress to the adaptive level, where the systems themselves can
automatically take the right actions based on the information that is
available to them and the knowledge of what is happening in the system.
5. 5. Autonomic level
”Tthe IT infrastructure operation is governed by business
policies and objectives. Users interact with the autonomic technology
to monitor the business processes, alter the objectives, or both.
Grand Challenge
Research into creating autonomic systems won't be easy, but future
computer systems will have to incorporate increased levels of
automation if we expect them to manage the ballooning amount of data,
the ever-expanding network and the increasing might of processing
power. To create autonomic systems researchers must address key
challenges with varying levels of complexity. Here is a partial list of
the challenges we face.
¢ System identity: Before a system can transact with other
systems it must know the extent of its own boundaries. How will we
design our systems to define and redefine themselves in dynamic
environments? ¢ Interface design: With a multitude of platforms running, system
administrators face a briar patch of knobs. How will we build
consistent interfaces and points of control while allowing for a
heterogeneous environment? ¢ Translating business policy into I/T policy: The end result
needs to be transparent to the user. How will we create human
interfaces that remove complexity and allow users to interact naturally
with I/T systems? ¢ Systemic approach: Creating autonomic components is not enough.
How can we unite a constellation of autonomic components into a
ffederated system? ¢ Standards: The age of proprietary solutions is over. How can we
design and support open standards that will work?
¢ Adaptive algorithms: New methods will be needed to equip our
systems to deal with changing environments and transactions. How will
we create adaptive algorithms to take previous system experience and
use that information to improve the rules? ¢ Improving network-monitoring functions to protect security,
detect potential threats and achieve a level of decision-making that
allows for the redirection of key activities or data. ¢
¢ Smarter microprocessors that can detect errors and anticipate
failures.
Chapter 4
Autonomic ComputingSelf managing computing Architecture Concepts
A standard set of functions and interactions govern the management of
the IT system and its resources, including client, server, database
manager or Web application server. This is represented by a control
loop (shown in the diagram below) that acts as a manager of the
resource through monitoring, analysis and taking action based on a set
of policies. These control loops, or managers, can communicate with each other in a
peer-to-peer context and with higher-level managers. For example, a
database system needs to work with the server, storage subsystem,
storage management software, the Web server and other system elements
to achieve a self-managing IT environment. The pyramid shown below
represents the hierarchy in which autonomic computingself managing
computing technologies will operate. The bottom layer of the pyramid consists of the resource elements of an
enterprise networks, servers, storage devices, applications, middleware
and personal computers. Autonomic computingSelf managing computing
begins in the resource element layer, by enhancing individual
components to configure, optimize, heal and protect themselves. Moving up the pyramid, resource elements are grouped into composite
resources, which begin to communicate with each other to create self-
managing systems. This can be represented by a pool of servers that
work together to dynamically adjust workload and configuration to meet
certain performance and availability thresholds. It can also be
represented by a combination of heterogeneous devices (databases, Web
servers and storage subsystems) that work together to achieve
performance and availability targets.
At the highest layer of the pyramid composite resources are tied to
business solutions, such as a customer care system or an electronic
auction system. True autonomic activity occurs at this level. The solution layer requires autonomic solutions to comprehend the
optimal state of business processes based on policies, schedules, and
service levels and so on and drive the consequences of process
optimization back down to the composite resources and even to
individual elements.
In an autonomic environment, components work together, communicating
with each other and with high-level management tools. They regulate
themselves and, sometimes, each other. They can proactively manage the
system, while hiding the inherent complexity of these activities from
end users and IT professionals. Another aspect of the autonomic computingself managing computing
architecture is shown in the diagram below. This portion of the
architecture details the functions that can be provided for the control
loops. The architecture organizes the control loops into two major
elements”a managed element and an autonomic manager. A managed element
is what the autonomic manager is controlling. An autonomic manager is a
component that implements a particular control loop.
Managed elements
The managed element is a controlled system component. There can be a
single resource (a server, database server or router) or a collection
of resources (a pool of servers, cluster or business application). The
managed element is controlled through its sensors and effectors:
¢ The sensors provide mechanisms to collect information about the
state and state transition of an element. To implement the sensors, you
can either use a set of get operations to retrieve information about
the current state, or a set of management events (unsolicited,
asynchronous messages or notifications) that flow when the state of the
element changes in a significant way.
¢ The effectors are mechanisms that change the state
(configuration) of an element. In other words, the effectors are a
collection of set commands or application programming interfaces
(APIs) that change the configuration of the managed resource in some
important way.
The combination of sensors and effectors form the manageability
interface that is available to an autonomic manager. As shown in the
figure above, by the black lines connecting the elements on the sensors
and effectors sides of the diagram, the architecture encourages the
idea that sensors and effectors are linked together. For example, a
configuration change that occurs through effectors should be reflected
as a configuration change notification through the sensor interface.
Autonomic manager
The autonomic manager is a component that implements the control loop.
The architecture dissects the loop into four parts that share
knowledge: ¢ The monitor part provides the mechanisms that collect,
aggregate, filter, manage and report details (metrics and topologies)
collected from an element.
