Package jline.api.qsys

Class Qsys_mginfKt

  • All Implemented Interfaces:
    
public final class Qsys_mginfKt

    • Nested Class Summary

      Nested Classes 
      Modifier and Type Class Description

    • Field Summary

      Fields 
      Modifier and Type Field Description

    • Constructor Summary

      Constructors 
      Constructor Description

    • Enum Constant Summary

      Enum Constants 
      Enum Constant Description

    • Method Summary

      Modifier and Type Method Description
      final static HashMap<String, Object> qsys_mginf(Double lambda, Double mu, Double cv2) M/G/∞ queue analysis (infinite servers)Computes exact performance measures for the M/G/∞ queueing system with unlimited servers.
      final static HashMap<String, Object> qsys_mginf(Double lambda, Double mu, Double cv2, Integer k) M/G/∞ queue analysis with state probability

      • Methods inherited from class java.lang.Object

        clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    • Constructor Detail

    • Method Detail

      • qsys_mginf

         final static HashMap<String, Object> qsys_mginf(Double lambda, Double mu, Double cv2)

        M/G/∞ queue analysis (infinite servers)

        Computes exact performance measures for the M/G/∞ queueing system with unlimited servers. This represents a pure delay system where customers never wait in queue.

        Parameters:
        lambda - arrival rate
        mu - service rate per server
        cv2 - squared coefficient of variation of service time (for consistency, not used in calculations)
        Returns:

        HashMap containing: - L: average number of customers in system - Lq: average number of customers in queue (always 0) - W: average time in system (= service time) - Wq: average waiting time in queue (always 0) - p0: probability of empty system

      • qsys_mginf

         final static HashMap<String, Object> qsys_mginf(Double lambda, Double mu, Double cv2, Integer k)

        M/G/∞ queue analysis with state probability

        Parameters:
        lambda - arrival rate
        mu - service rate per server
        cv2 - squared coefficient of variation of service time
        k - specific state for probability computation
        Returns:

        HashMap containing all metrics plus pk (probability of k customers)