1. Automatic Statistical Evaluation of Resources for Condor Daniel Nurmi, John Brevik, Rich Wolski University of California, Santa Barbara

2. Motivation Distributed System/Grid applications execute on wide variety of architectures –Clusters –Large SMP systems –Interactive workstation networks Condor provides vast, easily accessible resource pool, but is best suited to Condor applications

3. Condor As Resource Pool Provides many required features –Resource manager –Account manager –Scheduler Resource availability very dynamic –Controlled by large number of variables including overall load, user priority, occupancy time, owner revocation, etc. –Resources free up and drop out frequently Long running apps must be checkpointed

4. Checkpointing Schemes Condor checkpointing –Standard Universe uses system call liftoff –Core file is used to capture process state for restart Application-level checkpointing: –Application developer must generate checkpoints from within the application –Disk storage may be limited (none available locally)

5. Condor Checkpointing Checkpointing is invisible to application developer, but… –No threads –No forking –Single architecture support –Must use compiler supported by Condor (e.g. no GMP)

6. Application-Level Checkpointing No support from Condor for checkpointing in Vanilla universe –Left to the application No restrictions on system calls or compilation –If it compiles it will run No local disk storage –Checkpoints must traverse the network to a machine with stable storage Checkpoint schedule major performance concern

7. Checkpoint Scheduling Given a long running application and volatile resource, determine the amount of time perform useful computation between checkpoints such that the overhead of checkpointing is minimized Well studied –K. M. Chandy, C. V. Ramamoorthy. Rollback and recovery strategies for computer systems. –M. Elnozahy, L. Alvisi, Y. M. Wang, D. B. Johnson. A survey of rollback-recovery protocols in message passing systems. –A. Duda. The effects of checkpointing on program execution time. –N. H. Vaidya. Impact of checkpoint latency on overhead ratio of a checkpointing scheme We use Markov Model based approach proposed by N. H. Vaidya.

8. Checkpoint Interval Selection Model requires statistical distribution describing resource availability –Vaidya, and later Plank assume exponential distributions

9. What is the Availability Distribution? Weibull –T. Heath, P. M. Martin, T. D. Nguyen. The shape of failure –J. Xu, Z. Kalbarczyk, R. K. Iyer. Networked Windows NT system field failure data analysis Hyperexponential –M. Mutka, M. Livny. Profiling workstations’ available capacity for remote execution. –I. Lee, D. Tang, R. K. Iyer, M. C. Hsueh. Measurement-based evaluation of operating system fault tolerance.

10. Generating Statistical Models Network Weather Service monitoring of Condor pool over 2 year period –708 machines observed Automatic model fitting software –Takes as input distribution type and historical Condor uptime values –Outputs best fit parameters for given distribution Design experiment to test overall work efficiency of checkpointing scheme using four different distributions

11. Checkpoint Experiment Test application submitted to Condor and when it runs… –Sends resource information to central server –Model fitting software estimates model parameters using MLE or EMpht methods –Checkpoint scheduler solves the Markov model using tested distribution –Application uses schedule, checkpoints its memory, and records performance Test different distributions Checkpointing to disks at UCSB

12. Empirical Results: Execution Time

13. Empirical Results: Network Utilization

14. Moral We can determine optimal checkpoint schedules for Condor jobs automatically –Execution performance impact is about the same until checkpoint costs get big –Network load improvements are substantial (particularly useful in wide area) Software is real, but non-NWS parts are in prototype –We want to bring them into the NWS release cycle Paper in submission to HPDC

15. What’s Next Better Models –Brevik Method: we can predict the percentiles of availability with provable confidence bounds using less data –Can’t use it (yet) for Markov model Better Utility –Provide information to Condor itself –Automatic fault and anomaly detection Better Information for users –Publish availability predictions the in matchmaker

16. Thanks Rich Wolski John Brevik Miron Livny NSF Next Generation Software program VGrADS Project (NSF ITR, Ken Kennedy, PI) NSF Middleware Initiative (NWS) Questions?

17. Simulation Results: Execution Time

18. Simulation Results: Network Utilization