Minh-Tuong Nguyen | Searcharxiv

arXiv2026

Exact DC Representation of Multi-Tier Offloading Product in SAGINs via Quantifier Elimination

Task offloading in space--air--ground integrated networks (SAGIN) yields non-convex signomial or polynomial programs with cubic couplings. Sequential geometric programming (SGP) approximates them via exponential cone representations, which exceeds the second-order cone programming (SOCP) ceiling of embedded code generators such as CVXPYgen. We derive a difference-of-convex (DC) representation exactly certified over the reals by quantifier elimination and apply the convex--concave procedure (CCP), whose SOCP subproblems remove this structural obstacle to future embedded code generation. Comparisons with the BARON global solver show that SGP and CCP both attain near-global solutions. CCP further reduces the average solution time from SGP's $0.1012$~s to $0.0113$~s, an $8.9$-fold speedup.

eess.SP↗

arXiv2026

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

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.

cs.IT↗