¢ The analyze part provides the mechanisms to correlate and model
complex situations . situations. These mechanisms allow the autonomic
manager to learn about the IT environment and help predict future
situations.
¢ The plan part provides the mechanisms to structure the action
needed to achieve goals and objectives. The planning mechanism uses
policy information to guide its work.
¢ The execute part provides the mechanisms that control the
execution of a plan with considerations for on-the-fly updates.
The four parts work together to provide the control loop functionality.
The diagram shows a structural arrangement of the parts”not a control
flow. The bold line that connects the four parts should be thought of
as a common messaging bus rather than a strict control flow. In other
words, there can be situations where the plan part may ask the monitor
part to collect more or less information. There could also be
situations where the monitor part may trigger the plan part to create a
new plan. The four parts collaborate using asynchronous communication
techniques, like a messaging bus.
The sensors and effectors provided by the autonomic manager facilitate
collaborative interaction with other autonomic managers. In addition,
autonomic managers can communicate with each other in both peer-to-peer
and hierarchical arrangements. The numerous autonomic managers in a
complex IT system must work together to deliver autonomic computingself
managing computing to achieve common goals.
Autonomic manager knowledge
Data used by the autonomic managerâ„¢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 element into a system log. An example of the latter is
policy. A policy consists of a set of behavioral constraints or
preferences that influence the decisions made by an autonomic manager.
Specifically, the plan part of an autonomic manager is responsible for
interpreting and translating policy details. The analysis part is
responsible for determining if the autonomic manager can abide by the
policy, now and in the future.
Chapter 5
Grand Challenge
Research into creating autonomic systems won't be easy, but future
computer systems will have to incorporate increased levels of
automation if we expect them to manage the ballooning amount of data,
the ever-expanding network and the increasing might of processing
power. To create autonomic systems researchers must address key challenges
with varying levels of complexity. Here is a partial list of the
challenges we face.
¢System identity: Before a system can transact with other systems it
must know the extent of its own boundaries. How will we design our
systems to define and redefine themselves in dynamic environments? ¢Interface design: With a multitude of platforms running, system
administrators face a briar patch of knobs. How will we build
consistent interfaces and points of control while allowing for a
heterogeneous environment? ¢Translating business policy into I/T policy: The end result needs to
be transparent to the user. How will we create human interfaces that
remove complexity and allow users to interact naturally with I/T
systems? ¢Systemic approach: Creating autonomic components is not enough. How
can we unite a constellation of autonomic components into a federated
system? ¢Standards: The age of proprietary solutions is over. How can we design
and support open standards that will work? Adaptive algorithms: New methods will be needed to equip our systems to
deal with changing environments and transactions. How will we create
adaptive algorithms to take previous system experience and use that
information to improve the rules? Chapter 6
Implementing autonomic computingself managing computing
Shifting the burden of managing systems to self-managing technologies
does not happen overnight and cannot be solely accomplished by
acquiring new products. Skills within the organization need to adapt,
and processes need to change to create new benchmarks of success. As companies progress through the five levels of autonomic
computingself managing computing, the processes, tools and benchmarks
become increasingly sophisticated, and the skills requirement becomes
more closely aligned with the business. The basic level represents the starting point for many IT
organizations. If IT organizations are formally measured, they are
typically evaluated on the time required to finish major tasks and fix
major problems. The IT organization is viewed as a cost center, with
variable labor costs preferred over an investment in centrally
coordinated systems management tools and processes.
In the managed level IT organizations are measured on the availability
of their managed resources, their time to close trouble tickets in
their problem management system and their time to complete formally
tracked work requests. To improve on these measurements, IT
organizations document their processes and continually improve them
through manual feedback loops and adoption of best practices. IT
organizations gain efficiency through consolidation of management tools
to a set of strategic platforms and through a hierarchical problem
management triage organization.
In the predictive level IT organizations are measured on the
availability and performance of their business systems and their return
on investment. To improve, IT organizations measure, manage and analyze
transaction performance. The critical nature of the IT organizationâ„¢s
role in business success is understood. Predictive tools are used to
project future IT performance, and many tools make recommendations to
improve future performance.
In the adaptive level IT resources are automatically provisioned and
tuned to optimize transaction performance. Business policies, business
priorities and service-level agreements guide the autonomic
infrastructure behavior. IT organizations are measured on comprehensive
business system response times (transaction performance), the degree of
efficiency of the IT infrastructure and their ability to adapt to
shifting workloads. In the autonomic level IT organizations are measured on their ability
to make the business successful. To improve business measurements they
understand the financial metrics associated with e-business activities
and supporting IT activities. Advanced modeling techniques are used to
optimize e-business performance and quickly deploy newly optimized e-
business solutions.

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1.INTRODUCTION
Self managing computing helps address the complexity issues by using technology to manage technology. The idea is not new many of the major players in the industry have developed and delivered products based on this concept. Self managing computing is also known as autonomic computing.
Autonomic Computing is an initiative started by IBM in 2001. Its ultimate aim is 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. In other words, autonomic computing refers to the self-managing characteristics of distributed computing resources, adapting to unpredictable changes while hiding intrinsic complexity to operators and users.
