1. A Constraint Language Approach to Grid 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. 2 Problems l Selection of resources whose properties are expressed by a feature set or range l 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. 3 Outline l Problem l Description Language (RedLine) l Matchmaking l Applications l Summary

4. 4 Description Language ClassAds RedLine description language A set of named Expressions called ClassAds A set of constraints on value of attribute called Description Limited support for set expression data type set and related functions, such as Sum, Cardinality, Set_Intersection, etc.

5. 5 Description Language l Use constraints to describe attributes.

6. 6 Description Language l resource co-selection request.

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

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

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

10. 10 Example

11. 11 Example

12. 12 Definition of Match l RedLine also defines multilateral match: Descriptions D 1, D 2, …, D n match a description R if D 1, D 2, …, D n 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. 13 Example

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

15. 15 Example

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

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

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

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

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

21. 21 RedLine System l Layered structure

22. 22 Applications l Data Grid Example

23. 23 Applications l Access Grid Example

24. 24 Applications l Query Example

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

26. 26 Constraint A constraint C is of the form c1  …  cn where n >= 0 and c1, …, cn are primitive constraints. The symbol  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  vars(C) such that C holds. Otherwise, it is unsatisfiable. vars(C) denotes the set of variables occurring in constraint C.

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