SNOOPy Calendar Queue

Abstract

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. The 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.