Fabián E. Bustamante, Winter 2006

Autonomic Computing

The vision of autonomic computing, J. Kephart and D. Chess, IEEE Computer, Jan. 2003.

Also - A.G. Ganek and T.A. Corbi, “The dawning of the autonomic computing era”, IBM Systems Journal, 42 (1), 2003.

- R. Want, T. Pering and D. Tennehouse, “Comparing autonomic and proactive computing”, IBMS Systems Journal, 42 (1), 2003.

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CS 395/495 Autonomic Computing SystemsEECS, Northwestern University

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The problem

The main obstacle to further progress in IT industry– Not a change in Moore’s law, but – Looming software complexity crisis

• Beyond admin single environments, to integration into intra- and inter-corporate computing systems

“Complexity is the business we are in, and complexity is what limits us.”, Fred Brooks Jr.

Better programming won’t do it Consider– ~1/3 to ½ of a company’s total IT budget goes to preventing

and recovering from crashes– “For every dollar to purchase storage, you spend $9 to have

someone manage it.”, N. Tabellion, CTO Fujitsu Softek– ~40% of computer outages are caused by operator errors– Average downtime impact for IT ~ $1.4 millions revenue/hour

CS 395/495 Autonomic Computing SystemsEECS, Northwestern University

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The answer/hope – Autonomic computing

Autonomic systems – can manage themselves given high-level objectives from admins.~ autonomic nervous system

An autonomic system– Knows itself– Knows its environment & the context surrounding its activity– (Re) configure itself under varying and unpredictable

conditions– Is always on the look to optimize its working– Is able to protect and heal itself– Anticipates the optimized resources needed to meet a user’s

information needs

To incorporate these characteristics, it must have the following properties/features …

CS 395/495 Autonomic Computing SystemsEECS, Northwestern University

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Self-* properties

Self-configuration– Current: Data centers made of components from/for multiple

vendors/platforms; installation, configuration & integration is time consuming & error prone

– Autonomic: Automated based high-level policies, host system adjust itself automatically and seamalessly

Self-optimization– Current: Hundreds of manually set, nonlinear tuning knobs– Autonomic: Components and system continually seek

optimization opportunities

Self-healing– Current: e.g. problem determination can take weeks– Autonomic: self detection, diagnosis, and repair for HW&SW

Self-protection– Current: Detection & recovery from attacks & cascading

failures is manual– Autonomic: Self-defense using early warning to anticipate &

prevent system-wide failures

CS 395/495 Autonomic Computing SystemsEECS, Northwestern University

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Autonomic element

Autonomic systems – interactive collection of autonomic elementsAutonomic element– 1+ managed elements +

autonomic manager that controls it

– Function at many levels – from disk drives to entire enterprises

– Fixed behavior, connections and relationships gives away to increased dynamism and flexibility expresed as high-level goals

Autonomic manager

Analyze Plan

Knowledge

Monitor Execute

Managed element

CS 395/495 Autonomic Computing SystemsEECS, Northwestern University

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Evolution to autonomic systems

Basic Level 1

Managed Level 2

Predictive Level 3

Adaptive Level 4

Autonomic Level 5

Multiple sources of system generated data

Requires extensive, highly skilled IT staff

Consolidation of data through management tools

IT staff analyzes and takes actions

System monitors, correlates, and recommends actions

IT staff approves and initiate actions

System monitors, correlates and takes actions

IT staff manages performance against Service Level Agreements (SLAs)

Integrated components dynamically managed by business rules/policies

IT staff focuses on enabling business needs

Greater system awareness

Improved productivity

Reduced dependency on deep skills

Faster and better decision making

IT agility and resiliency with minimal human interaction

Business policy drives IT management

Business agility and resilience

CS 395/495 Autonomic Computing SystemsEECS, Northwestern University

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Engineering challenges

Design, test and verification

Installation and configuration

Monitoring, problem determination, upgrading

Managing the life cycle– Autonomic systems will have multiple elements at different

stages, handling multiple tasks, … how to handle all?

Relationships among autonomic elements– Specification of services needed/provided; ways to locate

providers; ways to establish SLA; …

Robustness against self-management-based attacks

Goal specification and robustness to wrongly specified goals

CS 395/495 Autonomic Computing SystemsEECS, Northwestern University

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Scientific challenges

How to understand, control, and design emergent behavior– Understanding the mapping from local to global behavior is

not enough

Develop a theory of robustness – Beginning with a definition

Learning and optimization theory– Machine learning by a single element in static environment is

just the basic – multiagent systems in dynamic environments

Negotiation theory– How should the multiple elements negotiate?

Automated statistical modeling– Statistical modeling for detection/prediction of performance

models; ways to aggregate statistical variables to reduce dimensionality