Autonomic computing seminar documentation

AUTONOMIC COMPUTING BACHELOR OF COMPUTER APPLLICATION

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INTRODUCTION


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1. INTRODUCTION

As enterprises strive to meet their current challenges, they

require an IT infrastructure that supports their business goals. An IT infrastructure that

enables business to be more responsive, variable, focused, and resilient. Autonomic systems

are such systems that are self-configuring, self-healing, self-protecting and self-optimizing.

They are Intelligent open systems that manage complexity, know themselves, continuously

tune themselves, adapt to unpredictable conditions, prevent and recover from failures and

provide a safe environment. They let enterprises focus on business, not on IT infrastructure.

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.

The 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.

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 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. Some autonomic systems involve mobile agents interacting via loosely

coupled communication mechanisms.

Autonomy-oriented computation is a paradigm proposed by

Jiming Liu in 2001 that uses artificial systems imitating social animals' collective behaviours

http://en.wikipedia.org/wiki/IBM

http://en.wikipedia.org/wiki/Self-management_(computer_science)

http://en.wikipedia.org/w/index.php?title=Computing_systems_management&action=edit&redlink=1

http://en.wikipedia.org/wiki/Multi-agent_system


http://en.wikipedia.org/wiki/Social_animals


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to solve difficult computational problems. For example, ant colony optimization could be

studied in this paradigm. The term autonomic computing is emblematic of a vast and

somewhat tangled hierarchy of natural self-governing systems, many of which consist

0of myriad interacting, self-governing components that in turn comprise large numbers

of interacting, autonomous, self-governing components at the next level down. The

enormous range in scale, starting with molecular machines within cells and extending to

human markets, societies, and the entire world socio economy, mirrors that of

computing systems, which run from individual devices to the entire Internet. Thus, we

believe it will be profitable to seek inspiration in the self-governance of social and

economic systems as well as purely biological ones.

http://en.wikipedia.org/wiki/Ant_colony_optimization


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EVOLUTION


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2. EVOLUTION

2.1AUTONOMIC SYSTEMS

Autonomic systems are such systems that are self-configuring, self-healing, self-

protecting and self-optimizing. They are ‘Intelligent’ open systems that manage complexity,

know themselves, continuously tune themselves, adapt to unpredictable conditions, prevent

and recover from failures and provide a safe environment.

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. A possible solution could be to enable modern, networked computing systems

to manage themselves without direct human intervention. The Autonomic Computing

Initiative (ACI) aims at providing the foundation for autonomic systems. It is inspired by

the autonomic nervous system of the human body. This nervous system controls important

bodily functions (e.g. respiration, heart rate, and blood pressure) without any conscious

intervention.

In a self-managing autonomic system, the human operator takes on a new role: instead of

controlling the system directly, he/she defines general policies and rules that guide the self-

management process. For this process, IBM defined the following four functional areas:

1. Self-configuration: Automatic configuration of components;

2. Self-healing: Automatic discovery, and correction of faults;

3. Self-optimization: Automatic monitoring and control of resources to ensure the

optimal functioning with respect to the defined requirements;

4. Self-protection: Proactive identification and protection from arbitrary attacks.

IBM defined five evolutionary levels, or the autonomic deployment model, for its

deployment: Level 1 is the basic level that presents the current situation where systems are

essentially managed manually. Levels 2 - 4 introduce increasingly automated management

functions, while level 5 represents the ultimate goal of autonomic, self-managing systems.

The design complexity of Autonomic Systems can be

simplified by utilizing design patterns such as the model-view-controller (MVC) pattern to

improve concern separation by encapsulating functional concerns. The system makes

decisions on its own, using high-level policies; it will constantly check and optimize its status

http://en.wikipedia.org/wiki/Autonomic_nervous_system

http://en.wikipedia.org/wiki/Heart_rate

http://en.wikipedia.org/wiki/Blood_pressure

http://en.wikipedia.org/wiki/Self-management_(computer_science)

http://www.ibm.com/press/us/en/pressrelease/464.wss

http://en.wikipedia.org/wiki/Design_patterns

http://en.wikipedia.org/wiki/Model-view-controller


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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.

