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Challenges in Adapting Automated Planning for Autonomic Computing. Biplav Srivastava Subbarao Kambhampati IBM India Research Lab Arizona State University sbiplav@in.ibm.com rao@asu.edu ICAPS 2005, Monterey, CA, USA (Also being presented at 2 nd Intl. Conference on

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challenges in adapting automated planning for autonomic computing

Challenges in Adapting Automated Planning for Autonomic Computing

Biplav Srivastava Subbarao Kambhampati

IBM India Research Lab Arizona State University

sbiplav@in.ibm.comrao@asu.edu

ICAPS 2005, Monterey, CA, USA

(Also being presented at 2nd Intl. Conference on

Autonomic Computing)

the case for automated planning in autonomic computing

The Case for Automated Planning in Autonomic Computing

Biplav Srivastava Subbarao Kambhampati

IBM India Research Lab Arizona State University

sbiplav@in.ibm.comrao@asu.edu

ICAC 2005, Seattle, USA

Presented by: Hemal Khatri

planning in autonomic computing ac

S

E

Planning in Autonomic Computing (AC)
  • The ‘P’ of the M-A-P-E loop in an Autonomic Manager
  • Planning provides the policy engine for goal-type policies
    • Given expected system behavior (goals), determine actions to satisfy them
  • Synthesis, Analysis & Maintenance of plans of action is a vital aspect of Autonomic Computing
    • Example 1: Taking high-level behavioral specifications from humans, and control the system behavior in such a way as to satisfy the specifications
      • Change requests (e.g., INSTALL, UPDATE, REMOVE) from administrator in managing software on a machine (Solution Install scenarios)
    • Example 2: Managing/propagating changes caused by installations and component changes in a networked environment
      • Remediation in the presence of failure

Autonomic Manager

Plan

Analyze

Knowledge

Monitor

Execute

ManagedElement

information expected to be available while planning in ac scenarios
Information Expected to be Available while Planning in AC Scenarios
  • Planning is <P, I, G, A>
    • P is a set of predicates
    • I and G are initial and goal states drawn from P
    • A is a set of actions, Ai with
      • Aipre (preconditions) Aipost (postconditions) drawn from P
comparing current status of automated planning and the needs of ac planning
Highly scalable planners exist for synthesizing plans of actions. However:

They expect complete domain theories

They focus on plan generation rather than plan management

Planning technology is relevant for AC computing, but we also need:

Ability to handle incomplete domain theories

Focus on plan management rather than just plan synthesis

Support mixed initiative continual (re)planning

Comparing Current Status of Automated Planning and the Needs of AC planning

Early systems in AC:

a) CHAMPS: Domain-dependent planner

for self-configuration

b) ABLE-Planner4J: Domain-independent planning for self-* but expects complete I,G, A.

planning with incomplete domain theories
Domain theory is partial if correctness cannot be causally explained

Domain theory  Explanation  Modification

HTNs provide natural support

Explainability: Event vs. State constraints

EVENT: If you do a, then do b before c (don’t ask why!)

STATE: The condition p is required by a and is given by b

State constraints can be compiled to event constraints. But the reverse?

In Autonomic computing (as well as web-service composition, scientific workflow handling), the planner doesn’t have access to complete and correct specification

Action specifications may be incomplete

Domain theory may be in terms of dependencies

The planner can’t always verify correctness

..but can certainly look for errors in a plan

Planning with Incomplete Domain Theories
prescriptions
AC Practioners

Leverage current planning solutions in convenient scenarios – very efficient and will answer qns such as:

What interactions will occur if a new operation is introduced into the plan

What high-level goals will go unsupported if an action is removed

Expend time in effect-based modeling

Complete specifications make it easy to provide the causal dependency structure of the plan. This in turn helps in plan-management by allowing us to answer questions such as:

What interactions will occur if a new operation is introduced into the plan

What high-level goals will go unsupported if an action is removed

Planning Researchers

In AC, we can at most expect incomplete specification

Ordering constraints may be provided withoutan explanation of why they are needed

Some information about incompatibility of actions may be provided

Managing such plans poses two technical challenges:

Deriving additional dependencies between workflow operations

Adapting planning techniques to deal with partial causal information

Prescriptions
summary
Summary
  • We developed an understanding of the “planning” needs of AC computing
    • Connections with 2 other very close applications—Web Services, and Scientific Workflow management
  • Evaluated the match between existing planning technology and AC computing needs, and identified specific needed extensions
  • Currently focusing on plan synthesis and management with incomplete domain theories (such as are present in AC computing scenarios)
    • Impact will be measured in terms of availability of information sought about the domain and improvement in the quality of plans handled (analyzed/ generated/ managed).
    • Benchmarking will be in the software installation and problem determination scenarios.