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

Zavieh, H ; Sharif University of Technology | 2023

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  1. Type of Document: Article
  2. DOI: 10.1007/s10586-022-03796-9
  3. Publisher: Springer , 2023
  4. Abstract:
  5. 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. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature
  6. Keywords:
  7. Cloud computing ; Cuckoo algorithm ; Fault tolerance ; PSO ; Task processing ; Virtual machine migration
  8. Source: Cluster Computing ; Volume 26, Issue 1 , 2023 , Pages 745-769 ; 13867857 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s10586-022-03796-9