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Task processing optimization using cuckoo particle swarm (CPS) algorithm in cloud computing infrastructure

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Abstract

Recently, cloud computing infrastructure (CCI) models have received much attention for their exceptional scalability, dependability, Data Information Sharing (DIS), and low cost rate. There are many hardware and software elements that are accessed over the internet by cloud data centers. Modern data centers utilize Virtualization Techniques (VT) to offer a dispersed CI that employs Virtual Machines (VM) based on Physical Hosts (PH). With the increasing number of centers, optimizing energy consumption has become vital to saving costs due to DCC's high energy consumption. In our CPS algorithm, we combine the Cuckoo algorithm and the particle swarm optimization (PSO). It is determined which virtual machine can be assigned to each host, thus choosing the best virtual machine. As a result, if the selected host is overloaded, it is determined which virtual machines are generating high loads and migrated to another host, which is determined based on the cuckoo algorithm and PSO. In testing each algorithm separately, the combination method proved to consume less energy and execute faster than the other methods in the CloudSim simulation environment. Fault tolerance for our network and evaluation of VMs have also been emphasized in vSphereTM.

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Data availability

Data available on request from the authors, data that support the findings of this study are available from the corresponding author, upon reasonable request.

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Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. School of Economics and Statistics, Guangzhou University, Guangzhou, 510006, China

    Hadi Zavieh & Yuan Li

  2. Department of Computer Science and Technology (Cyberspace Security), Harbin Institute of Technology, Shenzhen, China

    Amir Javadpour

  3. ADiT-Lab, Electrotechnics and Telecommunications Department, Instituto Politécnico de Viana do Castelo, Porto, Portugal

    Amir Javadpour

  4. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Forough Ja’fari

  5. Department of Mathematics, University of Mazandaran, Babolsar, 48175-866, Iran

    Seyed Hadi Nasseri

  6. Foshan University, Foshan, China

    Seyed Hadi Nasseri

  7. Sharif University of Technology, Tehran, Iran

    Seyed Hadi Nasseri

  8. Department of Communication Engineering, University of Sistan and Baluchestan, Zahedan, Iran

    Ali Shokouhi Rostami

Authors
  1. Hadi Zavieh
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  2. Amir Javadpour
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Contributions

HZ, AJ: Conceived and designed the analysis, collected the data, contributed data or analysis tools, performed the analysis, wrote the paper, other contribution, conceived and designed the analysis. YL, FJ, SHN, ASR: collected the data, contributed data or analysis tools, wrote the paper.

Corresponding authors

Correspondence to Amir Javadpour or Yuan Li.

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Zavieh, H., Javadpour, A., Li, Y. et al. Task processing optimization using cuckoo particle swarm (CPS) algorithm in cloud computing infrastructure. Cluster Comput 26, 745–769 (2023). https://doi.org/10.1007/s10586-022-03796-9

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  • DOI: https://doi.org/10.1007/s10586-022-03796-9

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