Optimize Task Scheduling and Resource Allocation Using Nature Inspired Algorithms in Cloud based BDA

Abstract

Task Scheduling and Resource allocation is a prominent research topic in cloud computing. There are several objectives associated with Optimize Task Scheduling and Resource allocation as cloud computing systems are more complex than the traditional distributed system. There are several challenges like resolving the task mapped to the node on which task to be executed. A simplified but near optimal proposed nature inspired algorithms are focus in this paper. In this paper basic idea about optimization, reliability and complexity is considered while design a solution for modern BDA (Big Data Application). Detailed analysis of experimental results, it is shown that the proposed algorithm has better optimization effect on the fair share policies which are presently available in most of the BDA. In this paper we focused on Dragonfly algorithm and Sea lion algorithms which are nature inspired algorithms. These algorithms are efficient for optimization purpose for solving task scheduling and resource allocation problem. Finally performance of the hybrid DA algorithm and Sea lion is compared with traditional techniques used for modern BDA using Hadoop MapReduce. Simulation results prove the efficacy of the suggested algorithms.