We propose a suite of tests based on two-state Markov chains for experimentally assessing the dynamic performance of a variety of simulation event calendar implementations. in contrast to previous studies based on the standard hold model for evaluation of performance statically, the proposed Markov hold model is more general and can be used to examine how different implementations respond dynamically to dependent sequences of insertion and deletion requests. The Markov hold model is used to conduct tests based on random, stressed, and correlated input sequences of requests, with performance measures including completion times, sensitivity to correlations, sensitivity to duplication, and efficiency of datahandling. We apply these tests to fourteen different event calendar implementations. To demonstrate the utility of the proposed model, we also include a comparison of the event calendar algorithms on a token ring protocol with bursty Markovian packet-traffic.
KEY WORDS Event calendar Markov chain Simulation Hold model Priority queueA simulation which begins with one or a few initially scheduled events ensures that as events occur in time, a sufficient number of new (future) events are generated so that program execution can continue indefinitely. The scheduling mechanism that effects this behavior does so by using a special structure, called the simulation event c~l e n d u r ,~ to store events which are supposed to occur at some future time. These pending events remain in the event calendar, in some data-structure dependent order, until the simulation program decides to extract an event at a time for processing. New events are scheduled and inserted into the calendar according to model specifications, and these are usually generated while some related event is
The Si lightweight‐process based system for simulating process interactions is an enhancement to the C programming languge in the form of library primitives with sets of predefined data structures. The Si system encapsulates an existing lightweight‐process library to provide a discrete‐event simulation environment supporting the process view. It was developed as a research testbed for investigating methods which support simulations efficiently. Easy extensions and modifications to the Si system are a major design objective, accomplished through modularity and layering. This paper describes the system, our experiences with its implementation, and its applicability to simulation modeling. We report on performance measurements of different implementations of the simulation scheduler, and of different algorithms for simulating service disciplines.
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