A comparative study of parallel and sequential priority queue algorithms
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A comparison of simulation event list algorithms
Communications of the ACM
SS '99 Proceedings of the Thirty-Second Annual Simulation Symposium
Queue-based method for efficient simulation of biological self-assembly systems
Journal of Computational Physics
Short note: O(N) implementation of the fast marching algorithm
Journal of Computational Physics
P-tree structures and event horizon: efficient event-set implementations
WSC '05 Proceedings of the 37th conference on Winter simulation
NS-2 TCP-Linux: an NS-2 TCP implementation with congestion control algorithms from Linux
WNS2 '06 Proceeding from the 2006 workshop on ns-2: the IP network simulator
WNS2 '06 Proceeding from the 2006 workshop on ns-2: the IP network simulator
ICCOM'08 Proceedings of the 12th WSEAS international conference on Communications
The fix-point method for discrete events simulation using SQL and UDF
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
A model reduction approach for improving discrete event simulation performance
Proceedings of the 6th International ICST Conference on Simulation Tools and Techniques
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Discrete event simulations often require a future event list structure to manage events according to their timestamp. The choice of an efficient data structure is vital to the performance of discrete event simulations as 40% of the time may be spent on its management. A Calendar Queue (CQ) or Dynamic Calendar Queue (DCQ) are two data structures that offers O(1) complexity regardless of the future event list size. CQ is known to perform poorly over skewed event distributions or when event distribution changes. DCQ improves on the CQ structure by detecting such scenarios in order to redistribute events. Both CQ and DCQ determine their operating parameters (bucket widths) by sampling events. However, sampling technique will fail if the samples do not accurately reflect the inter-event gap size. This paper presents a novel and alternative approach for determining the optimum operating parameter of a calendar queue based on performance statistics. Stress testing of the new calendar queue, henceforth referred to as the Statistically eNhanced with Optimum Operating Parameter Calendar Queue (SNOOPy CQ), with widely varying and severely skewed event arrival scenarios show that SNOOPy CQ offers a consistent O(1) performance and can execute up to 100 times faster than DCQ and CQ in certain scenarios.