Efficient Utilization of IaaS Cloud using Adaptive Evolution Based Technique
Pradeep Singh Rawat1, Punit Gupta2
1Pradeep Singh Rawat, Department of Computer Science and Engineering, DIT University, Dehradun (Uttarakhand), India.
2Punit Gupta, Department of Computer and Communication Engineering, Manipal University Jaipur (Rajasthan), India.
Manuscript received on 09 October 2019 | Revised Manuscript received on 23 October 2019 | Manuscript Published on 26 December 2019 | PP: 677-691 | Volume-8 Issue-12S October 2019 | Retrieval Number: L116410812S19/2019©BEIESP | DOI: 10.35940/ijitee.L1164.10812S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the present era of technology and innovation, computing provides as a service. The service-oriented computing paradigm is a subset of utility and distributed computing paradigm. Hardware specification, platform, and application provide a service to the users in a different time zone. Resource utilization and request completion time is the prime focus of the service providers and consumers. In this work, we proposed a nature-inspired evolution and astrology science-based approach. The Big-Bang Big-Crunch optimization method based task allocation technique focuses on efficient resource utilization of IaaS (Infrastructure as a Service) cloud. We focused on three performance metrics, which may be user-oriented and service provider oriented. Performance metrics measure in terms of average resource utilization cost. Average Finish time, an average start time, and operational cost (customer-oriented) performance metrics are consumer-centric. The simulation performs using five cloud resources, six cloud configurations, and user-defined population size, and number iterations. The selection operator includes a fitness function that depends on estimated resource cost in the duration of execution. BB-BC cost-aware model provides optimal resource utilization, and minimum average finish time, and minimum average start time (ms) of cloudlets. The results exhibit that the proposed astrology, evolution based meta-heuristic approach outperforms the static (first come first serve (FCFS)), dynamic, and meta-heuristic (Genetic cost-aware, Genetic execution time aware and Particle Swarm optimization) approaches.
Keywords: BB-BC (Big-Bang Big-Crunch), Cloudsim, FCFS, GA-Cost, GA-Exe, IaaS, PaaS, PSO, SaaS, Simulation.
Scope of the Article: Adaptive Systems