Speculative Execution for a Single Job in a MapReduce-Like System

Parallel processing plays an important role for large-scale data analytics. It breaks a job into many small tasks which run parallel on multiple machines such as MapReduce framework. One fundamental challenge faced to such parallel processing is the straggling tasks as they can delay the completion of a job seriously. In this paper, we focus on the speculative execution issue which is used to deal with the straggling problem in the literature. We present a theoretical framework for the optimization of a single job which differs a lot from the previous heuristics-based work. More precisely, we propose two schemes when the number of parallel tasks the job consists of is smaller than cluster size. In the first scheme, no monitoring is needed and we can provide the job deadline guarantee with a high probability while achieve the optimal resource consumption level. The second scheme needs to monitor the task progress and makes the optimal number of duplicates when the straggling problem happens. On the other hand, when the number of tasks in a job is larger than the cluster size, we propose an Enhanced Speculative Execution (ESE) algorithm to make the optimal decision whenever a machine is available for a new scheduling. The simulation results show the ESE algorithm can reduce the job flow time by 50% while consume fewer resources comparing to the strategy without backup.