Multiobjective Task Scheduling for Energy-Efficient Cloud Implementation of Hyperspectral Image Classification

Cloud computing has become a promising solution to efficient processing of remotely sensed big data, due to its high-performance and scalable computing capabilities. However, existing cloud solutions generally involve the problems of low resource utilization and high energy consumption when processing large-scale remote sensing datasets, affecting the quality-of-service of the cloud system. Aiming at hyperspectral image classification applications, this article proposes an energy-efficient cloud implementation by employing a multiobjective task scheduling algorithm. We first present a parallel computing mechanism for a fusion-based classification method based on Apache Spark. With the general classification flow represented by a workflow model, we formulate a multiobjective scheduling framework that jointly minimizes the total execution time as well as energy consumption. We further develop an effective scheduling algorithm to solve the multiobjective optimization problem and produce a set of Pareto-optimal solutions, providing the tradeoff between computational efficiency and energy efficiency. Experimental results demonstrate that the multiobjective scheduling approach proposed in this work can substantially reduce the execution time and energy consumption for performing large-scale hyperspectral image classification on Spark. In addition, our proposed algorithm can generate better tradeoff solutions to the multiobjective scheduling problem as compared to competing scheduling algorithms.