Package jline.examples.java.advanced

Class FCRegionModel

  • All Implemented Interfaces:
    
public class FCRegionModel

    Examples of models with Finite Capacity Regions (FCR). FCRs allow defining capacity constraints across multiple nodes, with either blocking (wait) or dropping behavior when full.

    • Nested Class Summary

      Nested Classes 
      Modifier and Type Class Description

    • Field Summary

      Fields 
      Modifier and Type Field Description

    • Constructor Summary

      Constructors 
      Constructor Description
      FCRegionModel()

    • Enum Constant Summary

      Enum Constants 
      Enum Constant Description

    • Method Summary

      Modifier and Type Method Description
      static Network fcr_oqnwaitq() Multiclass open network with FCR blocking.
      static Network fcr_oqndrop() Multiclass open network with FCR dropping.
      static Network fcr_mm1waitq() Simple M/M/1 with FCR blocking.
      static Network mm1() Simple M/M/1 without FCR (for comparison).
      static Network fcr_mm1kdrop() M/M/1/K with FCR dropping (K=2).
      static Network mm1k() M/M/1/K using queue capacity (K=2, for comparison).
      static Network fcr_constraints() FCR with multiple constraint types demonstration.
      static Network fcr_lossn() Loss network with FCR (for NC solver lossn method).

      • Methods inherited from class java.lang.Object

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

    • Constructor Detail

      • FCRegionModel

        FCRegionModel()

    • Method Detail

      • fcr_oqnwaitq

         static Network fcr_oqnwaitq()

        Multiclass open network with FCR blocking.

        Features: - Two open classes with different arrival/service rates - Two queues covered by a single FCR - Global max: 8 jobs, Class1 max: 5, Class2 max: 4 - Blocking behavior: jobs wait when region is full

        Returns:

        configured FCR blocking model

      • fcr_oqndrop

         static Network fcr_oqndrop()

        Multiclass open network with FCR dropping.

        Features: - Two open classes with different arrival/service rates - Two queues covered by a single FCR - Global max: 8 jobs, Class1 max: 5, Class2 max: 4 - Dropping behavior: jobs are lost when region is full

        Returns:

        configured FCR dropping model

      • fcr_mm1waitq

         static Network fcr_mm1waitq()

        Simple M/M/1 with FCR blocking.

        This model demonstrates that FCR with blocking around a single queue behaves identically to a standard M/M/1 queue.

        Returns:

        configured M/M/1 with FCR blocking

      • mm1

         static Network mm1()

        Simple M/M/1 without FCR (for comparison).

        Returns:

        standard M/M/1 model

      • fcr_mm1kdrop

         static Network fcr_mm1kdrop()

        M/M/1/K with FCR dropping (K=2).

        This model demonstrates that FCR with dropping around a single queue behaves like an M/M/1/K queue where K is the FCR capacity.

        Returns:

        configured M/M/1/K with FCR dropping

      • mm1k

         static Network mm1k()

        M/M/1/K using queue capacity (K=2, for comparison).

        Returns:

        standard M/M/1/K model

      • fcr_constraints

         static Network fcr_constraints()

        FCR with multiple constraint types demonstration.

        Features: - Global max jobs: limits total jobs across all classes - Per-class max jobs: limits jobs of a specific class - Different drop rules per class: blocking vs dropping

        Returns:

        configured FCR model with multiple constraint types

      • fcr_lossn

         static Network fcr_lossn()

        Loss network with FCR (for NC solver lossn method).

        This model demonstrates the NC solver's ability to analyze open loss networks using the Erlang fixed-point approximation. Features: - Single Delay node (infinite server) inside an FCR - Multiple classes with different arrival/service rates - DROP policy: jobs are lost when region is full

        The NC solver can analytically solve this using the lossn method.

        Returns:

        configured loss network model