The term autonomic is derived from human biology. The autonomic nervous system monitors your heartbeat, checks your blood sugar level and keeps your body temperature close to 98.6°F, without any conscious effort on your part. In much the same way, self managing computing components anticipate computer system needs and resolve problems with minimal human intervention.
Self managing computing systems have the ability to manage themselves and dynamically adapt to change in accordance with business policies and objectives. Self-managing systems can perform management activities based on situations they observe or sense in the IT environment. Rather than IT professionals initiating management activities, the system observes something about itself and acts accordingly. This allows the IT professional to focus on high-value tasks while the technology manages the more mundane operations. Self managing computing can result in a significant improvement in system management efficiency, when the disparate technologies that manage the environment work together to deliver performance results system wide.
However, complete autonomic systems do not yet exist. This is not a proprietary solution. It's a radical change in the way businesses, academia, and even the government design, develop, manage and maintain computer systems. Self managing computing calls for a whole new area of study and a whole new way of conducting business.
Self managing computing is the self-management of e-business infrastructure, balancing what is managed by the IT professional and what is managed by the system. It is the evolution of e-business.
1.1 Objective
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.
Research into creating autonomic systems won't be easy, but future computer systems will have to incorporate increased levels of automation if we expect them to manage the ballooning amount of data, the ever-expanding network and the increasing might of processing power.
To create autonomic systems researchers must address key challenges with varying levels of complexity. Here is a partial list of the challenges we face.
Autonomic computing systems have the ability to manage themselves and dynamically adapt to change in accordance with business policies and objectives. Self-managing systems can perform management activities based on situations they observe or sense in the IT environment. Rather than IT professionals initiating management activities, the system observes something about itself and acts accordingly. Self-Management technologies are expected to pervade the next generation of network management systems.
The growing complexity of modern networked computer systems is currently the biggest limiting factor in their expansion. The increasing heterogeneity of big corporate computer systems, the inclusion of mobile computing devices,and the combination of different networking technologies like WLAN, cellular phone networks, and mobile ad hoc networks make the conventional, manual management very difficult, time-consuming, and error-prone.
of complexity to solve a wide variety 62258752
2.2SYSTEM OVERVIEW
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.
Self managing computing is about freeing IT professionals to focus on high-value tasks by making technology work smarter. This means letting computing systems and infrastructure take care of managing themselves. Ultimately, it is writing business policies and goals and letting the infrastructure configure, heal and optimize itself according to those policies while protecting itself from malicious activities. Self managing computing systems have the ability to manage themselves and dynamically adapt to change in accordance with business policies and objectives.
In an autonomic environment the IT infrastructure and its components are Self-managing. Systems with self-managing components reduce the cost of owning and operating computer systems. Self-managing systems can perform management activities based on situations they observe or sense in the IT environment. Rather than IT professionals initiating management activities, the system observes something about itself and acts accordingly. This allows the IT professional to focus on high-value tasks while the technology manages the more mundane operations.
IT infrastructure components take on the following characteristics:
• self-configuring
• self-healing
• self-optimizing and
self-protecting.
2.1 Self-configuring
Systems adapt automatically to dynamically changing environments. When hardware and software systems have the ability to define themselves “on-the fly,” they are self-configuring. This aspect of self-managing means that new features, software, and servers can be dynamically added to the enterprise infrastructure with no disruption Systems adapt automatically to dynamically changing environments. When hardware of services. Self-configuring not only includes the ability for each individual system to configure itself on the fly, but also for systems within the enterprise to configure themselves into the e-business infrastructure of the enterprise. The goal of self managing computing is to provide self-configuration capabilities for the entire IT infrastructure, not just individual servers, software, and storage devices.
2.2 Self- healing
Systems discover, diagnose, and react to disruptions. For a system to be self-healing, it must be able to recover from a failed component by first detecting and isolating the failed component, taking it off line, fixing or isolating the failed component, and reintroducing the fixed or replacement component into service without any apparent application disruption. Systems will need to predict problems and take actions to prevent the failure from having an impact on applications. The self-healing objective must be to minimize all outages in order to keep enterprise applications up and available at all times. Developers of system components need to focus on maximizing the reliability and availability design of each hardware and software product toward continuous availability.
2.3 Self-optimizing
Systems monitor and tune resources automatically. Self-optimization requires hardware and software systems to efficiently maximize resource utilization to meet end-user needs without human intervention. Features must be introduced to allow the enterprise to optimize resource usage across the collection of systems within their infrastructure, while also maintaining their flexibility to meet the ever-changing needs of the enterprise.
2.4 Self-protecting
Systems anticipate, detect, identify, and protect themselves from attacks from anywhere. Self-protecting systems must have the ability to define and manage user access to all computing resources within the enterprise, to protect against unauthorized resource access, to detect intrusions and report and prevent these activities as they occur, and to provide backup and recovery capabilities that are as secure as the original resource management systems. Systems will need to build on top of a number of core security technologies already available today. Capabilities must be provided to more easily understand and handle user identities in various contexts, removing the burden from administrators.

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