KAIST

The Vision of Autonomic The Vision of Autonomic ComputingComputing

Jeffrey O. Kephart, David M Chess

IBM Watson research Center

IEEE Computer, Jan. 2003

발표자 : 이승학

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ContentsContents

Introduction

Autonomic option

Self-management

Architectural considerations

Engineering challenges

Scientific challenges

Conclusion

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IntroductionIntroduction

In 2001, IBM released a manifestoSoftware complexity crisis

Beyond the administration of individual softwareIntegrate heterogeneous environment

Extend company boundaries into the Internet

Trillions of computing devices connected in pervasive computing

Programming language innovationsExtend the size and complexity of systems

Architects can design the system

Architects cannot anticipate interactions among components

Install, configure, optimize, maintain and merge

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Autonomic optionAutonomic option

Autonomic computingNamed after autonomic nervous systemSystems can manage themselves according to an administrator’s goalsSelf-governing operation of the entire system, not just parts of itNew components integrate as effortlessly as a new cell establishes itself in the body

First stepExamine the vision of autonomic computing

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Self-management (1/2)Self-management (1/2)

Self-managementChanging components

External conditions

Hardware/software failures

Ex. Component upgradeContinually check for component upgrades

Download and install

Reconfigure itself

Run a regression test

When it detects errors, revert to the older version

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Self-management (2/2)Self-management (2/2)

Four aspects of self-managementSelf-configuration

Configure themselves automatically

High-level policies (what is desired, not how)

Self-optimizationHundreds of tunable parameters

Continually seek ways to improve their operation

Self-healingAnalyze information from log files and monitors

Self-protectionMalicious attacks

Cascading failures

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Architectural considerations (1/2)Architectural considerations (1/2)

Autonomic elements will manage

Internal behaviorRelationships with other autonomic elements

Autonomic element will consist of

Managed elementsHardware/software resource

Autonomic managerMonitoring the managed elements and external env.

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Architectural considerations (2/2)Architectural considerations (2/2)

Fully autonomic computingEvolve as designers gradually add increasingly sophisticated autonomic managers to existing managed elements

Autonomic elements will function at many levelsAt the lower levels

Limited range of internal behaviors

Hard-coded behaviors

At the higher levelsIncreased dynamism and flexibility

Goal-oriented behaviors

Hard-wired relationships will evolve into flexible relationships that are established via negotiation

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Engineering challenges (1/3)Engineering challenges (1/3)

Life cycle of an autonomic elementDesign, test, and verification

Testing autonomic elements will be challenging

Installation and configurationElement registers itself in a directory service

Monitoring and problem determinationElements will continually monitor themselves

Adaptation, optimization, reconfiguration

Upgrading

Uninstallation or replacement

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Engineering challenges (2/3)Engineering challenges (2/3)

Relationships among autonomic elementsSpecification

Set of output/input services of autonomic elementsExpressed in a standard formatEstablishing standard service ontologyDescription syntax and semantics

LocationFind input services that autonomic element needs

NegotiationProvisionOperation

Autonomic manager oversees the operation

Termination

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Engineering challenges (3/3)Engineering challenges (3/3)

Systemwide issuesAuthentication, encryption, signingAutonomic elements can identify themselvesAutonomic system must be robust against insidious forms of attack

Goal specificationHumans provide the goal and constraintsThe indirect effect of policiesEnsure that goals are specified correctly in the first placeAutonomic systems will need to protect themselves from input goals that are inconsistent, implausible, dangerous, or unrealizable

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Scientific challengesScientific challenges

Behavioral abstractions and modelsMapping from local behavior to global behavior is a necessary

Inverse relationship

Robustness theory

Learning and optimization theoryAgents continually adapt to their environment that consists of other agents

There are no guarantees of convergence

Negotiation theory

Automated statistical modeling

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Autonomic computing systems manage themselves according an administrator’s goals.

We believe that it is possible to meet the grand challenge of autonomic computing without magic and without fully solving the AI problem.

Conclusion