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Related Experiment Videos

Fuzzy adaptive human memory optimization based optimal task scheduling in cloud computing.

Padmaja Patel1,2, K Murali Gopal1, Nimai Charan Patel3

  • 1Department of Computer Science and Engineering, GIET University, Gunupur, Odisha, 765022, India.

Scientific Reports
|May 7, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces Human Memory Optimization (HMO) and Fuzzy Adaptive Human Memory Optimization (FAHMO) for efficient cloud task scheduling. These methods significantly improve performance by reducing task completion time and makespan compared to traditional algorithms.

Keywords:
Cloud computingCloudletFuzzy adaptive human memory optimizationHuman memory optimizationMakespanMetaheuristic algorithmTask scheduling

Related Experiment Videos

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cloud Computing

Background:

  • Efficient resource utilization is critical in cloud computing.
  • Task scheduling is a key area of research for optimizing cloud resources.

Purpose of the Study:

  • To introduce and evaluate Human Memory Optimization (HMO) and Fuzzy Adaptive Human Memory Optimization (FAHMO) for cloud task scheduling.
  • To compare the performance of HMO and FAHMO against conventional metaheuristic algorithms.

Main Methods:

  • Harnessing human cognitive principles for adaptive search strategies.
  • Employing a history of scheduling decisions for continuous enhancement (HMO).
  • Integrating fuzzy logic to manage uncertainty and ambiguity in decision-making (FAHMO).

Main Results:

  • HMO and FAHMO demonstrate superior convergence speed and task completion times.
  • FAHMO achieved up to 67.46% improvement in makespan.
  • FAHMO showed a 63.18% improvement in convergence accuracy compared to PSO-PGA.

Conclusions:

  • The proposed HMO and FAHMO approaches significantly reduce makespan.
  • These methods enhance overall cloud task scheduling performance and efficiency.
  • FAHMO offers a more flexible and robust solution for complex scheduling problems.