Energy management for MapReduce clusters

Energy Management for MapReduce Clusters

The area of cluster-level energy management has attracted significant research attention over the past few years. One class of techniques to reduce the energy consumption of clusters is to selectively power down nodes during periods of low utilization to increase energy efficiency. One can think of a number of ways of selectively powering down nodes, each with varying impact on the workload res...

Towards Energy-Efficient Management of MapReduce Workloads

Resource-Aware Adaptive Scheduling for MapReduce Clusters

We present a resource-aware scheduling technique for MapReduce multi-job workloads that aims at improving resource utilization across machines while observing completion time goals. Existing MapReduce schedulers define a static number of slots to represent the capacity of a cluster, creating a fixed number of execution slots per machine. This abstraction works for homogeneous workloads, but fai...

Job Scheduling for Multi-User MapReduce Clusters

Sharing a MapReduce cluster between users is attractive because it enables statistical multiplexing (lowering costs) and allows users to share a common large data set. However, we find that traditional scheduling algorithms can perform very poorly in MapReduce due to two aspects of the MapReduce setting: the need for data locality (running computation where the data is) and the dependence betwe...

Towards Energy Efficient MapReduce

Energy considerations are important for Internet datacenters operators, and MapReduce is a common Internet datacenter application. In this work, we use the energy efficiency of MapReduce as a new perspective for increasing Internet datacenter productivity. We offer a framework to analyze software energy efficiency in general, and MapReduce energy efficiency in particular. We characterize the pe...