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Cloud-Based Advanced Shuffled Frog Leaping Algorithm for Tasks Scheduling.

Dipesh Kumar1, Nirupama Mandal1, Yugal Kumar2

  • 1Department of Electronics Engineering, Indian Institute of Technology (ISM), Dhanbad, India.

Big Data
|March 3, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced shuffled frog optimization algorithm for efficient cloud task scheduling. The new method effectively reduces makespan and average cost, outperforming existing techniques.

Keywords:
cloud computingdetection algorithmfrog optimization algorithmmulti-objective modeltask scheduling

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Area of Science:

  • Cloud Computing
  • Optimization Algorithms
  • Task Scheduling

Background:

  • Exponential growth in online activities increases data load on cloud servers.
  • Efficient task scheduling is crucial for maintaining cloud application performance.
  • Existing scheduling algorithms face challenges in optimizing makespan and cost.

Purpose of the Study:

  • To propose an advanced shuffled frog optimization algorithm for cloud task scheduling.
  • To enhance the efficiency of assigning tasks to virtual machines (VMs).
  • To reduce both makespan time and average cost in cloud environments.

Main Methods:

  • Developed a novel shuffled frog optimization algorithm inspired by frog foraging behavior.
  • Introduced a new method for shuffling frog positions within a memeplex.
  • Calculated CPU cost, makespan, and a combined fitness function (budget cost + makespan).

Main Results:

  • The proposed algorithm effectively schedules tasks to virtual machines.
  • Achieved a makespan of 6, average cost of 4, and fitness of 10.
  • Demonstrated superior performance compared to W-Scheduler, SPSO-SA, and SLPSO-SA.

Conclusions:

  • The advanced shuffled frog optimization algorithm offers more effective task scheduling.
  • This method significantly reduces makespan and average cost in cloud computing.
  • The proposed technique provides a promising solution for optimizing cloud resource allocation.