Optimizing Speculative Execution of Deadline-Sensitive Jobs in Cloud

摘要

In this paper, we bring various speculative scheduling strategies together under a unifying optimization framework, which defines a new metric, Probability of Completion before Deadlines (PoCD), to measure the probability that MapReduce jobs meet their desired deadlines. We propose an optimization problem to jointly optimize PoCD and execution cost in different strategies. Three strategies are prototyped on Hadoop MapReduce and evaluated against two baseline strategies using experiments. A 78% net utility increase with up to 94% PoCD and 12% cost improvement is achieved.