John S. Baras

2012

Convergence Results for Ant Routing Algorithms via Stochastic Approximation

P. Purkayastha and J.S. Baras

To appear in ACM Transactions on Autonomous and Adaptive Systems.

Abstract

In this paper, we provide convergence results for an Ant Routing (ARA) Algorithm for wireline, packet switched communication networks, that are acyclic. Such algorithms are inspired by the foraging behavior of ants in nature. We consider an ARA algorithm proposed by Bean and Costa [2]. The algorithm has the virtues of being adaptive and distributed, and can provide a multipath routing solution. We consider a scenario where there are multiple incoming data traffic streams that are to be routed to their destinations via the network. Ant packets, which are nothing but probe packets, are used to estimate the path delays in the network. The node routing tables, which consist of routing probabilities for the outgoing links, are updated based on these delay estimates. In contrast to the available analytical studies in the literature, the link delays in our model are stochastic, time-varying, and dependent on the link traffic. The evolution of the delay estimates and the routing probabilities are described by a set of stochastic iterative equations. In doing so, we take into account the distributed and asynchronous nature of the algorithm operation. Using methods from the theory of stochastic approximations, we show that the evolution of the delay estimates can be closely tracked by a deterministic ODE (Ordinary Differential Equation) system, when the step-size of the delay estimation scheme is small. We study the equilibrium behavior of the ODE in order to obtain the equilibrium behavior of the algorithm. We also provide illustrative simulation results.

Categories and Subject Descriptors: C.2.2 [Computer-Communication Networks]: Network ProtocolsóRouting Protocols; G.3 [Probability Statistics]: Queuing Theory, Probabilistic algorithms

General Terms: Algorithms, Theory, Performance

Keywords: Ant routing algorithms, stochastic approximations and learning algorithms, queuing networks

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