The paper explores the performance dynamics of a tandem queueing system (TQS) comprising two interconnected nodes and assesses its responsiveness under varying service conditions. By using simulation tools, we conducted a range of tests to discover the variability of waiting times in the queues and the system, for different server utilization, as well as for cases when the service times follow exponential and normal distributions. We discovered the interplay between the nodes, i.e., how the performance of one node affects the other within the same TQS. Namely, we showed how the waiting times in the queues are crucial in understanding the TQS dynamics. We also compared the simulation results with the well-established analytical models and discovered that their applicability is limited. Particularly noteworthy was the disparity observed in the performance estimation of the second node within the TQS across all simulations, indicating potential over- or underestimation by analytical models.

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Impact of Service Time Distributions and Server Utilization on Tandem Queueing System Performance

  • Marko Matulin,
  • Štefica Mrvelj,
  • Luka Čop

摘要

The paper explores the performance dynamics of a tandem queueing system (TQS) comprising two interconnected nodes and assesses its responsiveness under varying service conditions. By using simulation tools, we conducted a range of tests to discover the variability of waiting times in the queues and the system, for different server utilization, as well as for cases when the service times follow exponential and normal distributions. We discovered the interplay between the nodes, i.e., how the performance of one node affects the other within the same TQS. Namely, we showed how the waiting times in the queues are crucial in understanding the TQS dynamics. We also compared the simulation results with the well-established analytical models and discovered that their applicability is limited. Particularly noteworthy was the disparity observed in the performance estimation of the second node within the TQS across all simulations, indicating potential over- or underestimation by analytical models.