CONTENTS

ABSTRACT

INTRODUCTION

WHAT IS AUTONOMIC COMPUTING?

KEY ELEMENTS OF AUTONOMIC COMPUTING

FUNDAMENTALS OF AUTONOMIC COMPUTING

AUTONOMIC COMPUTING AND CURRENT COMPUTING SYSTEM

AUTONOMIC COMPUTING ARCHITECTURE

NEED FOR AUTONOMIC COMPUTING

BENEFITS

CHAIIENGES

CONCLUSIONS

Autonomic Computing

ABSTRACT

Imagine a world where computers fix their own problems before you even

know something is wrong. IBM is building that world with a range of autonomic

computing capabilities across all of our product lines, helping you control an

increasingly complex and expensive IT business. .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.

As computing systems evolve, they are subject to the effect of continuous

growth in the number of degrees of freedom that must be well managed in order to

maintain their efficiency. 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.

Autonomic Computing 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 whilst hiding intrinsic

complexity to operators and users. 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

In this White paper, I present an overview of Autonomic Computing. Also, the

basic fundamental features of Autonomic Computing. This seminar report aims to

explain basic concept of Autonomic Computing.

Submitted By

Saurabh S. Gilalkar

IIIrd year, Computer Engineering.

Government Polytechnic,Amravati.

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Autonomic Computing

INTRODUCTION

"Civilization advances by extending the number of important operations which we

can perform without thinking about them." - Alfred North Whitehead

This quote made by the preeminent mathematician Alfred Whitehead holds

both the lock and the key to the next era of computing. It implies a threshold moment

surpassed only after humans have been able to automate increasingly complex tasks in

order to achieve forward momentum.

There is every reason to believe that we are at just such a threshold right now

in computing. The millions of businesses, billions of humans that compose them, and

trillions of devices that they will depend upon all require the services of the I/T industry

to keep them running. And it's not just a matter of numbers. It's the complexity of

these systems and the way they work together that is creating shortage of skilled I/T

workers to manage all of the systems. 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. It’s a

problem that's not going away, but will grow exponentially, just as our dependence on

technology has.

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.

The solution may lie in automation, or creating a new capacity where important

computing operations can run without the need for human intervention. On October

15th, 2001 Paul Horn, senior vice president of IBM Research addressed the Agenda

conference, an annual meeting of the preeminent technological minds, held in Arizona.

In his speech, and in a document he distributed there, he suggested a solution: build

computer systems that regulate themselves much in the same way our nervous systems

regulates and protects our bodies.

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Autonomic Computing

This new model of computing is called autonomic computing. The good news

is that some components of this technology are already up and running. 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 computing calls for a

whole new area of study and a whole new way of conducting business.

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Autonomic Computing

WHAT IS AUTONOMIC COMPUTING?

“Autonomic Computing” is a new vision of computing initiated by IBM. It is

the ability of systems to be more self-managing. Autonomic computing is the next

generation of integrated computer technology that will allow networks to manage

themselves with little or no human intervention. By choosing the word “autonomic,”

We are making an analogy with the autonomic nervous system, which controls many

organs and muscles in the human body, which sends impulses that control heart rate,

breathing and other functions without conscious thought or effort. The autonomic

nervous system frees our conscious brain from the burden of having to deal with vital

but lower level functions. Autonomic computing will free system administrators from

many of today’s routine management and operational tasks

Autonomic computing is the result of the realization that unless we begin to

build computing systems that reduce the complexity for those who use and manage

them, we will not have the time or the expertise to unravel problems arising in newer

systems.

Autonomic 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.

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Autonomic Computing

KEY ELEMENTS OF AUTONOMIC COMPUTING

The elements of autonomic computing can be summarized in to 8 key points.

In short, they are as follows:

1. Knows Itself

An autonomic computing system must be capable of taking continual stock of

itself, its connections, devices and resources, and know which are to be shared or

protected.

2. Configure Itself

An autonomic computing system must be able to configure and reconfigure

itself dynamically as needs dictate.

3. Optimizes Itself

An autonomic computing system must constantly search for ways to optimize

performance.

4. Heal Itself

An autonomic computing system must perform self-healing by redistributing

resources and reconfiguring itself to work around any dysfunctional elements.

5. Protect Itself

An autonomic computing system must be able to monitor security and protect

itself from attack.

6. Adapt Itself

An autonomic computing system must be able to recognize and adapt to the

needs of coexisting systems within its environment

7. Open Itself

It must work with shared technologies. Proprietary solutions are not compatible

with autonomic computing ideology.

