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Clouds which continue to garner interest from practitioners in industry and academia require effective energy aware resource managers to leverage processing power of underlying resources while minimizing energy consumption in global data centers. This thesis proposes several energy aware resource management techniques that can effectively perform matchmaking and scheduling of MapReduce jobs each of which is characterized by a Service Level Agreement (SLA) that includes a client specified earliest start time, execution time and a deadline with the objective of minimizing energy consumption. Techniques are proposed for both batch workloads and open systems subject to continuous job arrivals. Simulation based experimental results presented in this thesis demonstrate the effectiveness of the proposed energy aware resource management techniques compared to alternative resource management techniques that do not consider energy consumption in task allocation and scheduling decisions.

Energy Aware Resource Management for MapReduce Jobs