20,869 research outputs found

    Software-based fault-tolerant routing algorithm in multidimensional networks

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    Massively parallel computing systems are being built with hundreds or thousands of components such as nodes, links, memories, and connectors. The failure of a component in such systems will not only reduce the computational power but also alter the network's topology. The software-based fault-tolerant routing algorithm is a popular routing to achieve fault-tolerance capability in networks. This algorithm is initially proposed only for two dimensional networks (Suh et al., 2000). Since, higher dimensional networks have been widely employed in many contemporary massively parallel systems; this paper proposes an approach to extend this routing scheme to these indispensable higher dimensional networks. Deadlock and livelock freedom and the performance of presented algorithm, have been investigated for networks with different dimensionality and various fault regions. Furthermore, performance results have been presented through simulation experiments

    On Fault Diagnosis of random Free-choice Petri Nets

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    This paper presents an on-line diagnosis algorithm for Petri nets where a priori probabilistic knowledge about the plant operation is available. We follow the method developed by Benveniste, Fabre, and Haar to assign probabilities to configurations in a net unfolding thus avoiding the need for randomizing all concurrent interleavings of transitions. We consider different settings of the diagnosis problem, including estimating the likelihood that a fault may have happened prior to the most recent observed event, the likelihood that a fault will have happened prior to the next observed event. A novel problem formulation treated in this paper considers deterministic diagnosis of faults that occurred prior to the most recent observed event, and simultaneous calculation of the likelihood that a fault will occur prior to the next observed event

    Latency Optimal Broadcasting in Noisy Wireless Mesh Networks

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    In this paper, we adopt a new noisy wireless network model introduced very recently by Censor-Hillel et al. in [ACM PODC 2017, CHHZ17]. More specifically, for a given noise parameter p[0,1],p\in [0,1], any sender has a probability of pp of transmitting noise or any receiver of a single transmission in its neighborhood has a probability pp of receiving noise. In this paper, we first propose a new asymptotically latency-optimal approximation algorithm (under faultless model) that can complete single-message broadcasting task in D+O(log2n)D+O(\log^2 n) time units/rounds in any WMN of size n,n, and diameter DD. We then show this diameter-linear broadcasting algorithm remains robust under the noisy wireless network model and also improves the currently best known result in CHHZ17 by a Θ(loglogn)\Theta(\log\log n) factor. In this paper, we also further extend our robust single-message broadcasting algorithm to kk multi-message broadcasting scenario and show it can broadcast kk messages in O(D+klogn+log2n)O(D+k\log n+\log^2 n) time rounds. This new robust multi-message broadcasting scheme is not only asymptotically optimal but also answers affirmatively the problem left open in CHHZ17 on the existence of an algorithm that is robust to sender and receiver faults and can broadcast kk messages in O(D+klogn+polylog(n))O(D+k\log n + polylog(n)) time rounds.Comment: arXiv admin note: text overlap with arXiv:1705.07369 by other author

    Getting expert systems off the ground: Lessons learned from integrating model-based diagnostics with prototype flight hardware

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    As an initial attempt to introduce expert system technology into an onboard environment, a model based diagnostic system using the TRW MARPLE software tool was integrated with prototype flight hardware and its corresponding control software. Because this experiment was designed primarily to test the effectiveness of the model based reasoning technique used, the expert system ran on a separate hardware platform, and interactions between the control software and the model based diagnostics were limited. While this project met its objective of showing that model based reasoning can effectively isolate failures in flight hardware, it also identified the need for an integrated development path for expert system and control software for onboard applications. In developing expert systems that are ready for flight, artificial intelligence techniques must be evaluated to determine whether they offer a real advantage onboard, identify which diagnostic functions should be performed by the expert systems and which are better left to the procedural software, and work closely with both the hardware and the software developers from the beginning of a project to produce a well designed and thoroughly integrated application
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