8. Hide Itself

An autonomic computing system will anticipate the optimized resources

needed while keeping its complexity hidden.

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Autonomic Computing

FUNDAMENTALS OF AUTONOMIC COMPUTING

In order to incorporate these characteristics in “self-managing” systems, future

autonomic computing systems will have four fundamental features.

Fig.1: Autonomic Computing Attributes

Self-Configuring

Systems adapt automatically to dynamically changing environments. Then

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

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systems within the enterprise to configure them into the e-business infrastructure of the

enterprise.

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.

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. Some of the systems already include industry

leading technologies such as logical partitioning, dynamic workload management, and

dynamic server clustering. These kinds of capabilities should be ex-tended 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.

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 re-sources 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.

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Autonomic Computing

AUTONOMIC COMPUTING AND CURRENT COMPUTING SYSTEM

IBM frequently cites four aspects of self-management, which following Table

summarizes. Early autonomic systems may treat these aspects as distinct, with different

product teams creating solutions that address each one separately. Ultimately, these

aspects will be emergent properties of a general architecture, and distinctions will blur

into a more general notion of self-maintenance. The four aspects of self management

such as self-configuring, self-healing, self-optimizing and self-protecting are compared

here.

Concept Current Computing Autonomic Computing

Self configuration Corporate centers have

multiple vendors and

platforms. Installing,

configuring, and integrating

systems are time consuming

and error prone.

Automated configuration of

components and systems

follows high-level policies.

Rest of the system adjusts

automatically and seamlessly

Self-optimization Systems have hundreds of

manually set, nonlinear tuning

parameters and their number

increases with each release.

Components and systems continually seek opportunities to improve their own performance and efficiency.

Self-healing Problem determination in large, complex systems can take a team of programmer’s weeks.

System automatically detects diagnoses and repairs localized software and hardware problems.

Self-protection Detection of and recovery from attacks and cascading failures is manual.

System automatically defends against malicious attacks or cascading failures. It uses early warning to anticipate and prevent system wide failures.

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Autonomic Computing

AUTONOMIC COMPUTING ARCHITECTURE

The autonomic computing architecture concepts provide a mechanism

discussing, comparing and contrasting the approaches different vendors use to deliver

self-managing attributes in an autonomic computing system. The autonomic computing

architecture starts from the premise that implementing self-managing attributes involves

an intelligent control loop. This loop collects information from the system. Makes

decisions and then adjusts the system as necessary. An intelligent control loop can

enable the system to do such things as:

Self-configure, by installing software when it detects that

software is missing

Self-heal, by restarting a failed element

Self-optimize, by adjusting the current workload when it

observes an increase in capacity

Self-protect, by taking resources offline if it detects an

intrusion attempt.

A control loop can be provided by a resource provider, which embeds a loop in

the runtime environment for a particular resource. In this case, the control loop is

configured through the manageability interface provided for that resource (for example,

a hard drive).In some cases, the control loop may be hard-wired or hard coded so it is

not visible through the manageability interface.

Autonomic systems will be interactive collections of autonomic elements—

individual system constituents that contain resources and deliver services to humans and

other autonomic elements. , An autonomic element will typically consist of one or more

managed elements coupled with a single autonomic manager that controls and

represents them. At the core of an autonomic element is a control loop that integrates

the manager with the managed element.

In an autonomic environment, autonomic elements 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 computing architecture is shown in the diagram below.

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Autonomic Computing

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 control loop.

Fig.2: Autonomic Computing Architecture

Managed Element:

The managed element is a controlled system component. The managed element

will essentially be equivalent to what is found in ordinary non-autonomic systems,

although it can be adapted to enable the autonomic manager to monitor and control it.

The managed element could be a hardware resource, such as storage, CPU, or a printer,

or a software resource, such as a database, a directory service, or a large legacy system.

At the highest level, the managed element could be an e utility, an application service,

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Autonomic Computing

or even an individual business .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.

The effectors are mechanisms that change the state (configuration) of an element.

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

autonomic manager distinguishes the autonomic element from its non-autonomic

counterpart. By monitoring the managed element and its external environment, and

constructing and executing plans based on an analysis of this information, the

autonomic manager will relieve humans of the responsibility of directly managing the

managed element.

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 (time-series forecasting and queuing models, for

example). 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.

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Autonomic Computing

NEED FOR AUTONOMIC COMPUTING

It is observed that, “Complexity is the business we are in, and complexity is

what limits us.” The computer industry has spent decades creating systems of

marvelous and ever increasing complexity. But today, complexity itself is the problem.

