Optimal Control for Generalized Network-Flow Problems
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Abstract (arXiv)
We consider the problem of throughput-optimal packet dissemination, in the presence of an arbitrary mix of unicast, broadcast, multicast and anycast traffic, in a general wireless network. We propose an online dynamic policy, called Universal Max-Weight (UMW), which solves the above problem efficiently. To the best of our knowledge, UMW is the first throughput-optimal algorithm of such versatility in the context of generalized network flow problems. Conceptually, the UMW policy is derived by relaxing the precedence constraints associated with multi-hop routing, and then solving a min-cost routing and max-weight scheduling problem on a virtual network of queues. When specialized to the unicast setting, the UMW policy yields a throughput-optimal cycle-free routing and link scheduling policy. This is in contrast to the well-known throughput-optimal Back- Pressure (BP) policy which allows for packet cycling, resulting in excessive latency. Extensive simulation results show that the proposed UMW policy incurs a substantially smaller delay as compared to the BP policy. The proof of throughput-optimality of the UMW policy combines ideas from stochastic Lyapunov theory with a sample path argument from adversarial queueing theory and may be of independent theoretical interest.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Nov 2016.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2024Biased Backpressure Routing Using Link Features and Graph Neural Networkscited by 2
- 2025A Flexible Multi-Agent Deep Reinforcement Learning Framework for Dynamic Routing and Scheduling of Latency-Critical Servicescited by 0
- 2026A Constrained RL Approach for Cost-Efficient Delivery of Latency-Sensitive Applicationscited by 0
- 2026From Prior-Guided Heuristics to Deployable Agents: Accelerating Demonstration-Driven Reinforcement Learning for Deadline-Constrained Network Controlcited by 0
- 2026From Prior-Guided Heuristics to Deployable Agents: Accelerating Demonstration-Driven Reinforcement Learning for Deadline-Constrained Network Control3 Sep 2026
- 2026A Constrained RL Approach for Cost-Efficient Delivery of Latency-Sensitive Applications4 Mar 2026
- 2025A Flexible Multi-Agent Deep Reinforcement Learning Framework for Dynamic Routing and Scheduling of Latency-Critical Services13 Oct 2025
- 2024Biased Backpressure Routing Using Link Features and Graph Neural Networks13 Jul 2024