Class Based Weighted Fair Queueing (CBWFQ) is a very important router discipline that allows different types of Internet Protocol (IP) traffic like voice, video, and best effort data to receive the required quality of service measures they individually need. CBWFQ dynamically allocates the available bandwidth to each traffic class based on the class's weight. This discipline is playing a vital role as IP brings these traffic classes together in a truly converged network. Under stress and in extreme emergencies, it is critical to be able to determine how the CBWFQ discipline will perform. In this paper, we present and discuss the critical role simulation has played in our development of performance analysis tools for the CBWFQ discipline.
As a result of potential damage to our national infrastructure due to cyber attacks, a number of cybersecurity bills have been introduced in Congress and a National Strategy for Trusted Identities in Cyberspace has been developed by the White House; a component of this strategy is the development of models to assess risks due to cyber incidents. A worm attack on a network is one type of attack that is possible. The simulation of rare events, such as the occurrence of a catastrophic worm attack, is impractical without special simulation techniques. In this paper we present an application of splitting methods to estimate rare-event probabilities associated with the propagation of a worm through a network. We explore the sensitivity of the benefits of splitting methods, as compared to standard simulation, to the rarity of the event and the level function used.
This paper presents research toward generalizing the optimization of the allocation of simulation replications to an arbitrary number of designs, when the problem is to maximize the Probability of Correct Selection among designs, the best design being the one with the smallest probability of a rare event. The simulation technique within each design is an optimized version of the splitting method. An earlier work solved this problem for the special case of two designs. In this paper an alternative two-stage approach is examined in which, at the first stage, allocations are made to the designs by a modified version of the Optimal Computing Budget Allocation. At the second stage the allocation among the splitting levels within each design is optimized. Our approach is shown to work well on a two-tandem queuing model. INTRODUCTIONIt is well known that standard Monte Carlo (MC) techniques do not efficiently estimate the probability of rare events. Their inadequacy is exacerbated when simulations, subject to a computational budget constraint, are made over several designs, for the purpose of selecting the design with the smallest rare event probability. This paper explores methods to improve performance by optimizing the allocation of the computational budget among designs and, within each design, among the levels of a fixed effort splitting technique. The computational budget is defined in terms of time. The problem of allocating this time optimally, in order to maximize the probability of correct selection (the probability of selecting the "best" design, however best is defined) is addressed by the Optimal Computing Budget Constraint (OCBA) (Chen et al. 2000;He et al. 2007;Fu et al. 2007). OCBA implicitly assumes that standard MC is the simulation technique used within each design. It thus suffers, in the context of rare events, from the inefficiencies of MC. Applying OCBA, by itself, to optimize the allocation of the budget among designs offers only slight improvement over equal budget allocations, when MC is used to estimate rare event probabilities within the designs.There are several approaches to the problem of efficiently estimating rare events, within a single system or design. L'Ecuyer, Demers, and Tuffin (2006), L'Ecuyer et al. (2009), and Asmussen and Glynn 3998 978-1-4577-2109-0/11/$26.00 ©2011 IEEE
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