The spiraling cost of managing the increasing complexity of computing systems is

becoming a significant inhibitor that threatens to undermine the future growth and

societal benefits of information technology. Managing complex system has grown too

costly and prone to error. Administrating such a complex system is too labor

Intensive and people under such conditions are prone to make mistakes “It is now

estimated that one third or one half of the company’s Total IT budget is spent in

preventing or recovering from crashes “Well-engineered autonomic functions

targeted at improving and automating systems operations, installation, dependency

management, and performance management can address many causes of these “most

frequent” outages and reduce outages and downtime. Confluences of marketplace

forces are driving the industry toward autonomic computing.Itself under varying and

unpredictable conditions. .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 computing system must be an expert in self-protection. .An

autonomic computing system knows its environment and the context surrounding its

activity, and acts accordingly. Perhaps most critical for the user, an autonomic

computing system will anticipate the optimized resources needed to meet a user’s

information needs while keeping its complexity hidden.

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Autonomic Computing

BENEFITS

Autonomic computing was conceived to lessen the spiraling demands for

skilled IT resources, reduce complexity and to drive computing into a new era that may

better exploit its potential to support higher order thinking and decision making.

Immediate benefits will include reduced dependence on human

intervention to maintain complex systems accompanied by a substantial decrease

in costs. Long-term benefits will allow individuals, organizations and businesses

to collaborate on complex problem solving.

.Short-term IT related benefits

• Simplified user experience through a more responsive, real-time system.

• Cost-savings - scale to use.

• Scaled power, storage and costs that optimize usage across both hardware and

software.

• Full use of idle processing power, including home PC's, through networked

system.

• Natural language queries allow deeper and more accurate returns.

• Seamless access to multiple file types. Open standards will allow users to pull

data from all potential sources by re-formatting on the fly.

• Stability. High availability. High security system. Fewer system or network

errors due to self-healing

• Improved computational capacity

Long-term/ Higher Order Benefits

• Realize the vision of enablement by shifting available resources to higher-order

business.

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Autonomic Computing

• Embedding autonomic capabilities in client or access devices, servers, storage

systems, middleware, and the network itself. Constructing autonomic federated

systems.

• Achieving end-to-end service level management.

• Accelerated implementation of new capabilities

• Collaboration and global problem-solving. Distributed computing allows for

more immediate sharing of information and processing power to use complex

mathematics to solve problems.

• Massive simulation - weather, medical - complex calculations like protein

folding, which require processors to run 24/7 for as long as a year at a time.

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Autonomic Computing

CHAIIENGES

To create autonomic systems researchers must address key challenges with

varying levels of complexity. They are:

• 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, How will we build consistent interfaces and points of

control while allowing for a heterogeneous environment?

• Translating business policy into IT 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 IT 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?

• 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.

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Autonomic Computing

CONCLUSIONS

The simplest, most predictable of tasks the system tasks that operate under

well understood principles (memory allocation, buffer pool management, load

balancing, etc.), with accurate sensing and well-understood actions, are well suited for

autonomic computing. Tasks that rely intrinsically on user interaction, or critically

depend on external world-state are often not well suited for autonomic computing.

The time is right for the emergence of self-managed or autonomic systems.

Over the past decade, we have come to expect that "plug-and-play" for Universal Serial

Bus (USB) devices, such as memory sticks and cameras, simply works—even for

technophobic users. Today, users demand and crave simplicity in computing solutions.

With the advent of Web and grid service architectures, we begin to expect that an

average user can provide Web services with high resiliency and high availability. The

goal of building a system that is used by millions of people each day and administered

by a half- time person, as articulated by Jim Gray of Microsoft Research, seems

attainable with the notion of automatic updates. Thus, autonomic computing seems to

be more than just a new middleware technology; in fact, it may be a solid solution for

reining in the complexity problem. Historically, most software systems were

not designed as self-managing systems. Retrofitting existing systems with self-

management capabilities is a difficult problem. Even if autonomic computing

technology is readily available and taught in computer science and engineering

curricula, it will take another decade for the proliferation of autonomicity in existing

systems.

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REFERENCES

Web Reference:-

1. http://www.ibm.com/research/autonomic

2. http://en.wikipedia.org/wiki/Autonomic_Computing

3. http://autonomiccomputing.org

Book Reference:-

P. Horn’s “Autonomic Computing: IBM’s Perspective on the State of Information Technology”.

***

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