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 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. Some autonomic systems involve mobile agents interacting via loosely

coupled communication mechanisms.




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2.2The Complexity Problem

The increasing complexity of computing systems is overwhelming the capabilities of

software developers and system administrators who design, evaluate, integrate, and

manage these systems. Today, computing systems include very complex infrastructures and

operate in complex heterogeneous environments. With the proliferation of handheld devices,

the ever-expanding spectrum of users, and the emergence of the information economy with

the advent of the Web, computing vendors have difficulty providing an infrastructure to

address all the needs of users, devices, and applications. SOAs with Web services as their

core technology have solved many problems, but they have also raised numerous complexity

issues. One approach to deal with the business challenges arising from these complexity

problems is to make the systems more self-managed or autonomic. For a typical information

system consisting of an application server, a Web server, messaging facilities, and layers of

middleware and operating systems, the number of tuning parameters exceeds human

comprehension and analytical capabilities. Thus, major software and system vendors

endeavor to create autonomic, dynamic, or self-managing systems by developing methods,

architecture models, middleware, algorithms, and policies to mitigate the complexity

problem. In a 2004 Economist article, Kluth investigates how other industrial sectors

successfully dealt with complexity [Kluth 04]. He and others have argued that for a

technology to be truly successful, its complexity has to disappear. He illustrates his

arguments with many examples including the automobile and electricity markets. Only

mechanics were able to operate early automobiles successfully. In the early 20th century,

companies needed a position of vice president of electricity to deal with power generation

and consumption issues. In both cases, the respective industries managed to reduce the need

for human expertise and simplify the usage of the underlying technology. However, usage

simplicity comes with an increased complexity of the overall system (e.g., what is "under the

hood"). Basically for every mouse click or return we take out of the user experience, 20

things have to happen in the software behind the scenes. Given this historical perspective

with this predictable path of technology evolution, maybe there is hope for the information

technology sector.

Not only the number of computer systems but also the complexity of those systems are also

increasing. We can clearly identify the drastic increase in the number of users in internet

from the following graph. And we are very much familiar with the advanced features of these


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systems , so that we can easily arrive at a conclusion that the internal architecture is so

complexed to make the system user friendly.

Fig1. Global population v/s Inetrnet Users


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2.3 The Evolution Problem

By attacking the software complexity problem through technology simplification and automation,

autonomic computing also promises to solve selected software evolution problems. Instrumenting

software systems with autonomic technology will allow us to monitor or verify requirements

(functional or nonfunctional) over long periods of time. For example, self-managing systems will be

able to monitor and control the brittleness of legacy systems, provide automatic updates to evolve

installed software, adapt safety critical systems without halting them, immunize computers

against malware automatically, facilitate enterprise integration with self-managing integration

mechanisms, document architectural drift by equipping systems with architecture analysis

frameworks, and keep the values of quality attributes within desired ranges.


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WORKING


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3.WORKING

In order for the autonomic managers and the managed resources in an autonomic system to

work together, the developers of these components need a common set of capabilities. This

section conceptually describes an initial set of core capabilities that are needed to build

autonomic systems. These core capabilities include:

Solution Installation : A common solution knowledge capability eliminates the

complexity introduced by many formats and many installation tools. By capturing

install and configuration information in a consistent manner, autonomic managers can

share the facilities as well as information regarding the installed environment.

Common Systems Administration : Autonomic systems require common console

technology to create a consistent human interface for the autonomic managers of the

IT infrastructure. The common console capability provides a framework for reuse and

consistent presentation for other autonomic core technologies. The primary goal of a

common console is to provide a single platform that can host all of the administrative

console functions in server, software, and storage products in a manner that allows

users to manage solutions rather than managing individual systems or products.

Problem Determination : Autonomic managers take actions based on problems or

situations they observe in the managed resource. Therefore, one of the most basic

capabilities is being able to extract high quality data to determine whether or not a

problem exists in managed resources. In this context, a problem is a situation in which

an autonomic manager needs to take action. A major cause of poor quality

information is the diversity in the format and content of the information provided by

. the managed resource. To address the diversity of the data collected, a common

problem determination architecture normalizes the data collected, in terms of

format, content, organization, and sufficiency

Autonomic Monitoring : Autonomic monitoring is a capability that provides an

extensible run-time environment for an autonomic manager to gather and filter data

obtained through sensors. Autonomic managers can utilize this capability as

amechanism for representing, filtering, aggregating, and performing a range of

analysis of sensor data.


