InSTechEM: An Internet of Thing big data–oriented extended model for MapReduce performance prediction in multiple edge clouds

Uploading all Internet of Things big data to a centralized cloud for data analytics is infeasible because of the excessive latency and bandwidth limitation of the Internet. A promising approach to addressing the challenges for data analytics in Internet of Things is “edge cloud†that pushes various computing and data analysis capabilities to multiple edge clouds. MapReduce provides an efficient way to deal with a large amount of data. When performing data analysis, a challenge is to predict the performance of MapReduce jobs. In this article, we propose and evaluate InSTechEM, which is an extended Internet of Things big data–oriented model for predicting MapReduce performance in multiple edge clouds. InSTechEM is able to predict MapReduce jobs’ total execution time in a general implementation scenario with varying reduce amounts and cluster scales. The proposed model is built based on historical job execution records and employs locally weighted linear regression techniques to predict the execution time of each job. By modifying the prediction model used in Hadoop 1 and extracting more representative features to represent a job, the InSTechEM model can effectively predict the total execution time of MapReduce applications with the average relative error of less than 10% in Hadoop 2 with Ceph as the storage system.