Grid Resource Availability Prediction-Based Scheduling and Task Replication
The frequent and volatile unavailability of volunteer-based Grid computing resources challenges Grid schedulers to make effective job placements. The manner in which host resources become unavailable will have different effects on different jobs, depending on their runtime and their ability to be checkpointed or replicated. A multi-state availability model can help improve scheduling performance by capturing the various ways a resource may be available or unavailable to the Grid. This paper uses a multi-state model and analyzes a machine availability trace in terms of that model. Several prediction techniques then forecast resource transitions into the model's states. We analyze the accuracy of our predictors, which outperform existing approaches. We also propose and study several classes of schedulers that utilize the predictions, and a method for combining scheduling factors. We characterize the inherent tradeoff between job makespan and the number of evictions due to failure, and demonstrate how our schedulers can navigate this tradeoff under various scenarios. Lastly, we propose job replication techniques, which our schedulers utilize to replicate those jobs that are most likely to fail. Our replication strategies outperform others, as measured by improved makespan and fewer redundant operations. In particular, we define a new metric for replication efficiency, and demonstrate that our multi-state availability predictor can provide information that allows our schedulers to be more efficient than others that blindly replicate all jobs or some static percentage of jobs.
availability prediction scheduling replication multi-state characterization