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Brief Bio

Brief Bio. 1968, PhD in EE, Caltech: research in statistical communication theory 1968 – 1976, IBM San Jose Research Lab: r esearch in statistical communication theory, queueing theory, computer performance modeling, statistical simulation methodology.

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Brief Bio

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  1. Brief Bio • 1968, PhD in EE, Caltech: research in statistical communication theory • 1968 – 1976, IBM San Jose Research Lab: research in statistical communication theory, queueing theory, computer performance modeling, statistical simulation methodology. • 1976 – present, IBM T.J. Watson Research Center: research in computer performance modeling, queueing networks, statistical simulation methodology, system availability modeling1977-1981, Manager of Performance Modeling Methodology project: research in the analysis and simulation of queueing network models of computer system performance1981-1982, Visiting Professor, UCLA Computer Science Department1982-1995, Senior Manager of Systems Analysis Department: broad research program in computer system measurement, modeling and analysis, as well as in computer system architecture and computer system management; department members included Robert Berry, C-S Chang, Ambuj Goyal, Phil Heidelberger, Joe Hellerstein, Ambuj Goyal, Randy Nelson, Charlie Sauer, Joel Wolf, Philip Yu1995 – present: technical strategy and planning for IBM Research’s worldwide labs, currently in research areas that impact IBM’s software and services businesses; responsible for IBM Research’s Professional Interest Communities in Computer Science and Math, and for the Adventurous Research program

  2. A Few Key Papers • Lassetre, E.R., and Scherr, A.L., Modeling and Performance of the OS/360 Time Sharing Option (TSO), In Statistical Computer Performance Evaluation (W. Freiberger, Editor), Academic Press (1972) • Buzen, J.P., Computational Algorithms for Closed Queueing Networks with Exponential Servers, CACM (1973) • Baskett, F., Chandy, M., Muntz, R.R., and Palacios-Gomez, Open, Closed and Mixed Networks of Queues with Different Classes of Customers, JACM (1975) • Reiser, M. and Kobayashi, H., Queueing Networks with Multiple Closed Chains: Theory and Computational Algorithms, IBM Journal R & D (1975) • Reiser, M. and Lavenberg, S.S., Mean Value Analysis of Closed Multichain Queueing Networks, JACM (1980) • Lavenberg, S.S. and Reiser, M., Stationary State Probabilities at Arrival Instants for Closed Queueing Networks With Multiple Types of Customers, Journal of Applied Probability (1980).

  3. Mean Value Analysis • sj = mean service time of server j, j = 1, … ,J • pjk = probability a customer completing service at server j next visits server k • fj = f1 x p1j + …. + fJ x pJj ,f1 = 1 (fj = relative # visits to server j compared to server 1) • Rj(N) = mean response time of server j with N customers in the network • Lj(N) = mean queue length of server j with N customers in the network • TPj(N) = throughput of server j with N customers in the network = fj x TP1(N) • Li(N) = TPi(N) x Ri(N)(Little’s equation) • N = TP1(N) x R1(N) + … + TPJ(N) x RJ(N) • = TP1(N) x [R1(N) + (f2 x R2(N) + … + fJ x RJ(N)] • => • TP1(N) = N/[R1(N) + f2 x R2(N) + … + fJ x RJ(N)] • Ri(N) = si x [Li(N-1) + 1] (MVA recursion)

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