Optimization for speculative execution in a MapReduce-like cluster

A parallel processing job can be delayed substantially as long as one of its many tasks is being assigned to an unreliable machine. To tackle this so-called straggler problem, most parallel processing frameworks such as MapReduce have adopted various strategies under which the system may speculatively launch additional copies of the same task if its progress is abnormally slow or simply because extra idling resource is available. In this paper, we focus on the design of speculative execution schemes for a parallel processing cluster under different loading conditions. For the lightly loaded case, we analyze and propose two optimization-based schemes, namely, the Smart Cloning Algorithm (SCA) which is based on maximizing the job utility. We also derive the workload threshold under which SCA should be used for speculative execution. Our simulation results show SCA can reduce the total job flowtime by nearly 22% comparing to the speculative execution strategy of Microsoft Mantri. For the heavily loaded case, we propose the Enhanced Speculative Execution (ESE) algorithm which is an extension of the Microsoft Mantri scheme. We show that the ESE algorithm can beat the Mantri baseline scheme by 35% in terms of job flowtime while consuming the same amount of resource.