Dynamic Job Ordering and Slot Configurations for MapReduce Workloads
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Dynamic Job Ordering and Slot Configurations for MapReduce Workloads
Abstract—Dynamic Job Ordering and Slot Configurations for < Final Year Projects 2016 > MapReduce Workloads. Map Reduce is a popular parallel computing paradigm for large-scale data processing in clusters and data centers. A Map Reduce 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.
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