RCMP: A System Enabling Efficient Re-computation Based Failure Resilience for Big Data Analytics
Ng, T. S. Eugene
Multi-job I/O-intensive big-data computations can suffer a significant performance hit due to relying on data replication as the main failure resilience strategy. Data replication is inherently an expensive operation for I/O-intensive jobs because the datasets to be replicated are very large. Moreover, since the failure resilience guarantees provided by replication are fundamentally limited by the number of available replicas, jobs may fail when all replicas are lost. In this paper we argue that job re-computation should also be a first-order failure resilience strategy for big data analytics. Recomputation support is especially important for multi-job computations because they can require cascading re-computations to deal with the data loss caused by failures. We propose RCMP, a system that performs efficient job re-computation. RCMP improves on state-of-the-art big data processing systems which rely on data replication and consequently lack any dedicated support for recomputation. RCMP can speed-up a job’s re-computation by leveraging outputs that it stored during that job’s successful run. During re-computation, RCMP can efficiently utilize the available compute node parallelism by switching to a finer-grained task scheduling granularity. Furthermore, RCMP can mitigate hot-spots specific to re-computation runs. Our experiments on a moderate-sized cluster show that compared to using replication, RCMP can provide significant benefits during failure-free periods while still finishing multijob computations in comparable or better time when impacted by single and double data loss events.
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