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Performance Modeling and Analysis of Distributed Systems Using Petri Nets and Fuzzy Logic

t 1a. P 1a. P out-a. P a. (4,5,7,9). (0,0,0,0). d 1a ( t). d 2a ( t). d 2a ( t). (4,5,7,9). P free. d 2b ( t). (4,5,7,9). d 2b ( t). d 1b ( t). P b. P 1b. P out-b. (4,5,7,9). Performance Modeling and Analysis of Distributed Systems Using Petri Nets and Fuzzy Logic.

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Performance Modeling and Analysis of Distributed Systems Using Petri Nets and Fuzzy Logic

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  1. t1a P1a Pout-a Pa (4,5,7,9) (0,0,0,0) d1a(t) d2a(t) d2a(t) (4,5,7,9) Pfree d2b(t) (4,5,7,9) d2b(t) d1b(t) Pb P1b Pout-b (4,5,7,9) Performance Modeling and Analysis of Distributed Systems Using Petri Nets and Fuzzy Logic Investigator: Tadao Murata, Department of Computer Science Prime Grant Support: National Science Foundation • The size and complexity of real-time distributed systems makes it extremely difficult to predict the performance of these applications and their underlying networks • Fuzzy-timing models associate possibility distributions of delays with events taking place in the system being modeled, well mimicking complex behaviors of the system, making the formal model very beneficial in performance modeling and analysis of complicated distributed systems • Monitor the system to obtain parameters such as bandwidth and latency to characterize the possibility distributions of the Fuzzy-Timing Petri Net (FTHN) model • Build the FTHN model of the architecture to be analyzed based on the collected data • Use fuzzy logic and simulation to analyze and verify the modeled system. Network features that are needed in order to implement currently unattainable interactions can be obtained • Applied FTHN model to assist us in the design of a high-speed transport protocol for Long Fat Networks. • Developed techniques and tools for performance analysis of network protocols and QoS requirement analysis of the networks: Proposed a topology-approximation to enable the formal model to have capability in modeling unpredictable dynamic topology, thus enlarging its application domains • Future work includes: apply FTHN model in other areas such as developing the intelligent optimization of concerted heterogeneous data transmissions in distributed wide-area cluster computing environments

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