Unidirectional network

Abstract: Power supply, a surprisingly simple and new general paradigm for the development of self-stabilizing algorithms in different models, is introduced. The paradigm is exemplified by developing simple and efficient self-stabilizing algorithms for leader election and either breadth-first search or depth-first search spanning-tree constructions, in strongly connected unidirectional and bidirectional dynamic networks (synchronous and asynchronous). The different algorithms stabilize in O(n) time in both synchronous and asynchronous networks without assuming any knowledge of the network topology or size, where n is the total number of nodes. Following the leader election algorithms, we present a generic self-stabilizing spanning tree and/or leader election algorithm that produces a whole spectrum of new and efficient algorithms for these problems. Two variations that produce either a rooted depth-first search tree or a rooted breadth-first search tree are presented.

Abstract: Power supply, a surprisingly simple and new general paradigm for the development of self-stabilizing algorithms in different models, is introduced. The paradigm is exemplified by developing simple and efficient self-stabilizing algorithms for leader election and either breadth-first search or depth-first search spanning-tree constructions, in strongly connected unidirectional and bidirectional dynamic networks (synchronous and asynchronous). The different algorithms stabilize in O(n) time in both synchronous and asynchronous networks without assuming any knowledge of the network topology or size, where n is the total number of nodes. Following the leader election algorithms, we present a generic self-stabilizing spanning tree and/or leader election algorithm that produces a whole spectrum of new and efficient algorithms for these problems. Two variations that produce either a rooted depth-first search tree or a rooted breadth-first search tree are presented.

Abstract: Packet-switched unidirectional and bidirectional ring wavelength division multiplexing (WDM) networks with destination stripping provide an increased capacity due to spatial wavelength reuse Besides unicast traffic, future destination stripping ring WDM networks also need to support multicast traffic efficiently In this paper, we provide a probabilistic analysis of the mean hop distances traveled by multicast packet copies on the wavelength channels, and based on the mean hop distances analyze the nominal transmission capacity, reception capacity, and multicast capacity of both unidirectional and bidirectional ring WDM networks with destination stripping The developed analytical methodology accommodates not only multicast traffic with arbitrary multicast fanout but also unicast and broadcast traffic In our numerical investigations we examine the impact of number of ring nodes and multicast fanout on the transmission, reception, and multicast capacity of both types of ring networks for different unicast, multicast, and broadcast traffic scenarios and different mixes of unicast and multicast traffic Our analytical methodology provides a foundation for extended analyses of the multicast capacity of WDM ring networks and enables the evaluation and comparison of future multicast-capable medium access control (MAC) protocols for unidirectional and bidirectional ring WDM networks in terms of transmitter, receiver, and multicast throughput efficiency

Abstract: This paper presents distributed algorithms for election and traversal in strongly connected unidirectional networks. A unidirectional network consists of nodes which are processors connected by unidirectional communication links. Initially, processors differ by their identifier but are otherwise similar. The election algorithm distinguishes a single processor from all other processors. The election algorithm requires O(log n) bits of memory in each processor and has communication complexity of O(n • m+n2log n) bits. In the traversal algorithm one node initiates a token which visits all the nodes of the network and returns to the initiator. The traversal algorithm is derived from the election algorithm. It achieves the same communication complexity and uses only O(1) bits of memory in each processor.