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Complex Analysis : Autonomic managers need to have the capability to perform

complex data analysis and reasoning on the information provided through sensors.

The analysis will be influenced by stored knowledge data. An autonomic manager's

ability to quickly analyze and make sense of this data is crucial to its successful

operation.

Policy-based Management : 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.

Heterogeneous workload management : Heterogeneous workload management

includes the capability to instrument system components uniformly to manage

workflow through the system. Business workload management is a core technology

that monitors end-to-end response times for transactions or segments of transactions,

rather than at the component level, across the heterogeneous infrastructure.

These capabilities are enabled through a series of tools and facilities that are collected in

IBM Autonomic Computing Toolkit.


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3.2 ELEMENTS OF AUTONOMIC COMPUTING

Fig3. Elements of Autonomic Computing

IBM researchers have established an architectural framework for autonomic

systems. An autonomic system consists of a set of autonomic elements that contain and

manage resources and deliver services to humans or other autonomic elements. An

autonomic element consists of one autonomic manager and one or more managed

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

manager with the managed sensors, filters the data collected from them, and then stores

the distilled data in the knowledge base. The analysis engine compares the collected data

against the desired sensor values also stored in the knowledge base. The planning engine

devises strategies to correct the trends identified by the planning engine. The execution

engine finally adjusts parameters of the managed and element. The autonomic

manager consists of sensors, effectors, and a five-component analysis planning engine as

depicted in Figure 2. The mo

nitor observes the element by means of effectors and stores the affected values in the

knowledge base.


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Now we can discuss the elements in detail. The autonomic manager implements autonomic

control loops by dividing them into four parts: monitor, analyze, plan, and execute. The

control loop carries out tasks as efficiently as possible based on high- level policies

Monitor: Through information received from sensors, the resource monitors the

environm-ent for specific, predefined conditions. These conditions don't have to be

errors; they can be a certain load level or type of request.

Analyze: Once the condition is detected, what does it mean? The resource must

analyze the information to determine whether action should be taken.

Plan: If action must be taken, what action? The resource might simply notify the

administrator, or it might take more extensive action, such as provisioning another

hard drive.

Execute: It is this part of the control loop that sends the instruction to the effector,

which actually affects or carries out the planned actions.

The managed resources are controlled system components that can range from single

resources such as a server, database server, or router to collections of resources like server

pools, clusters, or business applications.

All of the actions in the autonomic control loop either make use of or supplement the

knowledge base for the resource. For example, the knowledge base helps the analysis phase

of the control loop to understand the information it's getting from the monitor phase. It also

provides the plan phase with information that helps it select the action to be performed.


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APPLICATIONS


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4. APPLICATIONS

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 (intention) and the Know-how to operate

itself (e.g., bootstrapping, 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 making the right decisions to serve its Purpose, and influence

by the observation of the operational context (based on the sensor input).

This 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.

Control loops

A basic concept that will 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.


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ADVANTAGES


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5ADVANTAGES

Fig2.Diagram of characteristics of autonomic computing


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An autonomic system can self-configure at runtime to meet changing operating

environments, self-tune to optimize its performance, self-heal when it encounters

unexpected obstacles during its operation, and-of particular current interest-protect

itself from malicious attacks. Research and development teams concentrate on

developing theories, methods, tools, and technology for building self-healing, self

configuring, self-optimizing, and self-protecting systems, as depicted in Figure 3. An

autonomic system can self-manage anything including a single property or multiple

properties.

