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Prioritized Task-Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization.

Sudheer Mangalampalli1, Sangram Keshari Swain2, Tulika Chakrabarti3

  • 1School of Computer Science and Engineering, VIT-AP University, Amarvati 522237, Andhra Pradesh, India.

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Summary
This summary is machine-generated.

This study introduces a novel cloud task-scheduling algorithm using cat swarm optimization to minimize makespan, energy use, and SLA violations. The new method effectively prioritizes tasks for better cloud resource management and service quality.

Keywords:
OpenStackSLA violationcloud computingenergy consumptionmakespantask scheduling

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Area of Science:

  • Cloud computing
  • Resource management
  • Algorithm optimization

Background:

  • Ineffective cloud resource scheduling impacts service quality, increases energy consumption, and prolongs task completion times (makespan).
  • Minimizing Service Level Agreement (SLA) violations is crucial for cloud environments, affecting makespan, energy use, and overall quality of service.
  • Existing scheduling algorithms offer near-optimal solutions but often overlook task priorities and VM suitability.

Purpose of the Study:

  • To develop a novel task-scheduling algorithm for cloud platforms that prioritizes tasks based on calculated VM priorities.
  • To enhance cloud service quality by minimizing makespan, energy consumption, and SLA violations.
  • To leverage the cat swarm optimization algorithm for efficient cloud task scheduling.

Main Methods:

  • Developed a novel task-scheduling algorithm incorporating task priorities and calculated VM priorities.
  • Modeled the scheduling algorithm using the cat swarm optimization (CSO) algorithm, inspired by feline behavior.
  • Implemented and tested the algorithm on CloudSim and OpenStack platforms using real-time workloads.

Main Results:

  • The proposed cat swarm optimization-based scheduling algorithm demonstrated superior performance compared to baseline algorithms (PSO, ACO, RATS-HM).
  • Significant improvements were observed in minimizing makespan, reducing energy consumption, and decreasing SLA violations.
  • The algorithm effectively matched tasks to appropriate virtual machines (VMs) based on calculated priorities.

Conclusions:

  • The novel task-scheduling algorithm using cat swarm optimization offers a more effective approach to cloud resource management.
  • The proposed method successfully addresses key performance indicators: makespan, energy consumption, and SLA violations.
  • This research provides a valuable contribution to optimizing cloud scheduling for improved efficiency and service quality.