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)
IEEE/ACM Transactions on Networking (TON)
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
Call blocking probabilities in a traffic-groomed tandem optical network
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: In memroy of Olga Casals
Fast simulation of wavelength continuous WDM networks
IEEE/ACM Transactions on Networking (TON)
An introduction to optical burst switching
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
An adaptive approach to accelerated evaluation of highly available services
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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In this paper, we consider the fast simulation of traffic groomed optical networks, in which multiple sub-rate traffic streams may be carried on the same wavelength. For real-sized systems, call blocking probabilities may be rare events and require a large amount of CPU power to simulate. We present two importance sampling methods for fast simulation. For a light-load case, we prove that static IS using the Standard Clock (S-ISSC) method does indeed have bounded relative error (BRE) even in multi-class case. For a non-light-load case, we suggest, alternatively, adaptive ISSC (A-ISSC) which calculates the relative possibility of reaching each target in every step of simulation. Using A-ISSC, biasing methods which are proven to be optimal or have bounded error can be extended to multi-dimensional cases while still retaining a very favorable performance.