Cloud Computing—Task scheduling based on genetic algorithms

Abstract

Cloud Computing is a cutting edge technology for managing and delivering services over the Internet. Map-Reduce is the programming model used in cloud computing for processing large data sets in parallel over huge clusters. In order to increase efficiency, a good task scheduling is needed. Genetic algorithms are very useful and accurate in finding solutions to large scale optimization problems, such as task scheduling. They have gained immense popularity over last few years as a robust and easily adaptable search technique. Hadoop, the open source implementation of Map-Reduce, has several task schedulers available (FIFO, Fair, Capacity Schedulers), but neither one of them is focused on minimizing the global execution time. The goal of this project is to improve Hadoop's functionality by implementing a scheduler based on a genetic algorithm, solving the stated problem.