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Optimizing load scheduling and data distribution in heterogeneous cloud environments using fuzzy-logic based

Bei Cheng1, Dongmei Li2, Xiaojun Zhu2

  • 1Research Center for Educational Evaluation and Inspection, China National Academy of Educational Sciences (CNAES), Beijing, China.

Plos One
|December 13, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a Two-level Scheduling and Distribution Framework (TSDF) using Fuzzy Logic (FL) to optimize cloud resource allocation. The framework reduces wait times and improves data distribution by efficiently handling unattended requests.

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Area of Science:

  • Cloud Computing
  • Artificial Intelligence
  • Operations Research

Background:

  • Cloud environments face challenges with heterogeneous resources and user demands, leading to suboptimal load scheduling and data distribution.
  • Unattended requests in cloud systems can cause prolonged delays, impacting overall system performance and user experience.
  • Existing resource scheduling strategies struggle to effectively manage heterogeneity and dynamic request patterns.

Purpose of the Study:

  • To propose and evaluate a novel Two-level Scheduling and Distribution Framework (TSDF) for optimizing cloud resource management.
  • To address the issue of prolonged delays caused by unattended requests in heterogeneous cloud environments.
  • To enhance load balancing and data distribution efficiency using Fuzzy Logic (FL).

Main Methods:

  • Development of a Two-level Scheduling and Distribution Framework (TSDF) incorporating Fuzzy Logic (FL).
  • Implementation of a two-level fuzzification process: first, distinguishing regular from paused requests to manage delays, and second, combining low and high delay distributions for joint resource allocation.
  • Time-based scheduling prioritizing minimum time-varying requests and optimizing cumulative response delay.

Main Results:

  • The proposed TSDF framework successfully reduces wait times by 7.8%.
  • Data distribution efficiency is improved by 13.07% through optimized allocation intervals.
  • The framework effectively manages heterogeneity and prevents prolonged delays caused by unattended requests.

Conclusions:

  • The TSDF framework offers an effective solution for optimizing resource scheduling and data distribution in heterogeneous cloud environments.
  • Fuzzy Logic integration enables intelligent handling of request prioritization and resource allocation, leading to significant performance improvements.
  • The study demonstrates the potential of TSDF to enhance cloud system efficiency and user satisfaction by minimizing delays and improving resource utilization.