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With the rapid proliferation of big data, real-time processing of huge datasets becomes a challenging task; primarily because of their heterogeneous nature. Due to this, one of the most serious concerns of the modern cloud data centers is massive energy consumption during job execution. Hence, energy-aware task scheduling with data placement are considered as two important parameters for enhanced energy efficiency of modern cloud data centers. Moreover, considering the ``pay-per-use" model of cloud computing infrastructure, it is important to maintain desirable service level agreement (SLA) while attaining improved data locality. Poor task scheduling decisions with limited focus of data locality are the prime reasons for escalated data communications and energy utilization levels. In order to deal with the aforementioned issues, data locality- aware energy-efficient (EnLoc) scheme for task scheduling and data placement has been proposed, particularly for MapReduce framework. The proposed EnLoc scheme is a multi-objective optimization problem (MOOP) and is solved using multi-objective evolutionary algorithm with ``Tchebycheff decomposition"; wherein the formulated MOOP is decomposed into theoretically finite number of subproblems to get optimal scheduling and placement decisions. The proposed scheme has been evaluated on real-time data traces acquired from OpenCloud Hadoop Cluster. The results obtained clearly demonstrate that the proposed EnLoc scheme outperforms the existing schemes in terms of energy efficiency, SLA assurance, and data locality.
Kaur et al. (Tue,) studied this question.