Class Lossn_links

java.lang.Object
jline.api.lossn.Lossn_links

public final class Lossn_links extends Object
Link-level performance measures of a product-form loss network.
  • Method Details

    • lossn_links

      public static Lossn_links.LossnLinksResult lossn_links(Matrix nu, Matrix A, Matrix C)
      Per-link blocking and occupancy by the exact Manjunath-Sikdar transform.
      Parameters:
      nu - offered load of route r (1xR)
      A - capacity requirement of link j for route r (JxR)
      C - capacity of link j (Jx1)
      Returns:
      E, Occ, Loss and QLen
    • lossn_links

      public static Lossn_links.LossnLinksResult lossn_links(Matrix nu, Matrix A, Matrix C, String method)
      Per-link blocking and occupancy with the given method.
      Parameters:
      nu - offered load of route r (1xR)
      A - capacity requirement of link j for route r (JxR)
      C - capacity of link j (Jx1)
      method - 'exact' (default), 'rec', 'erlangfp', 'asy' or 'mci'
      Returns:
      E, Occ, Loss and QLen
    • lossn_links

      public static Lossn_links.LossnLinksResult lossn_links(Matrix nuVec, Matrix Amat, Matrix cVec, String method, Lossn_mci.Options options)
      Link-level performance measures of a product-form loss network.

      For a loss network with offered loads nu, requirement matrix A and capacities C (A n <= C), returns for every link j and route r the probability E(j,r) that link j alone lacks the A(j,r) free units a route-r call needs, the mean occupancy of each link, and the route losses. With the exact methods the link blocking is

            E(j,r) = 1 - G(C - A(j,r) e_j) / G(C),
       
      where G is the product-form normalization constant and e_j the j-th unit vector, computed from one extra constant per distinct (j, A(j,r)) pair. A route-r call is lost when ANY of its links blocks, so Loss_r is at least max_j E(j,r). The mean occupancy of link j is sum_r A(j,r) QLen_r.

      Supported methods:

      • 'exact' (or 'manjunath', 'ms'): Manjunath-style exact recursion
      • 'rec': recursive decision diagram (Kirby-Wang style) normalization
      • 'erlangfp': Erlang fixed point; E is the per-link Kaufman-Roberts blocking on the reduced loads
      • 'asy': Kelly limit; E(j,r) = 1 - (1-a_j)^A(j,r)
      • 'mci': Monte Carlo summation; E from the same samples as Loss

      Parameters:
      nuVec - offered load of route r (1xR)
      Amat - capacity requirement of link j for route r (JxR)
      cVec - capacity of link j (Jx1)
      method - 'exact' (default when null or empty), 'rec', 'erlangfp', 'asy' or 'mci'
      options - Lossn_mci options (samples, seed, alpha, gamma), used by 'mci'; may be null
      Returns:
      E, Occ, Loss and QLen