Multi-objective Big Data Optimization with jMetal and Spark

Big Data Optimization is the term used to refer to optimization problems which have to manage very large amounts of data. In this paper, we focus on the parallelization of metaheuristics with the Apache Spark cluster computing system for solving multi-objective Big Data Optimization problems. Our purpose is to study the influence of accessing data stored in the Hadoop File System (HDFS) in each evaluation step of a metaheuristic and to provide a software tool to solve these kinds of problems. This tool combines the jMetal multi-objective optimization framework with Apache Spark. We have carried out experiments to measure the performance of the proposed parallel infrastructure in an environment based on virtual machines in a local cluster comprising up to 100 cores. We obtained interesting results for computational effort and propose guidelines to face multi-objective Big Data Optimization problems.