Joint Service Placement and Resource Optimization in Hierarchical Edge-Cloud Networks

2026-05-23

AI Summary

This study addresses the joint optimization of end-to-end latency and system cost in hierarchical edge–cloud IoT networks by co-designing service placement, task offloading, and bandwidth allocation strategies. The original non-convex mixed-integer nonlinear programming problem is relaxed and solved via an efficient iterative algorithm that integrates successive convex approximation (SCA) with a multi-timescale decomposition approach, guaranteeing convergence to a local optimum. Experimental results demonstrate that the proposed scheme significantly outperforms existing benchmarks, achieving substantial reductions in both response latency and operational overhead while enhancing overall system efficiency, thereby validating the critical value of the integrated optimization framework.

Abstract

Hierarchical edge-cloud computing-aided Internet of Things (IoT) networks offer low-latency and cost-efficient services to a growing number of data-intensive IoT devices. However, optimizing service placement, which involves determining the most suitable locations within a network to deploy various services, is critical to balancing workloads dynamically and ensuring efficient resource utilization. In this paper, we jointly optimize service placement, edge/cloud cooperation, task offloading, and bandwidth allocation to enhance processing efficiency and response times. The main objective is to minimize both the overall end-to-end latency and the system cost, including service deployment and operational costs. The formulated problem belongs to the class of non-convex mixed-integer nonlinear programming, where finding a feasible solution is already challenging. Towards a stable system, we first transform the original problem into a more tractable form and then decompose it into sub-problems which are solved at different timescales. Combining tools from relaxation and the successive convex approximation method, we develop iterative algorithms to solve these problems efficiently. With an appropriate penalty parameter, the proposed algorithms guarantee convergence to at least a local optimum. We produce extensive numerical results to demonstrate the superior performance of the proposed algorithms over benchmark schemes as well as emphasize the significance of the joint service placement and resource allocation in enhancing system performance and efficiency.

Problem

• service placement
• resource optimization
• edge-cloud networks
• latency minimization
• system cost

Innovation

• service placement
• edge-cloud cooperation
• task offloading
• successive convex approximation
• resource optimization