A MapReduce task scheduling algorithm for deadline constraints

  • Authors:

  • Zhuo Tang;Junqing Zhou;Kenli Li;Ruixuan Li

  • Affiliations:

  • School of Information Science and Engineering, Hunan University, Changsha, China 410082;School of Information Science and Engineering, Hunan University, Changsha, China 410082;School of Information Science and Engineering, Hunan University, Changsha, China 410082;School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China 430074

  • Venue:
  • Cluster Computing
  • Year:
  • 2013

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Abstract

The current works about MapReduce task scheduling with deadline constraints neither take the differences of Map and Reduce task, nor the cluster's heterogeneity into account. This paper proposes an extensional MapReduce Task Scheduling algorithm for Deadline constraints in Hadoop platform: MTSD. It allows user specify a job's deadline and tries to make the job be finished before the deadline. Through measuring the node's computing capacity, a node classification algorithm is proposed in MTSD. This algorithm classifies the nodes into several levels in heterogeneous clusters. Under this algorithm, we firstly illuminate a novel data distribution model which distributes data according to the node's capacity level respectively. The experiments show that the node classification algorithm can improved data locality observably to compare with default scheduler and it also can improve other scheduler's locality. Secondly, we calculate the task's average completion time which is based on the node level. It improves the precision of task's remaining time evaluation. Finally, MTSD provides a mechanism to decide which job's task should be scheduled by calculating the Map and Reduce task slot requirements.