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Related Experiment Video

Updated: Jul 4, 2026

Optimization of An Air-Based Heat Management System for Dusty Particulate Matter-Covered Lithium-Ion Battery Packs
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Published on: November 3, 2023

Research on hybrid cloud resource scheduling optimization algorithm based on EMPA-ASA.

Zhigang Zhang1, Jiaqi Gao1, Rong Liu1

  • 1Tianjin Chengjian University, Tianjin, China.

Plos One
|April 10, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive hybrid-cloud scheduling algorithm using MDP+Q-learning and EMPA-ASA. It significantly improves Quality of Service (QoS) and reduces costs, especially under high loads.

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Area of Science:

  • Cloud Computing
  • Resource Management
  • Artificial Intelligence

Background:

  • Hybrid-cloud scheduling faces challenges balancing cost, performance, and reliability.
  • Existing methods often require extensive parameter tuning and have limited QoS metrics.
  • High computational overhead is a common issue in current scheduling approaches.

Purpose of the Study:

  • To develop an efficient and reliable hybrid-cloud resource scheduling algorithm.
  • To address limitations of existing approaches regarding parameter tuning, QoS indicators, and computational overhead.
  • To achieve a superior cost-performance trade-off in hybrid-cloud environments.

Main Methods:

  • State-driven adaptive scheduling and resource allocation using Markov Decision Process (MDP) + Q-learning.
  • Incorporation of an M/M/c queueing model to quantify Quality of Service (QoS) constraints.
  • Fusion of Evolutionary Multi-Population Algorithm (EMPA) with Adaptive Simulated Annealing (ASA) and Lévy flights for enhanced exploration and convergence.

Main Results:

  • The proposed EMPA-ASA algorithm outperforms baseline methods in key QoS metrics like delay, response time, throughput, and packet-loss rate.
  • Significant cost reductions were observed: approximately 48% compared to Genetic Algorithms (GA) and 70% compared to Particle Swarm Optimization (PSO).
  • Performance advantages in both QoS and cost are particularly evident in high-load scenarios.

Conclusions:

  • The EMPA-ASA algorithm offers an efficient and reliable solution for hybrid-cloud resource scheduling.
  • It provides a superior cost-performance trade-off, overcoming limitations of existing methods.
  • The approach demonstrates effectiveness in improving QoS and reducing operational costs under demanding conditions.