Addressing job processing variability through redundant execution and opportunistic checkpointing: A competitive analysis

The completion times of jobs in a computing cluster may be influenced by a variety of factors including job size and machine processing variability. In this paper, we explore online resource allocation policies which combine size-dependent scheduling with redundant execution and opportunistic checkpointing to minimize the overall job flowtime. We introduce a simplified model for the job service capacity of a computing cluster while leveraging redundant execution/checkpointing. In this setting, we propose two resource allocation algorithms, SRPT+R and LAPS+R(β) subject to checkpointing overhead not exceeding the number of jobs which are processed. We provide new theoretical performance bounds for these algorithms: SRPT+R is shown to be O(1/∊) competitive under (1 + ∊)-speed resource augmentation, while LAPS+R(β) is shown to be O(1/β∊) competitive under (2+ 2β + 2∊)-speed resource augmentation.