29 / 2018-03-22 13:15:47

Dynamic Job Ordering and Slot Configurations

dynamic,jobs

MapReduce is a popular parallel computing paradigm for large-scale data processing in clusters and data centers. A
MapReduce workload generally contains a set of jobs, each of which consists of multiple map tasks followed by multiple reduce tasks.
Due to 1) that map tasks can only run in map slots and reduce tasks can only run in reduce slots, and 2) the general execution
constraints that map tasks are executed before reduce tasks, different job execution orders and map/reduce slot configurations for a
MapReduce workload have significantly different performance and system utilization. This paper proposes two classes of algorithms
to minimize the makespan and the total completion time for an offline MapReduce workload. Our first class of algorithms focuses on
the job ordering optimization for a MapReduce workload under a given map/reduce slot configuration. In contrast, our second class of
algorithms considers the scenario that we can perform optimization for map/reduce slot configuration for a MapReduce workload. We
perform simulations as well as experiments on Amazon EC2 and show that our proposed algorithms produce results that are up to
15
80 percent better than currently unoptimized Hadoop, leading to significant reductions in running time in practice.