Optimizing MapReduce Framework through Joint Scheduling of Overlapping Phases
Huanyang Zheng, Ziqi Wan, Jie Wu · 2016
MapReduce includes three phases of map, shuffle, and reduce. Since the map phase is CPU-intensive and the shuffle phase is I/O-intensive, these phases can be conducted in parallel. This paper studies a joint scheduling optimization of overlapping map and shuffle phases to minimize the average job makespan. Challenges come from the dependency relationship between map and shuffle phases, since the shuffle phase may wait to transfer the data emitted by the map phase. A new concept of the strong pair is introduced. Two jobs are defined as a strong pair, if the shuffle and map workloads of one job equal the map and shuffle workloads of the other job, respectively. We prove that, if the entire set of jobs can be decomposed to strong pairs of jobs, then the optimal schedule is to pairwisely execute jobs that can form a strong pair. Following the above intuition, several offline and online scheduling policies are proposed. They first group jobs according to job workloads, and then, execute jobs within each group through a pairwise manner. Real data-driven experiments validate the efficiency and effectiveness of the proposed policies.