A Constraint Language Approach to Grid Resource Selection Chuang Liu, Ian Foster Distributed System Lab University of Chicago Work performed within the Grid Application Development Software (GrADS) Project of the NSF Next Generation Software Program

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 Outline l Problem l Description Language (RedLine) l Matchmaking l Applications l Summary

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 Description Language l Use constraints to describe attributes.

6 Description Language l resource co-selection request.

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

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 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 Example

11 Example

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 Example

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 Example

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

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

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

21 RedLine System l Layered structure

22 Applications l Data Grid Example

23 Applications l Access Grid Example

24 Applications l Query Example

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

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