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AI-Driven Multi-Agent Real-Time Load Balancing for Energy-Efficient Cloud–Edge Systems
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Author(s): Jianfeng Chen (Powerchina Huadong Engineering Corporation, China), Sijing Zhu (Powerchina Huadong Engineering Corporation, China), Anke Li (Powerchina Huadong Engineering Corporation, China)and Yi Xue (Powerchina Huadong Engineering Corporation, China)
Copyright: 2026
Volume: 18
Issue: 1
Pages: 25
Source title:
International Journal of Grid and High Performance Computing (IJGHPC)
Editor(s)-in-Chief: Emmanuel Udoh (Sullivan University, USA)and Ching-Hsien Hsu (Asia University, Taiwan)
DOI: 10.4018/IJGHPC.411217
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Abstract
This study presents AI-driven real-time load balancing (AI-RTLB), a framework for heterogeneous cloud–edge–Internet of Things (IoT) systems. AI-RTLB combines long short-term memory attention-based workload forecasting with adaptive multi-agent reinforcement learning (decentralized actors, shared critic) and a post-optimization layer that enforces energy, fairness, thermal, and power-budget constraints. This design anticipates demand surges, coordinates distributed decisions, and produces service-level agreement (SLA)-aware schedules. Across large data-center traces, edge workloads, and IoT demand logs, AI-RTLB reduces average latency by 16.3% and improves energy efficiency by 21.7% over strong baselines, while increasing throughput and lowering SLA violations (5.3%) with a lower fairness index (0.041). Convergence is faster and more stable than with single-agent deep reinforcement learning, and robustness is maintained under workload noise and mixed job types. An ablation study confirms complementary gains from prediction, multi-agent control, and fairness- and energy-aware optimization. AI-RTLB offers a practical path toward efficient, equitable, and sustainable computing.
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