Adaptive scheduling algorithms for energy efficiency in hybrid cloud-edge computing paradigms
Abstract
Hybrid cloud-edge computing faces energy-efficiency challenges, as workloads can be dynamic and resource provisioning may be heterogeneous, leading to varying resource utilization. This research introduces ADEES, an Adaptive Dynamic Energy-Efficient Scheduler, that dynamically minimizes energy consumption while meeting QoS requirements in distributed environments. ADEES does this by combining real-time workload classification with resource allocation through reinforcement learning to optimize task distribution between edge and cloud nodes. The results indicate that ADEES outperforms conventional schedulers (i.e., Round-Robin, Kubernetes Default, and genetic algorithm) in simulations based on replicated Alibaba cluster traces and synthetic workloads, achieving 22.4% lower energy consumption and 9.8% more completed tasks, while incurring only a 7% increase in latency for non-critical tasks. ADEES is up to 2000 times lighter than other approaches and scales efficiently for deployments of up to 500 nodes. This research contributes to sustainable distributed computing by investigating the critical trade-off between energy efficiency and application performance in the context of 5G and the Internet of Things. The key contributions are the two-tiered adaptation strategy and the practical recommendations for heterogeneous environments.
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Copyright (c) 2026 Moyosoluwa Awodire, Adedayo Adisa (Author)
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