DyScale: a MapReduce Job Scheduler for Heterogeneous Multicore Processors

DyScale: a MapReduce Job Scheduler for Heterogeneous Multicore Processors

Abstract— DyScale: a MapReduce Job Scheduler for Heterogeneous Multicore Processors. The functionality of modern multi-core processors is often driven by a given power budget that requires designers to evaluate different decision trade-offs, e.g.,to choose between many slow, power-efficient cores, or fewer faster, power-hungry cores, or a combination of them. Here,we prototype and evaluate a new Hadoop scheduler,called DyScale,that exploits capabilities offered by heterogeneous cores within a single multi-core processor for achieving a variety of performance objectives. A typical MapReduce workload contains jobs with different performance goals: large, batch jobs that are throughput oriented, and smaller interactive jobs that are response time sensitive. Heterogeneous multi-core processors enable creating virtual resource pools based on “slow” and “fast” cores for multi-class priority scheduling. Since the same data can be accessed with either “slow” or “fast” slots, spare resources < Final Year Projects 2016 > slots can be shared between different resource pools.