1 Performance Impact of Resource Provisioning on Workflows Gurmeet Singh, Carl Kesselman and Ewa Deelman Information Science Institute University of Southern California Presented by Nut Taesombut Large-Scale Systems Seminar May 9, 2005

2 Outline Background and Objective Simulation Study Experimental Results Summary & Discussion

3 Background Grid –Resource sharing across multiple domains Workflow application –A collection of tasks with specified dependencies How to map a workflow application onto distributed resources in such a way to minimize its completion time ? T1 T2 T3 T4 T5 T6 T7 Grid Resources Workflow Application Schedule

4 Resource Provisioning Reserve resources for dedicated use by an application for certain timeframe Tradeoff between predictability of workflow performance and waiting time cost –Performance predictability enables efficient resource selection –Need to wait until the provisioned resources become available

5 Objective Study of performance impact of resource provisioning for a workflow application –Two performance metrics –Completion Time: Wait Time + Runtime –Resource Utilization –Three resource provisioning mechanisms –No provisioning –Advance reservation –Dynamic provisioning –Two resource scheduling policies –FIFO (First-in, First-out) –Variant Fair Share

6 Simulation Study Maui simulator –Simulate the running of jobs on the 890-processor cluster with different scheduling policies Workload trace from the TeraGrid facility –2095 jobs on 10-day collection (2/22/05 – 3/3/05) –102 initial running jobs –Record both requested and actual runtimes

7 Workload Characteristics More than half of the jobs requested a runtime between 16 and 24 hours Most of the jobs completed in shorter time than they requested Requested Runtime Actual Runtime

8 Application Workflows Original Montage workflow –Contain tasks in 7 levels (3.1 second average runtime) 12 Clustered workflows (generated from the original) –Produce workflow graphs with different levels of granularity and structures –Group the tasks at each level into a fixed number of clusters clustering parameter = 8 clustering parameter = 2

9 Advance Reservation Reserve resources for an application in the future –Use the Maui simulator to determine the earliest start time –Decide based on the requested runtimes of the running and queued jobs Request selection –Multiple requests can execute the workflow –Use the best request to determine the minimum completion time of the workflow –Decide based on the earliest start time and the metric to optimize

10 Dynamic Provisioning Reserve resources for an application during runtime –Use the Maui simulator to determine the actual start time –Compute based on the actual runtimes of the running and queued jobs Request selection –Use the best request as determined by advance reservation

11 Experimental Results (1): Varied Requests Completion time of various provisioning requests The use of provisioning can reduce the completion time –For FIFO with 84 processors, –65% in advance reservation –82% in dynamic provisioning Dynamic Provisioning vs. Advance Reservation –FIFO: D.P. always outperforms A.R. –Fair Share: D.P. and A.R. performs equally well FIFO Fair Share

12 Experiment Results (2): Varied Workflows Completion time of various workflows with their best request The use of provisioning can reduce the completion time –Large workflows tend to obtain significant reduction FIFO Fair Share

13 Experiment Results (3): Resource Utilization Completion time and resource utilization of various workflows using advance reservation –Consider utilization in optimization metric Resource utilization can be much improved by small increase in the completion time Utilization | FIFO Completion Time | FIFO

14 Experiment Results (4): Resource Utilization Completion time and resource utilization of various workflows using advance reservation –Consider utilization in optimization metric The requests that optimize resource utilization also optimize completion time Utilization | Fair Share Completion Time | Fair Share

15 Summary Study the performance impact of using resource provisioning for a workflow application –Significant reduction in the completion time for both FIFO and Fair Share scheduling policies –Amount of the reduction depends on scales and structures of the workflows –Resource utilization can be improved with small increase in the completion time

16 Discussion Is the proposed model practical for Grid environments? –Assume homogeneous resource capability and scheduling policies –Assume deterministic and known workflow characteristics (e.g., runtime) –Assume support for reservation systems