K%-Fair scheduling: A flexible task scheduling strategy for balancing fairness and efficiency in MapReduce systems

MapReduce is an important programming paradigm on big data-intensive computing using share-nothing cluster containing ten of thousands of nodes, in which computing nodes also acts as storage nodes. Since tasks belonging to different jobs are physical executing entities scattered among the whole cluster, task scheduling plays a crucial role in MapReduce systems. For data consolidation and utilization, MapReduce cluster is usually used as an shared computing environment rather than several private clusters. Typical workloads consist of concurrent jobs, which include interactive jobs and batch jobs, so fairness is an important target in this scenario. On the other hand, efficiency is also an vital concern for cluster owner, data locality is used as a heuristic to achieve high efficiency. To achieve both goals is a huge challenge, requiring extensive research work. State of the art schedulers cannot well solve this problem. In this paper, we proposed K%-Fair scheduling, a flexible task scheduling strategy, based on multiple task queues on node level, according to fairness and data locality. Finally, we evaluate our scheduling on data locality and fairness among jobs, it improves data locality much more, in the same time, fairness is kept on nearly the same.