Migrating Big Data to Cloud a Hybrid Algorithm for Solving the Cost Optimization Problem

Authors

  • Amitkumar Manekar , Dr. Pradeepini Gera

Abstract

Effective Processing of massive, intensive large scale unstructured data on modern BDA
(Extensive Data Processing) application facilitate decision making, production scale and so on. Cloud
Computing is promising and trusted solution for enterprises and institute with demand on Big Data
processing by submitting a request to cloud service provider. For CSP (Cloud Service provider) and Cloud
User keeping cost and maximizing profit is a challenge. There are four challenges particularly first is The
service request in cloud are highly dynamic and unpredictable, second is The electricity and spare capacity
price, third is enormous amount of computing resources with optimization problem, and last is energy
efficiency. Researchers were actively developing many novel solutions for addressing the challenges on
resource allocation in big data. Therefore, this paper aims to introduce a new resource allocation model
under spark. This model insists the utilization of optimization concept for resource allocation and migration
by considering the parameters like Deadline, utilization cost and migration cost. For this, the concepts of
Dragonfly (DA) and Sea Lion Algorithm (SLnO) are hybridized and a new algorithm termed as Combined
DA and SLnO (CD-SLnO) is introduced. At the end, it validates the betterment of the presented scheme in
terms of certain performance measures.

Published

2020-04-30

Issue

Section

Articles