Quantifying Energy and Cost Benefits of Hybrid Edge Cloud: Analysis of Traditional and Agentic Workloads

TL;DR

This paper analyzes how Hybrid Edge Cloud (HEC) improves energy efficiency and reduces costs for traditional and AI-driven workloads, demonstrating significant savings and scalability benefits.

Contribution

It provides a quantitative analysis of energy and cost benefits of HEC for both traditional IoT workloads and agentic AI workloads, highlighting its advantages over centralized cloud systems.

Findings

Energy savings up to 75% with HEC

Cost reductions exceeding 80%

Effective for resource-intensive agentic workloads

Abstract

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.