Data-locality-aware mapreduce real-time scheduling framework

MapReduce is widely used in cloud applications for large-scale data processing. The increasing number of interactive cloud applications has led to an increasing need for MapReduce real-time scheduling. Most MapReduce applications are data-oriented and nonpreemptively executed. Therefore, the problem of MapReduce real-time scheduling is complicated because of the trade-off between run-time blocking for nonpreemptive execution and data-locality. This paper proposes a data-locality-aware MapReduce real-time scheduling framework for guaranteeing quality of service for interactive MapReduce applications. A scheduler and dispatcher that can be used for scheduling two-phase MapReduce jobs and for assigning jobs to computing resources are presented, and the dispatcher enable the consideration of blocking and data-locality. Furthermore, dynamic power management for run-time energy saving is discussed. Finally, the proposed methodology is evaluated by considering synthetic workloads, and a comparative study of different scheduling algorithms is conducted. (C) 2015 Elsevier Inc. All rights reserved.