AEGEUS++: an energy-aware online partition skew mitigation algorithm for mapreduce in cloud

This paper investigates the partition skew problem at reduce phase in the mapreduce jobs. Our study summarize the skew problem in both offline and online manner. Offline is a heuristics based approach waits for the completion of map tasks and it involves computation overhead to estimate the partition size. In online approach, the overloaded tasks are distributed across other nodes that needs extra split and merge operations. These extra operations and ineffective utilization of resources in turn hamper the performance of the entire system. In this paper, we propose Aegeus++, to address the skew mitigation and adaptive data sampling problems for mapreduce jobs which enables to build an online prediction model with improved accuracy in minimal waiting time. In addition, we propose near linear skew detection and fine-grained Resource Allocation algorithms for identifying the skewed partition and allocating appropriate resources to reducers based on the partition size. Finally, our energy-aware opportunistic frequency tuning algorithm improves the performance of the reducer container on-fly, that can process the skewed data faster with minimal energy consumption. We evaluated Aegeus++ in the cloud setup by using benchmark datasets, compared its performance with native Hadoop and its other approaches. Based on our observation, Aegeus++ outperforms native Hadoop by 44% by maximizing its overall performance of the application and decreases the energy consumption by 37.67% when compared with existing approaches.