Taming Non-local Stragglers Using Efficient Prefetching in MapReduce

Abstract

MapReduce has been widely adopted as a programming model to process big data. However, parallel jobs in MapReduce are prone to be plagued by stragglers caused by non-local tasks for two reasons: first, system logs from production clusters show that a non-local task can be two times slower than a local task; second, a job's completion time is bottlenecked by its slowest parallel tasks. As a result, even one single non-local task can become the straggler of the whole job, causing significant delay of the whole job. In this paper, we propose to alleviate this problem by proactively prefetching input data for non-local tasks. However, performing such prefetching efficiently in MapReduce is difficult, because it requires both application-level information to generate accurate prefetching requests at runtime, and an appropriate network flow scheduling mechanism to guarantee the timeliness of prefetching flows. To address these challenges, we design and implement FlexFetch, which 1) leverages a novel mechanism called speculative scheduling to accurately generate prefetching flows, 2) explicitly allocates network resources to prefetching flows using a criticality-aware deadline-driven flow scheduling algorithm. We evaluate FlexFetch through both testbed experiments and large-scale simulations using production workloads. The results show that FlexFetch reduces the completion time by 41.8% for small jobs and 26.8% on average, compared with the default MapReduce implementation in Hadoop.