A heuristic method towards deadline-aware energy-efficient mapreduce scheduling problem in Hadoop YARN

The MapReduce (MR) scheduling is a prominent area of research to minimize energy consumption in the Hadoop framework in the era of green computing. Very few scheduling algorithms have been proposed in the literature which aim to optimize energy consumption. Moreover, most of them are only designed for the slot-based Hadoop framework, and hence, there is a need to address this issue exclusively for container-based Hadoop (known as Hadoop YARN). In this paper, we consider a deadline-aware energy-efficient MR scheduling problem in the Hadoop YARN framework. First, we model the considered scheduling problem as an integer program using the time-indexed binary decision variables. Thereafter, a heuristic method is designed to schedule map and reduce tasks on the heterogeneous cluster machines by taking advantage of the fact that tasks have different energy consumption values on different machines. Our heuristic method works in two phases, where each phase is composed of multiple similar rounds. We evaluate the proposed method for large-scale workloads of three standard benchmark jobs, namely, PageRank (CPU-bound), DFSIO (IO-bound), and NutchIndexing (mix-bound). The experimental results show that the proposed method considerably minimizes the energy consumption for all benchmarks against the custom-made makespan minimizing scheme which does not consider energy-saving criteria. We observe that energy-efficiency of the schedule generated by proposed heuristic stays within the 5% of the optimal solution. Apart from this, we also evaluate the proposed heuristic against delay scheduler (the default task-level scheduler in Hadoop YARN), and found it to be 35% more energy-efficient.