Cooperative checkpointing theory

Cooperative checkpointing uses global knowledge of the state and health of the machine to improve performance and reliability by dynamically deciding when to skip checkpoint requests made by applications. Using results from cooperative checkpointing theory, this paper proves that periodic checkpointing is not expected to be competitive with the offline optimal. By leveraging probabilistic information about the future, cooperative checkpointing gives flexible algorithms that are optimally competitive. The results prove that simulating periodic checkpointing; by performing only every dth checkpoint, is not competitive with the offline optimal in the worst case; a simple modification gives a provably competitive algorithm. Calculations using failure traces from a prototype of IBM's Blue Gene/L show an application using cooperative checkpointing may make progress 4 times faster than one using periodic checkpointing, under realistic conditions. We contribute an approach to providing large-scale system reliability through cooperative checkpointing and techniques for analyzing the approach

[1]  James S. Plank,et al.  Processor Allocation and Checkpoint Interval Selection in Cluster Computing Systems , 2001, J. Parallel Distributed Comput..

[2]  Anand Sivasubramaniam,et al.  Critical event prediction for proactive management in large-scale computer clusters , 2003, KDD '03.

[3]  Meeta Sharma Gupta,et al.  Performance implications of periodic checkpointing on large-scale cluster systems , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[4]  I. Rish,et al.  Autonomic Computing Features for Large-scale Server Management and Control , 2003 .

[5]  R. Vilalta,et al.  Providing Persistent and Consistent Resources through Event Log Analysis and Predictions for Large-scale Computing Systems , 2002 .

[6]  Mark S. Squillante,et al.  Failure data analysis of a large-scale heterogeneous server environment , 2004, International Conference on Dependable Systems and Networks, 2004.

[7]  E. N. Elnozahy,et al.  Checkpointing for peta-scale systems: a look into the future of practical rollback-recovery , 2004, IEEE Transactions on Dependable and Secure Computing.

[8]  Anand Sivasubramaniam,et al.  Filtering failure logs for a BlueGene/L prototype , 2005, 2005 International Conference on Dependable Systems and Networks (DSN'05).

[9]  Jason Duell,et al.  The Lam/Mpi Checkpoint/Restart Framework: System-Initiated Checkpointing , 2005, Int. J. High Perform. Comput. Appl..

[10]  Anand Sivasubramaniam,et al.  Fault-aware job scheduling for BlueGene/L systems , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[11]  Larry Rudolph,et al.  Probabilistic QoS guarantees for supercomputing systems , 2005, 2005 International Conference on Dependable Systems and Networks (DSN'05).

[12]  Adam Jamison Oliner.  Cooperative checkpointing for supercomputing systems , 2005 .

[13]  David F. Heidel,et al.  An Overview of the BlueGene/L Supercomputer , 2002, ACM/IEEE SC 2002 Conference (SC'02).

[14]  James S. Plank,et al.  Experimental assessment of workstation failures and their impact on checkpointing systems , 1998, Digest of Papers. Twenty-Eighth Annual International Symposium on Fault-Tolerant Computing (Cat. No.98CB36224).

[15]  Peter A. Dinda,et al.  A prediction-based real-time scheduling advisor , 2002, Proceedings 16th International Parallel and Distributed Processing Symposium.