A case for redundant arrays of inexpensive disks (RAID)
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Importance sampling for stochastic simulations
Management Science
A Unified Framework for Simulating Markovian Models of Highly Dependable Systems
IEEE Transactions on Computers
Importance sampling for the simulation of highly reliable Markovian systems
Management Science
Fast simulation of rare events in queueing and reliability models
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Fast simulation of packet loss rates in a shared buffer communications switch
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Fast simulation of networks of queues with effective and decoupling bandwidths
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Nearly optimal importance sampling for Monte Carlo simulation of loss systems
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Estimation of blocking probabilities in cellular networks with dynamic channel assignment
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue: Rare event simulation
Multiservice Loss Models for Broadband Telecommunication Networks
Multiservice Loss Models for Broadband Telecommunication Networks
Multilevel Splitting for Estimating Rare Event Probabilities
Operations Research
Fast simulation of wavelength continuous WDM networks
IEEE/ACM Transactions on Networking (TON)
An adaptive approach to fast simulation of traffic groomed optical networks
WSC '04 Proceedings of the 36th conference on Winter simulation
Parallel optical interconnects for enterprise class server clusters: needs and technology solutions
IEEE Communications Magazine
Quick simulation: a review of importance sampling techniques in communications systems
IEEE Journal on Selected Areas in Communications
Traffic grooming in WDM networks: past and future
IEEE Network: The Magazine of Global Internetworking
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We motivate and describe improved fast simulation techniques for the accelerated performance evaluation of highly available services. In systems that provide such services, service unavailability events are rare due to a low component failure rate or high resource capacity. Using traditional Monte Carlo simulation to evaluate such services requires a large amount of runtime. Importance sampling (IS) has been applied to certain instances of such systems, focusing on single-class and/or homogeneous resource demands. In this article, we formulate highly available services as multiresource losstype systems, and we present two IS methods for fast simulation, extending to multiple classes and nonhomogeneous resource demands. First, for the cases in which component failure rates are small, we prove that static IS using the Standard Clock (S-ISSC) method exhibits the bounded relative error (BRE) property. Second, for estimating failure probabilities due to large capacity or fast service in systems that have nonrare component failure rates, we propose adaptive ISSC (A-ISSC), which estimates the relative probability of reaching each possible state of system failure in every step of the simulation. Using A-ISSC, IS methods which are proven to be efficient can be extended to multidimensional cases, while still retaining a very favorable performance, as supported by our validation experiments.