An autonomic system has the following characteristics :

Self-Configuring: Self-configuring systems provide increased responsiveness

by adapting to a dynamically changing environment. A self-configuring system

must be able to configure and reconfigure itself under varying and unpredictable

conditions. Varying degrees of end-user involvement should be allowed, from

user-based reconfiguration to automatic reconfiguration based on monitoring and

feedback loops. For example, the user may be given the option of reconfiguring

the system at runtime; alternatively, adaptive algorithms could learn the best

configurations to achieve mandated performance or to service any other desired

functional or nonfunctional requirement. Variability can be accommodated at

design time (e.g., by implementing goal graphs) or at runtime (e.g., by adjusting

parameters). Systems should be designed to provide configurability at a feature

level with capabilities such as separation of concerns, levels of indirection,

integration mechanisms (data and control), scripting layers, plug and play, and

set-up wizards. Adaptive algorithms have to detect and respond to short-term and

long-term trends.

Self-Optimizing: Self-optimizing systems provide operational efficiency by

tuning resources and balancing workloads. Such a system will continually monitor

and tune its resources and operations. In general, the system will continually seek

to optimize its operation with respect to a set of prioritized nonfunctional

requirements to meet the ever changing needs of the application environment.

Capabilities such as repartitioning, reclustering, load balancing, and rerouting


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must be designed into the system to provide self-optimization. Again, adaptive

algorithms, along with other systems, are needed for monitoring and response.

Self-Healing: Self-healing systems provide resiliency by discovering

and preventing disruptions as well as recovering from malfunctions. Such a

system will be able to recover-without loss of data or noticeable delays in

processing from routine and extraordinary events that might cause some of its

parts to malfunction. Self-recovery means that the system will select, possibly

with user input, an alternative configuration to the one it is currently using and

will switch to that configuration with minimal loss of information or delay.

Self-Protecting: Self-protecting systems secure information and resources

by anticipating, detecting, and protecting against attacks. Such a system will

be capable of protecting itself by detecting and counteracting threats through the

use of pattern recognition and other techniques. This capability means that the

design of the system will include an analysis of the vulnerabilities and the

inclusion of protective mechanisms that might be employed when a threat is

detected. The design must provide for capabilities to recognize and handle

different kinds of threats in various contexts more easily, thereby reducing the

burden on administrators.

Some More Characteristics…

Reflexivity: An autonomic system must have detailed knowledge of its

components, current status, capabilities, limits, boundaries, interdependencies

with other systems, and available resources. Moreover, the system must be aware of

its possible configurations and how they affect particular nonfunctional

requirements.

Adapting: At the core of the complexity problem addressed by the AC initiative

is the problem of evaluating complex tradeoffs to make informed decisions. Most of

the characteristics listed above are founded on the ability of an autonomic system to

monitor its performance and its environment and respond to changes by

switching to a different behavior. At the core of this ability is a control loop.


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Sensors observe an activity of a controlled process, a controller component

decides what has to be done, and then the controller component executes the

required operations through a set of actuators. The adaptive mechanisms to be

explored will be inspired by work on machine learning, multi-agent systems, and

control theory.

Automatic: This essentially means being able to self-control its internal functions

and operations. As such, an autonomic system must be self-contained 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.

Aware: 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.


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5.2 NEED OF AUTONOMIC COMPUTING

In the evolution of humans and human society automation has always been the

foundation for progress. If human can handle one of his needs automatically, then he has free

mind and resources to concentrate on another task. So step by step he can get ability to

concentrate on more complex problems. For example few of people worry about harvesting

the grain to grind the flour to bake bread; other people even eat bread but do not worry about

producing they can simply buy it at a nearby store. Therefore they have relaxed mind for do

another job. Farmer’s also having the benefit of autonomic by using machines for their work.

It causes to save their time and cost and also increase the amount of harvest get by unit of

land.. But computing systems have proved that evolution via automation also produces

complexity as an unavoidable byproduct. Follow the evolution of computers from single

machines to modular systems to personal computers networked with larger machines and an

unmistakable pattern emerges. Compare with pervious machines incredible progress in

almost every aspect of computing, for example microprocessor power up by a factor of

10,000, storage capacity by a factor of 45,000, communication speeds by a factor of

1,000,000 . Along with that growth has become increasingly sophisticated architectures

governed by software whose complexity now demands tens of millions of lines of code.

