We describe a novel approach for designing network control algorithms that incorporate traffic models. Traffic models can be viewed as stochastic predictions about the future network state, and can be used to generate traces of potential future network behavior. Our approach is to use such traces to heuristically evaluate candidate control actions using a technique called hindsight optimization. In hindsight optimization, the finite-horizon "utility" achievable from a given system state is estimated by averaging estimates obtained from a number of traces starting at the state. For each trace, the utility value of the state is estimated by determining the optimal "hindsight control" -- this is the control that would be applied by an optimal controller that somehow "knew" the whole trace beforehand -- and then measuring the utility obtained under that control. Averaging over many samples then gives a simulation-based "hindsight-optimal" utility for the starting state that upper bounds the true...

In this paper, we present a novel control-theoretic explicit rate (ER) allocation algorithm for the MAX--MIN flow control of elastic traffic services with minimum rate guarantee in the setting of the ATM available bit rate (ABR) service. The proposed ER algorithm is simple in that the number of operations required to compute it at a switch is minimized, scalable in that per-virtual -circuit (VC) operations including per-VC queueing, per-VC accounting, and per-VC state management are virtually removed, and stable in that by employing it, the user transmission rates and the network queues are asymptotically stabilized at a unique equilibrium point at which MAX--MIN fairness with minimum rate guarantee and target queue lengths are achieved, respectively. To improve the speed of convergence, we normalize the controller gains of the algorithm by the estimate of the number of locally bottlenecked VCs. The estimation scheme is also computationally simple and scalable since it does not require per-VC accounting either. We analyze the theoretical performance of the proposed algorithm and verify its agreement with the practical performance through simulations in the case of multiple bottleneck nodes. We believe that the proposed algorithm will serve as an encouraging solution to the MAX--MIN flow control of elastic traffic services, the deployment of which has been debated long due to their lack of theoretical foundation and implementation complexity. Index Terms---Asymptotic decay rate, elastic traffic services, max--min flow rate, scalibility, stability. I.

We consider the burst-level congestion-control problem in a communication network with multiple traffic sources, each modeled as a fully-controllable stream of fluid traffic. The controlled traffic shares a common bottleneck node with high-priority cross traffic described by a Markov-modulated fluid (MMF). Each controlled source is assumed to have a unique round-trip delay. The goal is to maximize a linear combination of the throughput, delay, traffic loss rate, and a fairness metric at the bottleneck node. We introduce a simulation-based congestion-control scheme capable of performing effectively under rapidly-varying cross traffic by making use of the provided MMF model of that variation. In our scheme, the control problem is posed as a finite-horizon Markov decision process and is solved heuristically using a technique called Hindsight Optimization. We provide a detailed derivation of our congestion-control algorithm based on this technique. Our empirical study shows that the control scheme performs sign...

We consider the congestion-control problem in a communication network with multiple traffic sources, each modeled as a fullycontrollable stream of fluid traffic. The controlled traffic shares a common bottleneck node with high-priority cross traffic described by a Markovmodulated fluid (MMF). Each controlled source is assumed to have a unique round-trip delay. We wish to maximize a linear combination of the throughput, delay, traffic loss rate, and a fairness metric at the bottleneck node. We introduce an online sampling-based burst-level congestioncontrol scheme capable of performing effectively under rapidly-varying cross traffic by making explicit use of the provided MMF model of that variation. The control problem is posed as a finite-horizon Markov decision process and is solved heuristically using a technique called Hindsight Optimization. We provide a detailed derivation of our congestion-control algorithm based on this technique. The distinguishing feature of our scheme relative to conventional congestion-control schemes is that we exploit a stochastic model of the cross traffic. Our empirical study shows that our control scheme significantly outperforms the conventional proportionalderivative (PD) controller, achieving higher utlization, lower delay, and lower loss under reasonable fairness. The performance advantage of our scheme over the PD scheme grows as the rate variance of cross traffic increases, underscoring the effectiveness of our control scheme under variable cross traffic.

