1. Design and Evaluation of a Resource Selection Framework for Grid Applications
Chuang Liu, Lingyun Yang, Dave Angulo, 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. Problem Co-selection of resources, which involves
Select a set resources for an Grid application that meet application requirements
Map workload to resources
Investigate feasibility of declarative language for expressing application requirements and resource capabilities
Set matching as an extension of matchmaking

3. Outline Problem Set matching Resource selection service Experiments
Summary

4. ClassAds Language (U.Wisconsin Condor Group)
ClassAds is a language for expressing and evaluating a named expression
A ClassAd is a set of named expressions
Attributes: describe characteristics
Requirements: identify matched ClassAds
Rank: ranks matched ClassAds

5. Set Match Set extended ClassAds language Matchmaker
Represent characteristics of resources
Represent requests for a resource set
Matchmaker
Construct candidate resource sets
Identify compatible matches between requests and candidate resource sets
Rank compatible matches

6. Set Match: Set-extended ClassAds Language
Aggregation functions for set attributes
Max(expression), Min(expression), Sum(expression), SetSize(set)
Other functions
Suffix(S, <string List>), Prefix(S, <string List>)
Example:

7. Set-extended Condor matchmaking engine
Set Match: Matchmaker
requests
Set-extended Condor matchmaking engine
Set
Resource ClassAd 1
Constructor
Resource ClassAd 2
resources
evaluate
Request ClassAd
Resource ClassAd 3
{Res2}
{Res1,Res2}
Resource ClassAd 4
{Res1,Res3}
match, or
fail

8. Set Match: An Example

9. Outline Problem Set matching Resource selection service Experiments
Summary

10. Resource Selection Service (RSS)
Help applications to choose a good resource set in Grid environment
Synchronous and asynchronous service
Mapping application workload to resources if needed

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

12. RSS: Resource Request Owner: The sender of this request
Type of Service: Synchronous or asynchronous
Job description: The characteristics of the job to be run, for example, problem size, and the performance model
Job resource requirements: User resource requirements, for example, memory capability, type of operating system, software packages installed, etc.
Mapper: The mapper algorithm to be used
Rank: Criteria to rank the matched resources

13. Resource Request: An Example
Type of Service
1. [ Service = “SynService";
2. iter=100; alpha=100; x=100; y=100; z=100;
3. computetime = x*y*alpha/other.cpuspeed*370;
4. comtime= ( other.RLatency+ y*x*254/other.RBandwidth
+other.LLatency+y*x*254/other.Lbandwidth);
5. exectime=(computetime+comtime)*iter+startup;
6. Mapper = [type ="dll";
libraryname="cactus"; function="mapper"];
7. requirements = Sum(other.MemorySize) >= ( *z*x*y) && suffix(other.machine, domains);
8. domains={ “cs.utk.edu”, “ucsd.edu”};
9. rank=Min(1/exectime) ]
Job description
Mapper
Resource Requirements
Rank

14. Resource Request: Result
<virtualMachine>
<result statusCode="200" statusMessage="OK"/>
<machineList>
<machine dns="torc2.cs.utk.edu" processor= 2 x= 20>
<machine dns="torc3.cs.utk.edu" processor= 2 x= 15>
<machine dns="torc6.cs.utk.edu" processor= 2 x= 15>
</machineList>
</virtualMachine>

15. Outline Problem Set Match Resource Selection Service Experiments
Summary

16. Experiments: Application & Testbed
Cactus code that simulates the 3D scalar field produced by two orbiting sources
Application performance model
ExecTime= Comm. time + Computation time+ start-up
Application mapping algorithm
1 Dimension mapping
Experiment environment
GrADS testbed
Comprises workstation clusters at universities across the United States (including the University of Chicago, UIUC, UTK, UCSD, Rice University, and USC/ISI)

17. Experiments: Mapping Result
Machine1: 450 MHz, no CPU load
Machine2: 500 MHz, CPU load=2

18. Experiments: Resource Selection (One Site)
1. o.ucsd.edu, mystere.ucsd.edu, saltimbanco.ucsd.edu
2: mystere.ucsd.edu, o.ucsd.edu
3: o.ucsd.edu, Saltimbanco.ucsd.edu : o.ucsd.edu
5: saltimbanco.ucsd.edu : mystere.ucsd.edu

19. Experiments: Resource Selection (Two Sites)
1: torc6.cs.utk.edu 2: o.ucsd.edu 3: Saltimbanco.ucsd.edu
4: torc6.cs.utk.edu + o.ucsd.edu : o.ucsd.edu + saltimbanco.ucsd.edu
6: o.ucsd.edu, mystere.ucsd.edu, torc 6: cs.utk.edu

20. Summary
Extended the ClassAds language to describe set-based requirement for a resource set
Implemented a set matchmaker & created a resource selection service framework
Validation with Cactus application
Future: Extend semantics, implementation, application of set matching framework
Thanks to
NSF Next Generation Software Program
Alain Roy, Jennifer Schopf, GrADS colleagues