• Journal of Information Processing Systems
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• v.10 no.1
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• pp.36-54
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• 2014

Resource sharing is a major advantage of distributed computing. However, a distributed computing system may have some physical or virtual resource that may be accessible by a single process at a time. The mutual exclusion issue is to ensure that no more than one process at a time is allowed to access some shared resource. The article proposes a token-based mutual exclusion algorithm for the clustered mobile ad hoc networks (MANETs). The mechanism that is adapted to handle token passing at the inter-cluster level is different from that at the intra-cluster level. It makes our algorithm message efficient and thus suitable for MANETs. In the interest of efficiency, we implemented a centralized token passing scheme at the intra-cluster level. The centralized schemes are inherently failure prone. Thus, we have presented an intra-cluster token passing scheme that is able to tolerate a failure. In order to enhance reliability, we applied a distributed token circulation scheme at the inter-cluster level. More importantly, the message complexity of the proposed algorithm is independent of N, which is the total number of nodes in the system. Also, under a heavy load, it turns out to be inversely proportional to n, which is the (average) number of nodes per each cluster. We substantiated our claim with the correctness proof, complexity analysis, and simulation results. In the end, we present a simple approach to make our protocol fault tolerant.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.6
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• pp.152-160
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• 1996

This paper focuses on development and evaluation of a performance management algorithm for IEEE 802.4 token bus networks to serve large-scale integrated manufacturing systems. Such factory automation networks have to satisfy delay constraints imposed on time-critical messages while maintaining as much network capacity as possible for non-time-critical messages. This paper presents a network performance manager that adjusts queue capacity as well as timers by using a set of fuzzy rules and fuzzy inference mechanism. The efficacy of the performance management has been demonstrated by a series of simulation experiments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.461-466
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• 1993

This paper focuses on development and implementation of a performance management algorithm for IEEE802.4 token bus networks to serve large-sale integrated systems. The delivery of time critical messages within delay constraints is an important criterion in the design and management of computer communication networks. In order to achieve this goal, the theory of fuzzy sets has been employed to imitate human's reasoning. The Fuzzy Network Performance Manager(FNPM) is composed of two parts: FNPM1 & FNPM2. FNPM1 is solily intended to satisfy the data latencyfor the highest priority while the other part is trying to satisfy those for the lower priorities. The FNPM requires average data latency, throughput, and token circulation time for its inputs. The efficacy of the FNPM has been evaluated by a series of simulation experiments.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.8
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• pp.2436-2448
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• 1996

This paper presents the development of three types of network performance manager for IEEE 802.4 token bus networks that are a part of Manufacturing Automation Protocol(MAP). The performance managers attempt to keep the average data latency below a certain level specified for each priority class. All of the three performance managers are based on a set of fuzzy rules incorporating the knowledge on the relationship between data latency and parameters of the priority mechanism. These Fuzzy Network Performance Managers(FNPMs) have been evaluated via discrete event simulation to demonstrate their efficacy.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.4
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• pp.46-55
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• 1997

This paper presents the development of intelligent performance management of computer communication networks for larger-scale integrated systems and the demonstration of its efficacy using computer simula- tion. The innermost core of the performance management is based on fuzzy set theory. This fuzzy perfor- mance manager has learning ability by using principles of neuro-fuzzy model, neuralnetwork, genetic algo- rithm(GA). Two types of performance managers are described in this paper. One is the Neuro-Fuzzy Per- formance Manager(NFPM) of which learning ability is based on the conventional gradient method, and the other is GA-based Neuro-Fuzzy Performance Manager(GNFPM)with its learning ability based on a genetic algorithm. These performance managers have been evaluated via discrete event simulation of a computer network.