Evaluating MapReduce for profiling application traffic
Proceedings of the first edition workshop on High performance and programmable networking
Clotho: an elastic MapReduce workload/runtime co-design
Proceedings of the 12th International Workshop on Adaptive and Reflective Middleware
A Scalable Distributed Framework for Efficient Analytics on Ordered Datasets
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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Applications in many areas are increasingly developed and ported using the Map Reduce framework (more specifically, Hadoop) to exploit (data) parallelism. The application scope of Map Reduce has been extended beyond the original design goal which was large-scale data processing. This extension inherently makes a need for scheduler to explicitly take into account characteristics of job for two main goals of efficient resource use and performance improvement. In this paper, we study Map Reduce scheduling strategies to effectively deal with different workload characteristicsCPU intensive and I/O intensive. We present the Workload Characteristic Oriented Scheduler (WCO), which strives for co-locating tasks of possibly different Map Reduce jobs with complementing resource usage characteristics. WCO is characterized by its essentially dynamic and adaptive scheduling decisions using information obtained from its characteristic estimator. Workload characteristics of tasks are primarily estimated by sampling with the help of some static task selection strategies, e.g., Java byte code analysis. Results obtained from extensive experiments using 11 benchmarks in a 4-node local cluster and a 51-node Amazon EC2 cluster show 17% performance improvement on average in terms of throughput in the situation of co-existing diverse workloads.