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Scheduling plays a key role in MapReduce systems. In this paper, we explore the efficiency of an MapReduce cluster running lots of independent and continuously arriving MapReduce jobs. Data locality and load balancing are two important factors to improve computation effi-ciency in MapReduce systems for data-intensive computations. Traditional cluster scheduling technologies are not well suitable for MapReduce en-vironment, there are some in-used schedulers for the popular open-source Hadoop MapReduce implementation, however, they can not well optimize both factors. Our main objective is to minimize total flowtime of all jobs, given it's a strong NP-hard problem, we adopt some effective heuristics to seek satisfied solution. In this paper, we formalize the scheduling prob-lem as job selection problem, a load balance aware job selection algorithm is proposed, in task level we design a strict data locality tasks schedul-ing algorithm for map tasks on map machines and a load balance aware scheduling algorithm for reduce tasks on reduce machines. Comprehen-sive experiments have been conducted to compare our scheduling strategy with well-known Hadoop scheduling strategies. The experimental results validate the efficiency of our proposed scheduling strategy. © 2013 The Institute of Electronics, Information and Communication Engineers.

An Efficiency-Aware Scheduling for Data-Intensive Computations on MapReduce Clusters