Traffic-splitting networks operating under alpha-fair sharing policies and balanced fairness

We consider a data network in which, besides classes of users that use specific routes, one class of users can split its traffic over several routes. We consider load balancing at the packet-level, implying that traffic of this class of users can be divided among several routes at the same time. Assuming that load balancing is based on an alpha-fair sharing policy, we show that the network has multiple possible behaviors. In particular, we show that some classes of users, depending on the state of the network, share capacity according to some Discriminatory Processor Sharing (DPS) model, whereas each of the remaining classes of users behaves as in a single-class single-node model. We compare the performance of this network with that of a similar network, where packet-level load balancing is based on balanced fairness. We derive explicit expressions for the mean number of users under balanced fairness, and show by conducting extensive simulation experiments that these provide accurate approximations for the ones under alpha-fair sharing

Towards an Erlang formula for multiclass networks

International audienceConsider a multiclass stochastic network with state-dependent service rates and arrival rates describing bandwidth-sharing mechanisms as well as admission control and/or load balancing schemes. Given Poisson arrival and exponential service requirements, the number of customers in the network evolves as a multi-dimensional birth-and-death process on a finite subset of ℕ. We assume that the death (i.e., service) rates and the birth rates depending on the whole state of the system satisfy a local balance condition. This makes the resulting network a Whittle network, and the stochastic process describing the state of the network is reversible with an explicit stationary distribution that is in fact insensitive to the service time distribution. Given routing constraints, we are interested in the optimal performance of such networks. We first construct bounds for generic performance criteria that can be evaluated using recursive procedures, these bounds being attained in the case of a unique arrival process. We then study the case of several arrival processes, focusing in particular on the instance with admission control only. Building on convexity properties, we characterize the optimal policy, and give criteria on the service rates for which our bounds are again attained

Flow-level performance analysis of data networks using processor sharing models

Most telecommunication systems are dynamic in nature. The state of the network changes constantly as new transmissions appear and depart. In order to capture the behavior of such systems and to realistically evaluate their performance, it is essential to use dynamic models in the analysis. In this thesis, we model and analyze networks carrying elastic data traffic at flow level using stochastic queueing systems. We develop performance analysis methodology, as well as model and analyze example systems. The exact analysis of stochastic models is difficult and usually becomes computationally intractable when the size of the network increases, and hence efficient approximative methods are needed. In this thesis, we use two performance approximation methods. Value extrapolation is a novel approximative method developed during this work and based on the theory of Markov decision processes. It can be used to approximate the performance measures of Markov processes. When applied to queueing systems, value extrapolation makes possible heavy state space truncation while providing accurate results without significant computational penalties. Balanced fairness is a capacity allocation scheme recently introduced by Bonald and Proutière that simplifies performance analysis and requires less restrictive assumptions about the traffic than other capacity allocation schemes. We introduce an approximation method based on balanced fairness and the Monte Carlo method for evaluating large sums that can be used to estimate the performance of systems of moderate size with low or medium loads. The performance analysis methods are applied in two settings: load balancing in fixed networks and the analysis of wireless networks. The aim of load balancing is to divide the traffic load efficiently between the network resources in order to improve the performance. On the basis of the insensitivity results of Bonald and Proutière, we study both packet- and flow-level balancing in fixed data networks. We also study load balancing between multiple parallel discriminatory processor sharing queues and compare different balancing policies. In the final part of the thesis, we analyze the performance of wireless networks carrying elastic data traffic. Wireless networks are gaining more and more popularity, as their advantages, such as easier deployment and mobility, outweigh their downsides. First, we discuss a simple cellular network with link adaptation consisting of two base stations and customers located on a line between them. We model the system and analyze the performance using different capacity allocation policies. Wireless multihop networks are analyzed using two different MAC schemes. On the basis of earlier work by Penttinen et al., we analyze the performance of networks using the STDMA MAC protocol. We also study multihop networks with random access, assuming that the transmission probabilities can be adapted upon flow arrivals and departures. We compare the throughput behavior of flow-optimized random access against the throughput obtained by optimal scheduling assuming balanced fairness capacity allocation