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Optimize Task Allocation in Cloud Environment Based on Big-Bang Big-Crunch

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Abstract

Efficient resource allocation is indispensable in the current scenario of a service-oriented computing paradigm. Instance allocation to the host and the task allocation to the instance depends on the efficiency of scheduling technique. In this work, we exhibit the provisioning of tasks or cloudlets on a virtual machine. The Big-Bang Big-Crunch-cost model is proposed for efficient resource allocation. The proposed technique supports the principle of optimization method and performance is measured using makespan and resource cost. Our proposed cost-aware Big-Bang- Big-Crunch model, provides an optimal solution using the IaaS (Infrastructure as a service) model. It supports dynamic and independent task allocation on virtual machines. The proposed technique proclaims an evolution scheme that measures an objective function depends on performance metrics cost and time respectively. The input dataset defines the number of host nodes and datacenter configuration. The learning, evolution-based on BB-BC cost-aware method provides a globally optimal solution in a dynamic resource provisioning environment. Our approach effectively finds optimal simulation results than existing static, dynamic, and bio-inspired evolutionary provisioning techniques. Simulation results are exhibited that the cost-aware Big-Bang Big-Crunch method illustrates an adequate schedule of tasks on respective virtual machines. Reliability is measured using the operational cost of the resources in execution duration. Efficient resource utilization and the global optimum solution depends on the fitness function. The simulation results illustrate that our cost-aware astrology based soft computing methodology provides better results than time aware and cost-aware scheduling approaches. From simulation results, it is observed that Big-Bang Big-Crunch Cost aware proposed methodology improves average finish time by 15.23% with user requests 300, and average finish time improves by 19.18% with population size 400. The performance metric average resource cost enhancement by 30.46% with population size 400. The infrastructure cloud is considered for the performance measurement of the proposed cost-aware model which is constituted using static, dynamic, and meta-heuristic bio-inspired resource allocation techniques.

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  • 30 December 2020

    The original version of this article has been revised: An incorrect ORCID has been removed.

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Acknowledgements

The authors would like to thanks the reviewers and the associate editor for providing constructive and generous reviews. We are thankful to our University staff who helped us to complete this work. We are thankful for the researchers who provide the cloud simulation tools, e.g., Cloudsim 3.0, CloudAnalyst for modeling and simulation of the extensible scalable cloud environment.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Uttarakhand Technical University, Dehradun, 248007, India

    Pradeep Singh Rawat

  2. Department of Computer Science & Applications, GBPIET, Pauri Garhwal, 246194, India

    Priti Dimri

  3. Department of Computer Science and Engineering, DIT University, Dehradun, 248009, India

    Soumen Kanrar

  4. Department of Computer Science, Vidyasagar University, Midnapore, West Bengal, 721102, India

    Soumen Kanrar

  5. University Computer Center, Maharshi Dayanand University, Rohtak, Haryana, 124001, India

    Gyanendra Pal Saroha

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  1. Pradeep Singh Rawat
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Correspondence to Pradeep Singh Rawat.

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Rawat, P.S., Dimri, P., Kanrar, S. et al. Optimize Task Allocation in Cloud Environment Based on Big-Bang Big-Crunch. Wireless Pers Commun 115, 1711–1754 (2020). https://doi.org/10.1007/s11277-020-07651-1

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