2026· IEEE Transactions on Networking· Vol 34, pp. 6693-6707· 0 citations· 36 references
Computer Science
Abstract
Emerging edge computing paradigms enable heterogeneous devices to collaborate on complex computation applications. However, for arbitrary heterogeneous edge networks, delay-optimal forwarding and computation offloading for long-term average performance remains an open problem. In this paper, we jointly optimize data/result routing and computation placement in arbitrary networks with heterogeneous node capabilities and congestion-dependent nonlinear transmission and processing costs. Despite the non-convexity of the formulated problem, by analyzing the KKT conditions, we provide a set of sufficient optimality conditions that solve the problem globally. To provide insight into such global optimality, we show that the proposed non-convex problem is geodesically convex under mild assumptions. We also show that the proposed sufficient optimality condition leads to a lower hemicontinuous solution set, providing stability against user-input perturbations. We then extend the framework to incorporate utility-based congestion control and fairness. We develop a fully distributed algorithm that converges to the global optimum. Numerical results demonstrate significant improvements over multiple baseline algorithms.
Finding a routing path that satisfies two independent additive constraints (e.g., delay and cost) is a critical requirement in quality of service (QoS) routing. While this two-constraint path problem is NP-Hard, it is prevalent in practical network applications. Existing solutions typically face a trade-off: heuristics lack feasibility guarantees, while exact algorithms suffer from exponential computational complexity. In this paper, we propose a dual-guided exact algorithm that effectively bridges the gap between the computational efficiency of Lagrangian relaxation and the optimality guarantees of combinatorial search. Our method first solves the Lagrangian dual problem to derive the optimal multiplier, which subsequently serves as the optimal aggregate coefficient to guide a heuristic A*-prune search. This hybrid mechanism allows the algorithm to efficiently prune the search space while guaranteeing the identification of a cost-efficient feasible solution. Numerical experiments on random network topologies demonstrate that the proposed algorithm significantly outperforms the standard A*-prune algorithm while maintaining exactness. Specifically, in networks with up to 500 nodes, our method reduces the execution time by orders of magnitude compared to traditional exact methods.
Kaixiang Hu, Xiankai Li, Caixia Kou· International Journal of Fou...· 0 citations
As practical quantum networks approach large-scale deployment, the need for efficient user-to-user frequency allocation is increasing, yet current approaches only provide partial solutions to the routing and spectrum allocation problem for an arbitrary quantum network. We address this challenge for repeater-less flex-grid quantum networks based on hyperentangled photons using an efficient three-stage pipeline combining leading tools in classical networking with recent advances in numerical optimization. First, double instantiations of Yen's algorithm obtain low-loss route candidates between each pair of users and the entanglement sources. Second, the advanced process optimizer (APOPT) obtains frequency channel allocations that maximize distribution rates under fidelity constraints. Finally, the constraint programming solver using satisfiability methods (CP-SAT) assigns specific frequency bins to each link, ensuring that there is no contention between frequencies from different sources. We numerically demonstrate this approach on a representative ring network and a Manhattan incumbent local exchange carrier topology, realizing significant improvements over prior genetic algorithm approaches in speed, accuracy, and scalability. Overall, this pipeline provides an efficient heuristic workflow for optimizing broadband entanglement distribution, applicable to arbitrarily connected quantum networks integrated within the existing lightwave infrastructure.
Zachary Goisman, M. L. Stevens, Maxwell Goisman et al.· 0 citations
Future 6G networks will integrate communication and computing capabilities to support intelligent, delay-sensitive services. In heterogeneous cloud-edge environments, however, task offloading and routing decisions are strongly coupled, and dynamic workloads, limited computing resources, and constrained link capacity make efficient service provisioning challenging. Existing reinforcement learning-based offloading methods can improve decision efficiency, but many focus on simplified or single-domain settings and do not adequately account for backbone topology and bandwidth constraints. To address this problem, this paper studies joint task offloading and routing optimization in multi-domain cloud-edge networks, explicitly modeling network topology and link capacity. We propose a cooperative multi-agent deep reinforcement learning method that coordinates distributed edge agents through centralized training and decentralized execution. Routing optimization feedback is further incorporated to guide constraint-aware policy learning. Simulation results demonstrate that the proposed method reduces end-to-end latency, mitigates network congestion, and avoids link and node overload in cloud-edge networks.
Yi Yue, Shuai Zhang, Zhen Han et al.· IEEE International Conferenc...· 0 citations
Computility networks have emerged as a critical infrastructure for large-scale, data-intensive task execution, where computing and network resources must be jointly allocated across multiple providers and network operators. Although prior studies have mainly focused on computing resource allocation, the allocation and pricing of network resources have received relatively limited attention, due to the topology-dependent and flow-based nature of network resources. To tackle this issue, we investigate the problem of network link allocation and pricing in Computility networks, with a focus on mechanism design. We first formulate a mixed-integer optimization model that minimizes users' network transmission costs under joint network-flow and computing-resource constraints. Based on this model, we propose VCG-LAPM(VCG-based link allocation and pricing mechanism). VCG-LAPM employs a successive shortest-path min-cost flow algorithm to compute efficient link allocations and adopts a reverse VCG pricing rule to ensure incentive compatibility and individual rationality in theory. To improve robustness in the presence of critical links, a price-cap rule is further introduced. Experimental results show that VCG-LAPM can achieve nearoptimal user payments and significantly improves task acceptance rates compared with single-operator constrained schemes across varying network scales and requirement intensities.
X. Huang, Ya-Bing Kang, Qing-Zhen Xiang et al.· Fall Joint Computer Conferen...· 0 citations
This work proposes Double-Channel Graph Attention (DCGA), an end-to-end reinforcement learning framework that isolates network reachability and demand-service logic into separate graph channels and constructs valid routes using a simulator-coupled, constraint-informed decoder.
Hao Sun, Fang He, Congyuan Ji et al.· arXiv.org· 0 citations
Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches.
Tanmay Baidya, Sangman Moh· Italian National Conference...· 0 citations
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