The responsibility of contemporary multi-core processors is oftentimes bent on by a given power ration that requisite developer to evaluate different resolution trade-offs, e.g., to espouse between many slow, power-efficient cores, or fewer faster, power-hungry cores, or a amalgamation of them . Here, a prototype, a new Hadoop scheduler, called adjudicator, that utilizes aptness proffered by heterogeneous cores within a single multi-core processor for accomplishing a variety of performance objectives. 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” apertures, spare resources (apertures) can be shared between different resource pools. Using sample experimental data and via simulation, a wrangle is made in approbation of heterogeneous multi-core processors as they achieve “faster” processing of small, interactive MapReduce jobs, while proffering improved throughput for large, batch jobs. Evaluation is done on performance benefits of adjudicator versus the FIFO and Capacity job schedulers that are broadly used in the Hadoop community.

Hadoop, MapReduce, Adjudicator, Job scheduler, Computing, Heterogeneous

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