报告详情
AI-Assisted Hybrid PSO–GWO Optimization with Deep Reinforcement Learning Control and LSTM Forecasting for Energy-Aware Load Balancing in Heterogeneous Distributed Systems
编号：125
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更新：2026-10-05 20:32:10 浏览：23次
Online
报告开始：2026年10月13日 17:15（Asia/Ho_Chi_Minh）
报告时间：15min
所在会场：[S5] Track 5: Emerging Trends of AI/ML [S5-7] Track 5: Emerging Trends of AI/ML
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摘要
Cloud data centers, edge nodes, and fog infrastructures have made distributed computing environments highly heterogeneous, and static load-balancing policies cannot keep pace with their dynamic workloads. This paper introduces AHMHO, an AI-Assisted Hybrid Meta-Heuristic Optimization framework that combines a hybrid Particle Swarm Optimization–Grey Wolf Optimizer (PSO–GWO) search with a Deep Reinforcement Learning (DRL) controller and an LSTM workload predictor. The DRL controller continuously tunes the search hyperparameters and the objective weights from environmental feedback, and the LSTM forecasts load so that scheduling is proactive. Simulations on CloudSim Plus 8.0 with Google cluster workload traces compared AHMHO with five baselines: First-Come-First-Served (FCFS), Round Robin, standalone PSO, standalone GWO, and Ant Colony Optimization (ACO). Over 30 runs, AHMHO reduced makespan by 9.2% and energy consumption by 13.1% relative to the strongest baseline (GWO), and by 34.7% and 41.1% relative to FCFS, while raising resource utilization from 70.2% to 88.5% and cutting SLA violations from 7.1% to 3.2%. The gains are statistically significant (Wilcoxon signed-rank test, p < 0.01). The per-cycle scheduling time of AHMHO (203.8 ms) is 17% higher than that of GWO but remains compatible with a 500 ms scheduling interval
关键词
Load balancing, hybrid meta-heuristics, deep reinforcement learning, particle swarm optimization, grey wolf optimizer, LSTM workload forecasting, energy-aware scheduling, heterogeneous distributed computing, cloud computing
报告人
Girish Kumar Kalludevanahalli Shamegowda
Technical PNC Financial Services稿件作者
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重要日期
-
会议日期
10月11日
2026
至10月14日
2026
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12月30日 2025
报告提交截止日期
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09月28日 2026
提前注册日期
-
10月10日 2026
初稿截稿日期
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10月14日 2026
注册截止日期
主办单位
United Societies of Science
承办单位
Posts and Telecommunications Institute of Technology
协办单位
IEEE Section
IEEE Vietnam Section
IEEE Vietnam Section
联系方式
- ASIANComNet 2026
- as******@usssociety.org
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