Data networks
Feedback control of congestion in packet switching networks: the case of a single congested node
IEEE/ACM Transactions on Networking (TON)
Link capacity allocation and network control by filtered input rate in high-speed networks
IEEE/ACM Transactions on Networking (TON)
Optimization flow control—I: basic algorithm and convergence
IEEE/ACM Transactions on Networking (TON)
A game theoretic framework for bandwidth allocation and pricing in broadband networks
IEEE/ACM Transactions on Networking (TON)
A new predictive flow control scheme for efficient network utilization and QoS
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
A Linear Dynamic Model for Design of Stable Explicit-Rate ABR Control Schemes
INFOCOM '97 Proceedings of the INFOCOM '97. Sixteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Driving the Information Revolution
Congestion control as a stochastic control problem with action delays
Automatica (Journal of IFAC)
Non-convex optimization and rate control for multi-class services in the Internet
IEEE/ACM Transactions on Networking (TON)
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In this paper, we consider a network with both controllable and uncontrollable flows. Uncontrollable flows are typically generated from applications with stringent QoS requirements and are given high priority. On the other hand, controllable flows are typically generated by elastic applications and can adapt to the available link capacities in the network. We provide a general model of such a system and analyze its queueing behavior. Specially, we obtain a lower bound and an asymptotic upper bound for the tail of the workload distribution at each link in the network. These queueing results provide us with guidelines on how to design a feedback flow control system. Simulation results show that the lower bound and asymptotic upper bound are quite accurate and that our feedback control method can effectively control the queue length in the presence of both controllable and uncontrollable traffic. Finally, we describe a distributed strategy that uses the notion of Active Queue Management (AQM) for implementing our flow control solution.