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Optimal Scheduling of Tasks in Cloud Computing Using Hybrid Firefly-Genetic Algorithm

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Book cover Advances in Decision Sciences, Image Processing, Security and Computer Vision (ICETE 2019)

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 4))

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Abstract

Today cloud computing is an evolved form of utility computing which is widely used for commercial computing needs. The Cloud service provider’s success, profit, and efficiency lie in optimally allocating the computing resources to users from a vast pool of resources. The ability to allocate resources in a ubiquitous, seamless and on-demand connection involves serious challenges. Task scheduling is a variant of job-shop scheduling problem which is categorized as NP-COMPLETE. In this paper, a novel meta-heuristic algorithm of hybrid Firefly-Genetic combination is propounded for scheduling tasks. The proposed algorithm blends benefits of a mathematical optimization algorithm like Firefly with an evolutionary algorithm like Genetic algorithm to form a powerful metaheuristic search algorithm. The proposed hybrid Firefly-Genetic algorithm was able to schedule the tasks with the objective of minimal execution time for all tasks and a swift convergence to the near optimal solution. The proposed algorithm was tested in CloudSim which is a simulator toolkit for testing cloud-based algorithms. The experimental results showed that the proposed algorithm outweighed the performances of traditional First In First Out (FIFO) and Genetic algorithms.

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Author information

Authors and Affiliations

  1. Department of Information Technology, Madras Institute of Technology Campus, Anna University, Chennai, India

    Aravind Rajagopalan, Devesh R. Modale & Radha Senthilkumar

Authors
  1. Aravind Rajagopalan
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  2. Devesh R. Modale
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  3. Radha Senthilkumar
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Corresponding author

Correspondence to Aravind Rajagopalan .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  2. Department of CSE, CMR Technical Campus, Hyderabad, Telangana, India

    K. Srujan Raju

  3. Department of CSE, Osmania University, University College of Engineering, Hyderabad, Telangana, India

    K. Shyamala

  4. Department of ECE, Osmania University, University College of Engineering, Hyderabad, Telangana, India

    D. Rama Krishna

  5. Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Margarita N. Favorskaya

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Rajagopalan, A., Modale, D.R., Senthilkumar, R. (2020). Optimal Scheduling of Tasks in Cloud Computing Using Hybrid Firefly-Genetic Algorithm. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-030-24318-0_77

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