IoTDeM: An IoT Big Data-oriented MapReduce performance prediction extended model in multiple edge clouds

Uploading all IoT 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 IoT 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 paper, we propose and evaluate IoTDeM, which is an extended IoT Big Data-oriented model for predicting MapReduce performance in multiple edge clouds. IoTDeM is able to predict MapReduce jobs' total execution time in a general implementation scenario with varying reduce amounts and cluster scales in Hadoop 2, rather than Hadoop 1. The extended model is based on historical job execution records and Locally Weighted Linear Regression (LWLR) techniques to predict the execution time of each job. Through extracting more representative features to represent a job, the IoTDeM model selects a cluster scale as a crucial parameter to further extend LWLR model. In the environment of Hadoop 2 with Ceph as the storage system, the experiments verify IoTDeM can effectively predict the total execution time of MapReduce applications, with the average relative error of less than 10%.