| Cluster 2005 | START ConferenceManager |
In this paper, we investigate a new system for computing efficient checkpoint schedules in cycle-harvesting environments. Our system records the historical availability from each resource and fits a statistical model to the observations. Because checkpointing must often traverse the network (i.e. the desktop hosts do not provide sufficient persistent storage for checkpoints), we combine this model with predictions of network performance to the storage site to compute a checkpoint schedule. When an application is initiated on a particular resource, the system uses the computed distribution to parameterize a Markov state-transition model for the application's execution, evaluates the expected time and network overhead as a function of the checkpoint interval, and numerically optimizes with respect to time.
We report on the performance of and implementation of this system using the Condor cycle-harvesting environment at the Universiity of Wisconsin. We also evaluate the efficiencies we achieve for a variety of network overheads using trace-based simulation. Finally, we validate our simulations against the observed performance with Condor. Our results indicate that while the choice of model distribution has a relatively small but positive effect on time efficiency, it has a substantial impact on network utilization.
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