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We develop a new class of asynchronous distributed algorithms for the explicit rate control of elastic sessions in an integrated packet network. Sessions can request for minimum guaranteed rate allocations (e.g., MCRs in the ATM context), and, under this constraint, we seek to allocate the max-min fair rates to the sessions. We capture the integrated network context by permitting the link bandwidths available to elastic sessions to be stochastically time varying. The available capacity of each link is viewed as some statistic of this stochastic process (e.g., a fraction of the mean, or a large deviations Equivalent Service Capacity (ESC)). For fixed available capacity at each link, we show that the vector of max-min fair rates can be computed from the root of a certain vector equation. A distributed asynchronous stochastic approximation technique is then used to develop a provably convergent distributed algorithm for obtaining the root of the equation, even when the link flows and the ...

The growing demand of computer usage requires efficient ways of managing network traffic in order to avoid or at least limit the level of congestion in cases where increases in bandwidth are not desirable or possible. Using non-linear control theory we developed and analysed a generic Integrated Dynamic Congestion Control (IDCC) scheme for controlling traffic using information on the status of each queue in the network. The IDCC scheme is based on a nonlinear model of the network that is generated using fluid flow considerations. The methodology used is general and independent of technology, as for example TCP/IP or ATM. We assume a differentiated-services network framework and formulate our control strategy in the same spirit as IP Diff-Serv for three types of services: Premium Service, Ordinary Service, and Best Effort Service. The three differentiated classes of traffic operate at each output port of a router/switch. An IDCC scheme is designed for each output port, and a powerful, simple to implement controller is designed and analysed. The IDCC methodology has been applied to an ATM network. We use OPNET simulations to demonstrate that the proposed control methodology achieves the desired behaviour of the network, and possesses important attributes, such as: stable and robust behaviour, high utilisation with bounded delay and loss performance, and good steady state and transient behaviour.

Public-access networks need to handle persistent congestion and overload caused by high bandwidth aggregates that may occur during times of flooding- based DDoS attacks or flash crowds. The often unpredictable nature of these two activities can severely degrade server performance. Legitimate user requests also suffer considerably when traffic from many different sources aggregates inside the network and causes congestion. This paper studies a “family ” of algorithms that “proactively ” protect a server from overload by installing rate throttles in a set of upstream routers. A control-theoretic approach is used to obtain an “optimal ” control setting that achieves throttling in a distributed and fair manner by taking important performance metrics into consideration, such as minimizing overall load variations. Using ns-2 simulations, we show that our proposed algorithms (1) are highly adaptive by avoiding unnecessary control parameter configuration, (2) provide max-min fairness for any number of throttling routers, (3) respond very quickly to network changes, (4) are extremely robust against extrinsic factors beyond the system control, and (5) are stable under given delay bounds. Most importantly, our approach provides a systematic way to handle high bandwidth aggregates by viewing them as a generic congestion control problem with relevance to many network applications. I.

A new, more realistic model for the Available Bit-Rate traffic class in ATM network congestion control with explicit rate feedback is introduced and analyzed. This model is based on recent results by Ekanayake, regarding discrete time models for time-variant delays. The discrete time model takes into account the effect of time-variant buffer occupancy levels of ATM switches, thus treating the case of time-variant delays between a single congested node and the connected sources. For highly dynamic situations, such a model is crucial for a valid analysis of the resulting feedback system. The new model also handles the effects of the mismatch between the resource management cell rates and the variable bit rate controller sampling rate as well as buffer and rate nonlinearities. A brief stability study shows that an equilibrium in the buffer occupancy is impossible to achieve in the presence of time-variant forward path delays. Stability conditions for the case of time-variant delays in the return path are presented. Finally, illustrative examples are provided.

The establishment of the overall objectives of a distributed sensor network is a dynamic task so that it may sufficiently well `track' its environment.