Convergence Results for Ant Routing Algorithms via Stochastic Approximations

Convergence Results for Ant Routing Algorithms via Stochastic Approximations

Title : Convergence Results for Ant Routing Algorithms via Stochastic Approximations
Authors :
Purkayastha, Punyaslok
Baras, John, S.
Conference : ACM Transactions on Autonomous and Adaptive Systems (TAAS) Vol. 8, Issue 1, Article 3, pp. 1-3
Date: April 01 - April 01, 2013

In this article, we provide convergence results for an Ant-based Routing Algorithm (ARA) 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 earlier by Bean and Costa [2005]. 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 respective destinations via the network. Ant packets, which are nothing but probe packets, are introduced 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 system in order to obtain the equilibrium behavior of the routing algorithm. We also explore properties of the equilibrium routing probabilities and provide illustrative simulation results.

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