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Improvement for tasks allocation system in VM for cloud datacenter using modified bat algorithm.

Arif Ullah1, Aziza Chakir2

  • 1Department of Computing, Riphah International University, Faisalabad, Punjab 44000 Faisalabad, Pakistan.

Multimedia Tools and Applications
|April 11, 2022
PubMed
Summary
This summary is machine-generated.

This study enhances cloud computing task scheduling using a modified Bat algorithm for improved load balancing in virtual machines (VMs). The optimized algorithm boosts data center efficiency and accuracy.

Keywords:
Cloud computingDatacenterTask allocationVirtual machine

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Artificial Intelligence

Background:

  • Cloud computing has become essential for global connectivity and business operations, accelerated by the COVID-19 pandemic.
  • Virtual machines (VMs) are fundamental to cloud virtualization, requiring efficient task scheduling for optimal performance.
  • Effective load balancing is crucial for managing VM resources and ensuring efficient task distribution in cloud environments.

Purpose of the Study:

  • To improve the task distribution system in virtual machines (VMs) for cloud computing.
  • To enhance load balancing techniques within cloud data centers.
  • To optimize the performance of cloud-based task scheduling.

Main Methods:

  • Modification of the Bat algorithm's fitness function for load balancing.
  • Adjustments to the Bat algorithm's search process within its dimensions.
  • Implementation of a modified Bat algorithm for task scheduling in virtual machines.

Main Results:

  • The modified Bat algorithm demonstrated improved efficiency in task distribution.
  • Performance was evaluated using metrics including throughput, makespan, degree of imbalance, and processing time.
  • The proposed algorithm showed superior results compared to standard techniques.

Conclusions:

  • The modified Bat algorithm effectively enhances task scheduling and load balancing in cloud environments.
  • The proposed method leads to increased accuracy and efficiency in cloud data centers.
  • This research contributes to more robust and performant cloud computing infrastructures.