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Locust Inspired Algorithm for Cloudlet Scheduling in Cloud Computing Environments.

Mohammed Alaa Ala'anzy1, Mohamed Othman1,2, Zurina Mohd Hanapi1

  • 1Department of Communication Technology and Networks, Universiti Putra Malaysia, Serdang 43400, Malaysia.

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Summary
This summary is machine-generated.

This study introduces a novel locust-inspired algorithm for efficient cloudlet scheduling. The proposed method optimizes task allocation, reducing average makespan and waiting times while enhancing resource utilization in cloud environments.

Keywords:
bio-inspiredcloud computingcloudlet schedulingmakespanresource utilisationtask allocationwaiting time

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Algorithm Design

Background:

  • Cloud computing offers flexible services but faces challenges in scheduling user tasks (cloudlets) due to numerous users and resources.
  • Efficient cloudlet scheduling is crucial for optimizing execution times, resource utilization, and waiting times, especially with increasing task complexity.
  • Cloudlet scheduling is an NP-complete problem often addressed by meta-heuristic algorithms.

Purpose of the Study:

  • To propose an efficient cloudlet scheduling algorithm inspired by locust behavior.
  • To reduce average makespan and waiting time for cloudlet execution.
  • To improve virtual machine (VM) and server utilization in cloud data centers.

Main Methods:

  • Development of a locust-inspired algorithm for cloudlet scheduling.
  • Simulation of the proposed algorithm using the CloudSim toolkit.
  • Comparison of the algorithm's performance against existing nature-inspired algorithms.

Main Results:

  • The locust-inspired algorithm significantly reduces average makespan.
  • The proposed algorithm leads to a notable decrease in average waiting time.
  • Enhanced utilization of VMs and servers was observed, outperforming other state-of-the-art algorithms.

Conclusions:

  • The locust-inspired algorithm presents an effective solution for cloudlet scheduling challenges.
  • This approach offers improved performance metrics compared to existing methods.
  • The algorithm contributes to more efficient resource management in cloud computing environments.