Some operating environments weigh in at over 30 million lines of code created by over 4,000

programmers. In fact, the growing complexity of the IT infrastructure threatens to undermine

the very benefits information technology aims to provide. Until now the computer systems

relied mainly on human intervention and administration to manage this complexity. When

considering about current rates of expansion, there will not be enough skilled IT people to

keep the world’s computing systems running. Even in uncertain economic times, still have

high demand for skilled IT workers. Even if people could somehow come up with enough

skilled people, the complexity is growing beyond human ability to manage it. As computing

evolves, the overlapping connections, dependencies, and interacting applications call for

administrative decision-making and responses faster than any human can deliver. Identifying

root causes of failures becomes more difficult, while finding ways of increasing system

efficiency generates problems with more variables than any human can hope to solve.

Without new approaches, things will only get worse. To solve the problem people need

computer systems with autonomic behavior.


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Value …

By enabling computer systems to have self-configuring, self-healing, self-optimizing and

self-protecting features, autonomic computing is expected to have many benefits for business

systems, such as reduced operating costs, lower failure rates, more security, and the ability to

have systems that can respond more quickly to the needs of the business within the market in

which they operate.

The implications for an autonomic, on demand business approach are

immediately evident: A network of organized, smart computing components can give clients

what they need, when they need it, without a conscious mental or even physical effort.

Few examples of the results delivered by implementing autonomic computing solutions with

self-management characteristics are : Operational efficiency, Supporting business needs with

IT, Workforce productivity. Systems that are self- managing free up IT resources which then

can move from mundane system management tasks to focusing on working with users to

solve business problems.

Examples

A variety of autonomic computing capabilities are already in use throughout IBM

products, and these products are already helping companies succeed. IBM Self-

Managing Autonomic Computing capabilities are present in all IBM software product

families; Information Management, Lotus, Tivoli, Rational, Websphere, Ö .


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DISADVANTAGES


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6. DISADVANTAGES

Dealing with issues of trust is critical for the successful design,

implementation, and operation of AC systems. Since an autonomic system is

supposed to reduce human interference or even take over certain heretofore human

duties, it is imperative to make trust development a core component of its design.

Even when users begin to trust the policies hard-wired into low-level autonomic

elements, it is a big step to gain their trust in higher level autonomic elements that

use these low-level elements as part of their policies. Autonomic elements are

instrumented to provide feedback to users beyond what they provide as their service.

Deciding what kind of feedback to provide and how to instrument the autonomic

elements is a difficult problem. The trust feedback required by users will evolve with

the evolution of the autonomic system. However, the AC field can draw experience from

the automation and HCI communities to tackle these problems.

Autonomic systems can become more trustable by actively

communicating with their users. Improved interaction will also allow these systems

to be more autonomous over time, exhibiting increased initiative without losing the

users' trust. Higher trustability and usability should, in turn, lead to improved

adoptability.


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FUTURESCOPE


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7.FUTURE OF AUTONOMIC COMPUTING

The computer industry is filled with pundits, speculators, visionaries, salesman, brilliant

architects and professors. Each provides invaluable insight into their experience, their

intelligence, their alma mater, their ticker symbol, their ego and what’s next. Some win the

“what’s next lottery”, others work for years of brilliance in relative obscurity.

Seemingly, a world that has deployed over 1 Billion devices a year for the last 3 years , is

incapable of understanding the gravity of a new programming models, a new hardware

architecture, a sleek new design that delivers on a vision that Gene Rodenberry thought of in

the 1960’s or Da Vinci in the 15th Century. What is old is new…..and let me tell you why? It

will revolutionize the industry (not evolutionize…a term reserved for slower growing

industry’s that require government assistant every decade or so…), transform your

environment and provide freedoms you had only hoped to enjoy….and we invented it 40

years ago. Does any of this sound familiar?

It should. These are the paraphrased slogans of an industry in transition. Real products

matters, product differentiation matters, standards matter, interoperability matters….and

shareholders pay for future expectations.

The future of computing…is NOW. The future of the computer industry is NOW. The

next generation of computer programming, software architectures and transformational

technologies is NOW. As an industry we have finally begun to embrace interface,

architectural and software programming standards to usher in a new era of interoperability

and scalability. Behind us are the days of “proprietary interfaces” (What does that actually

mean other than I am going to sell you some extra accessories that will be worthless in 2

years?), which do not provide a differentiated performance/cost advantage. Gone are the days

of developing programming languages that lock-in customers to individual companies,

whether vendors innovate or not. These rules of the past are slowly melting away, allowing

the entire industry to embrace interoperability and standards at the highest level in history.

