Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Author: search.filters.author.Purkayastha, PunyaslokSubject: search.filters.subject.ant routing algorithms

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    Convergence Results for Ant Routing Algorithms via Stochastic Approximations
    (2009-10) Purkayastha, Punyaslok; Baras, John; Baras, John
    In this paper, we provide convergence results for an Ant-Based 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. 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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    Convergence Results for Ant Routing Algorithms via Stochastic Approximation and Optimization
    (2007) Purkayastha, Punyaslok; Baras, John S.
    ``Ant algorithms'' have been proposed to solve a variety of problems arising in optimization and distributed control. They form a subset of the larger class of ``Swarm Intelligence'' algorithms. The central idea is that a "swarm" of relatively simple agents can interact through simple mechanisms and collectively solve complex problems. Instances that exemplify the above idea abound in nature. The abilities of ant colonies to collectively accomplish complex tasks have served as sources of inspiration for the design of ``Ant algorithms''. Examples of ``Ant algorithms'' are the set of ``Ant Routing'' algorithms that have been proposed for communication networks. We analyze in this paper Ant Routing Algorithms for packet-switched wireline networks. The algorithm retains most of the salient and attractive features of Ant Routing Algorithms. The scheme is a multiple path probabilistic routing scheme, that is fully adaptive and distributed. Using methods from adaptive algorithms and stochastic approximation, we show that the evolution of the link delay estimates can be closely tracked by a deterministic ODE system. A study of the equilibrium points of the ODE gives us the equilibrium behavior of the routing algorithm, in particular, the equilibrium routing probabilities, and mean delays in the links under equilibrium. We also show that the fixed-point equations that the equilibrium routing probabilities satisfy are actually the necessary and sufficient conditions of an appropriate optimization problem. Simulations supporting the analytical results are provided.