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A Constraint Language Approach to Resource Selection in Grid Computing

This paper presents a systematic approach to resource selection in grid computing using a constraint language framework. We introduce RedLine, a description language that enables both resource providers and requesters to articulate the attributes and requirements of resources effectively. By redefining the matchmaking problem as a Constraint Satisfaction Problem (CSP), we leverage established algorithms in constraint solving for efficient resource allocation. Furthermore, we discuss the future prospects of implementing service interfaces and optimizing algorithm performance in practical applications.

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A Constraint Language Approach to Resource Selection in Grid Computing

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  1. A Constraint Language Approach toGrid Resource Selection Chuang Liu, Ian Foster Distributed System Lab University of Chicago http://dsl.cs.uchicago.edu Work performed within the Grid Application Development Software (GrADS) Project of the NSF Next Generation Software Program

  2. Problems • Selection of resources whose properties are expressed by a feature set or range • Co-selection of resources • Description of requirement for a resource set for example, aggregation characteristics of a resource set. • Efficient algorithm to locate resource set

  3. Outline • Problem • Description Language (RedLine) • Matchmaking • Applications • Summary

  4. Description Language

  5. Description Language • Use constraints to describe attributes.

  6. Description Language • resource co-selection request.

  7. Outline • Problem • Description Language • Matchmaking • Applications • Summary

  8. Description of Resources and Requests • Both resources owners and requesters use RedLine syntax to describe their resources or requests • The requestor and resource providers must use the same variable name to express a resource attribute and associate common meaning to responding values.

  9. Definition of Match • A constraint C is satisfiable if there exists a value assignment to every variable v Î vars(C) such that C holds. Otherwise, it is unsatisfiable. vars(C) denotes the set of variables occurring in constraint C. • RedLine defines bilateral match: Two descriptions C1 and C2 match each other if C1  C2 is satisfiable. Scope of satisfying Capability Scope of resource Capability

  10. Example

  11. Example

  12. Definition of Match • RedLine also defines multilateral match: Descriptions D1, D2, … , Dnmatch a description R if D1, D2, …, Dn is an assignment to variables with description or description set type in description R and R is still satisfiable after replacing these variables with their values.

  13. Example

  14. Matchmaking Problem as CSP • A constraint satisfaction problem, or CSP, • A Constraint on variables • Every variable has a finite value domain • Matchmaking as CSP problem • Associate a variable with every requested resource called resource variable • Domain of every resource variable are available resources

  15. Example

  16. Matchmaking Process as Constraint Solving • CSP & Constraint solving • Sound theory developed in AI, Logic programming • Existing algorithms of constraint solving • systematic search • Backtracking algorithm • heuristic and stochastic algorithms • Hill-Climbing, Min-Coflict and Tabu-Search

  17. Performance of Algorithms • Evaluation of different algorithms • Completion of algorithm • Speed of algorithm • User’s controls on matchmaking process • Search# • Distribution <algorithm> • SetConstruct <algorithm>

  18. User’s Control on Matchmaking Process • User controls matchmaking process by predicate

  19. Summary • Describe resource properties whose value is expressed as a feature set or a range • Describe set-based requirement for a resource set • Formalize matchmaking problem into a Constraint Satisfaction problem and utilize algorithms developed in CSP area to solve it • Future: Service Interface implementation, Organization of descriptions in matchmaker, and study performance of the algorithm in in realistic application settings • Thanks to • NSF Next Generation Software Program • Alain Roy, GrADS colleagues • http://dsl.cs.uchicago.edu

  20. Outline • Problem • Description Language • Matchmaking • Redline System & Applications • Summary

  21. RedLine System • Layered structure

  22. Applications • Data Grid Example

  23. Applications • Access Grid Example

  24. Applications • Query Example

  25. Summary • Describe resource properties whose value is expressed as a feature set or a range • Describe set-based requirement for a resource set • Formalize matchmaking problem into a Constraint Satisfaction problem and utilize algorithms developed in CSP area to solve it • Future: Service Interface implementation, Organization of descriptions in matchmaker, and study performance of the algorithm in in realistic application settings • Thanks to • NSF Next Generation Software Program • Alain Roy, GrADS colleagues • http://dsl.cs.uchicago.edu

  26. Constraint • A constraint C is of the form c1 Ù … Ù cn where n >= 0 and c1, …, cn are primitive constraints. The symbol Ù denotes and, so a constraint C holds whenever all of the primitive constraints c1, …, cn hold. • A constraint C is satisfiable if there exists a value assignment to every variable v Î vars(C) such that C holds. Otherwise, it is unsatisfiable. vars(C) denotes the set of variables occurring in constraint C.

  27. Resource Selection Service: Framework RSS GIIS Resource Information Resource Request MDS Set GRISes Resource App Matcher Monitor NWS Result Mapper

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