Title: Reliability and Availability Modeling in Practice
Non-state-space solution methods are often used to solve reliability block diagrams, fault trees and reliability graphs. Relatively efficient solution algorithms are known to handle systems with hundreds of components and have been implemented in many software packages. Nevertheless many practical problems cannot be handled by such algorithms. Bounding algorithms are then used in such cases as was done for a major subsystem of Boeing 787. Non-state-space methods derive their efficiency from the independence assumption that is often violated in practice. State space methods based on Markov chains, stochastic Petri nets, semi-Markov and Markov regenerative processes can be used to capture various kinds of dependencies among system components. However, the resulting state space explosion severely restricts the size of the problem that can be solved. Hierarchical and fixed-point iterative methods provide scalable alternatives that combine the strengths of state space and non-state-space methods and have been extensively used to solve real-life problems. We will take a journey through these model types via interesting examples.
Kishor Trivedi holds the Hudson Chair in the Department of Electrical and Computer Engineering at Duke University, Durham, NC. He has a B.Tech. (EE, 1968) from IIT Mumbai, M.S. (CS, 1972) and PhD (CS, 1974) from the University of Illinois, Urbana-Champaign. He has been on the Duke faculty since 1975. He is the author of a well-known text entitled, Probability and Statistics with Reliability, Queuing and Computer Science Applications, first published by Prentice-Hall; a thoroughly revised second edition (including its Indian edition) of this book has been published by John Wiley. This book has been recently translated into Chinese. He is a Fellow of the Institute of Electrical and Electronics Engineers. He is a Golden Core Member of IEEE Computer Society. He has published over 600 articles and has supervised 46 Ph.D. dissertations. He is the recipient of IEEE Computer Society Technical Achievement Award for his research on Software Aging and Rejuvenation. His research interests in are in reliability, availability, performance, performability and survivability modeling of computer and communication systems. He works closely with industry in carrying our modeling studies, providing short courses and in the development and dissemination of software packages such as SHARPE and SPNP. His URL is www.ee.duke.edu/~ktrivedi
Title: Mobile Cloud Application Partitioning in Dynamic and Multi-User Environments
Mobile cloud computing (MCC) has emerged as a new paradigm of cloud computing. It offers great opportunities for mobile service industry, allowing mobile devices to access the applications and utilize the elastic resources offered by the cloud. By using MCC technologies, developers can create advanced applications on mobile devices, such as multimedia applications, and augmented reality, which far more exceed the capability of the devices. In this talk, I focus on application partitioning and offloading which is to decide whether each part of the application should be offloaded to the cloud or executed on mobile devices.
There exists new challenges in mobile-cloud computation partitioning. From the mobile side, the partitioning of applications should dynamically change with the user’s mobile environments which may vary due to the user’s mobility. From the cloud side, computation partitioning should support multi-user applications to gain profit through realizing the economic of scale. I will describe a systematic approach to support application partitioning in dynamic and multi-user mobile cloud computing environments and techniques for performance optimization and adaptation.
Dr. Cao is currently a chair professor and head of the Department of Computing at Hong Kong Polytechnic University, Hung Hom, Hong Kong. His research interests include parallel and distributed computing, computer networks, mobile and pervasive computing, fault tolerance, and middleware. He has co-authored 3 books, co-edited 9 books, and published over 300 papers in major international journals and conference proceedings. He is a fellow of IEEE, a senior member of China Computer Federation, and a member of ACM. He was the Chair of the Technical Committee on Distributed Computing of IEEE Computer Society from 2012 - 2014. Dr. Cao has served as an associate editor and a member of the editorial boards of many international journals, including ACM Transactions on Sensor Networks, IEEE Transacitons on Computers, IEEE Transactions on Parallel and Distributed Systems, IEEE Networks, Pervasive and Mobile Computing Journal, and Peer-to-Peer Networking and Applications. He has also served as a chair and member of organizing / program committees for many international conferences, including PERCOM, INFOCOM, ICDCS, IPDPS, ICPP, RTSS, DSN, ICNP, SRDS, MASS, PRDC, ICC, GLOBECOM, and WCNC.
Dr. Cao received the BSc degree in computer science from Nanjing University, Nanjing, China, and the MSc and the Ph.D degrees in computer science from Washington State University, Pullman, WA, USA.
Title: Supercanonical convergence rates in the simulation of Markov chains
Consider a discrete-event model (or a Markov chain) that evolves over several time steps, with a state-dependent cost at each step. The goal is to estimate the expected cost (or perhaps the entire distribution of the cost), either at a given step number, or on average over all steps. For a majority of real-life applications, these quantities are too dicult to compute exactly and must be estimated by simulation. The standard Monte Carlo approach is to simulate n independent sample paths of the chain and take the average over those paths. For the mean cost, this gives an unbiased estimator whose error converges as O(n -1/2), which means that one must multiply n by 100 to get one additional decimal digit of accuracy. When estimating the density of the cost instead of just its mean, the convergence rate is even slower. In this talk, we discuss an approach named Array-RQMC which can provide faster con-vergence rates. The main idea is to simulate the n copies of the chain together and move them forward by using a randomized quasi-Monte Carlo (RQMC) point set of cardinality n at each step, after sorting the n copies in a particular (multidimensional) order. We review Array-RQMC, its variants, sorting strategies, and convergence results. We summarize known convergence rate results and show empirical results that suggest much better convergence rates than those that are proved. We also compare dierent types of multivariate sorts to match the chains with the RQMC points.
Pierre L'Ecuyer is a Professor in the Département d'Informatique et de Recherche Opérationnelle at the Université de Montréal, since 1990. He holds the Canada Research Chair in Stochastic Simulation and Optimization since 2004 and an Inria International Chair (at Inria-Rennes, France) for 2013-2018. He was a professor in the Département d'Informatique at Université Laval (Québec) from 1983 to 1990. He is a member of the CIRRELT and GERAD research centers, in Montreal.
He has published 260 scientific articles, book chapters, and books in various areas, including random number generation, quasi-Monte Carlo methods, efficiency improvement in simulation, sensitivity analysis and optimization for discrete-event simulation models, simulation software, stochastic dynamic programming, and applications in finance, manufacturing, telecommunications, reliability, and service center management. He also developed software libraries and systems for the theoretical and empirical analysis of random number generators and quasi-Monte Carlo point sets, and for general discrete-event simulation.
He has been a referee for 140 different scientific journals, past Editor-in-Chief for the ACM Transactions on Modeling and Computer Simulation, and is currently Associate Editor for ACM Transactions on Mathematical Software, Statistics and Computing, and International Transactions in Operational Research.
He obtained the Canadian Operational Research Society Award of Merit in 2014, the INFORMS Simulation Society Distinguished Service Award in 2011, the INFORMS Simulation Society Outstanding Research Publication Award twice, in 1999 and 2009, a Killam Research Fellowship in 2001-03, the Urgel-Archambault Prize from ACFAS in 2002, Steacie Fellowship from NSERC-Canada in 1995-97, and was elected INFORMS Fellow in 2006.
He is a competitive cyclist in road racing, with four titles of Canadian champion and eleven titles of Québec Champion.