Industry diversity is healthy and insures that the most innovative and technologically relevant

companies will “win” most of the time. Allowing the 1 Billion and the Next Billion

customers of the world to enjoy the best interface technology yet developed….each other. It

also provides us with a unique ability to move to the next phase in our dynamic industry’s

growth, autonomic instrumentation.

Why is this important? Instrumentation matters. As we apply business and personal rules to


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our growing compute environments it has become increasingly clear that the more tools we

make available to users the better informed we are in making decisions. The more disclosure

we provide to investors through the use of autonomic programming architectures the more

informed they will be of their investing decisions.

How can you day trade $1B in 35 different stocks without clear autonomic controls in your

data center, your database, your application and your client devices?

How can you move 450 Million people efficiecntly throughout a country for 2 weeks

without autonomic controls on transportation: plains, trains, boats and automobiles, as they

do during the Spring Festival in China?

How can you process 1 Billion text messages a day without clear business rules? What

happens when these messages are also coming from machines to other machines, modifying

databases, applications and clients?

As humans, we must apply guidelines, much like laws, for our machines to take action when

we are asleep, when we are tired, when we are not present, when we are just simply being

human….to slow to react to a rapidly changing environment.

The innovators of the computer industry today understand this NOW. We do not need to

discuss a vision of 40 years ago without a plan to act NOW. Claiming ideas without action is

dishonorable at best, criminal at worst. The innovators of today must build products and

services that help solve the problems of today. We do not need to look to 2050 without a plan

to act NOW. The visionaries of tomorrow are…..not born. The visionaries of today…can call

me in 10 years.

Autonomic controls are in place today, machine to machine computer architectures are

here today, scalable compute engines are here today. Are they perfect, no. Are they effective,

yes. The design architects, product engineers and systems designers of today need to address

these concerns. Autonomic Instrumentation delivers control to the administrator, the user and

the developer. Rules engines can be modified to maximize efficiency, minimize consumption

and increase productivity. All of these will lead to increase shareholder (read: No just people

who buy shares of stock) value across your enterprise, your school, our hospitals, our

governments, and your home.

When executed properly, Autonomic controls should be able to deliver 20-25%

performance and efficiency increases with each new generation of Moore’s law. In some

cases, as in the Intel Xeon® 5500 Series these increases have been over 150% in

virtualization performance, these increases will be a combination of software architecture


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enhancement and silicon optimization. In other cases, it will be through the dedicated hard

work of increase instrumentation capability of a processor platform at the same price of the

previous generation through energy efficiency and memory controls.

Autonomic controls will also allow end users to avert disasters in our data centers, our homes

and in our hands. Autonomic instrumentation design frameworks, allow users to set

parameters on data migrations, data backup, security, memory access, power consumption

and virtual machine architectures.


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CONCLUSION


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8 .CONCLUSIONS

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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APPENDIXES


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9.APPENDIXES

FIGURE NAME

FIGURE NUMBER

Global population v/s Internet Users

Fig1

Diagram of characteristics of

autonomic computing

Fig2

Elements of Autonomic Computing

Fig3


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BIBLIOGRAPHY


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10.BIBLIOGRAPHY

1. Autonomic Computing: IBM's perspective on the state of information technology.

2. Jeffery 0. Kelhart and David M Chess (2003) "The vision of autonomic

computing".

3. http:1/www. research. ibm. com/autonomic/research/index. html

4. http:/Iautonomiccomputing.org/.

5. http:1/www. ibm.com/developerworks/autonomic.

6. https://communities.intel.com/community/datastack/blog/2009/06/26/the-future-of-

autonomic-computing- innovation.

7. http://en.wikipedia.org/wiki/Autonomic_computing

https://communities.intel.com/community/datastack/blog/2009/06/26/the-future-of-autonomic-computing-innovation

https://communities.intel.com/community/datastack/blog/2009/06/26/the-future-of-autonomic-computing-innovation

http://en.wikipedia.org/wiki/Autonomic_computing

Technology

Autonomic computing seminar documentation