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Hybrid gradient descent cuckoo search (HGDCS) algorithm for resource scheduling in IaaS cloud computing environment

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Abstract

Resource scheduling is a procedure for the distribution of resources over time to perform a required task and a decision making process in cloud computing. Optimal resource scheduling is a great challenge and considered to be an NP-hard problem due to the fluctuating demand of cloud users and dynamic nature of resources. In this paper, we formulate a new hybrid gradient descent cuckoo search (HGDCS) algorithm based on gradient descent (GD) approach and cuckoo search (CS) algorithm for optimizing and resolving the problems related to resource scheduling in Infrastructure as a Service (IaaS) cloud computing. This work compares the makespan, throughput, load balancing and performance improvement rate of existing meta-heuristic algorithms with proposed HGDCS algorithm applicable for cloud computing. In comparison with existing meta-heuristic algorithms, proposed HGDCS algorithm performs well for almost in both cases (Case-I and Case-II) with all selected datasets and workload archives. HGDCS algorithm is comparatively and statistically more effective than ACO, ABC, GA, LCA, PSO, SA and original CS algorithms in term of problem solving ability in accordance with results obtained from simulation and statistical analysis.

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Authors and Affiliations

  1. Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Syed Hamid Hussain Madni & Muhammad Shafie Abd Latiff

  2. Department of Cyber Security Science, Federal University of Technology Minna, Minna, Niger State, Nigeria

    Shafi’i Muhammad Abdulhamid

  3. College of Computing Informatics, Saudi Electronic University, Madinah Munawarah, Kingdom of Saudi Arabia

    Javed Ali

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  1. Syed Hamid Hussain Madni
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  2. Muhammad Shafie Abd Latiff
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Madni, S.H.H., Abd Latiff, M.S., Abdulhamid, S.M. et al. Hybrid gradient descent cuckoo search (HGDCS) algorithm for resource scheduling in IaaS cloud computing environment. Cluster Comput 22, 301–334 (2019). https://doi.org/10.1007/s10586-018-2856-x

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