2025/01/21 by Siavash Alamouti, Alamouti, Siavash · 2 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Cloud Computing and Resource Management #Distributed #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Parallel #Transportation and Mobility Innovations #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2501.14823
openalex publication_date 2025/01/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper examines the workload distribution challenges in centralized cloud systems and demonstrates how Hybrid Edge Cloud (HEC) [1] mitigates these inefficiencies. Workloads in cloud environments often follow a Pareto distribution, where a small percentage of tasks consume most resources, leading to bottlenecks and energy inefficiencies. By analyzing both traditional workloads reflective of typical IoT and smart device usage and agentic workloads, such as those generated by AI agents, robotics, and autonomous systems, this study quantifies the energy and cost savings enabled by HEC. Our findings reveal that HEC achieves energy savings of up to 75% and cost reductions exceeding 80%, even in resource-intensive agentic scenarios. These results highlight the critical role of HEC in enabling scalable, cost-effective, and sustainable computing for the next generation of intelligent systems.