Towards Improving MapReduce Task Scheduling Using Online Simulation Based Predictions

Abstract

MapReduce is the model of choice for processing emerging big-data applications, and is facing an ever increasing demand for higher efficiency. In this context, we propose a novel task scheduling scheme that uses current task and system state information to drive online simulations concurrently within Hadoop, and predict with high accuracy future events, e.g., when a job would complete, or when task-specific data-local nodes would be available. These predictions can then be used to make more efficient resource scheduling decisions. Our framework consists of two components: (i) Task Predictor that predicts task-level execution times based on historical data of the same type of tasks, and (ii) Job Simulator that instantiates the real task scheduler in a simulated environment, and predicts expected scheduling decisions for all the tasks comprising a MapReduce job. Evaluation shows that our framework can achieve high prediction accuracy - 95% of the predicted task execution times are within 10% of the actual times - with negligible overhead (1.29%). Finally, we also present two realistic use cases, job data prefetching and a multi-strategy dynamic scheduler, which can benefit from integration of our prediction framework in Hadoop.