Product-Form Queueing Networks

MVA, convolution, and normalizing constant methods.

The pfqn module contains algorithms for product-form queueing networks, including Mean Value Analysis (MVA), convolution, and normalizing constant methods.

Key function categories:

• Mean Value Analysis: pfqn_mva(), pfqn_mvald(), pfqn_mvams(), pfqn_mvamx()

• Normalizing constants: pfqn_nc(), pfqn_ncld(), pfqn_fnc()

• Convolution algorithms: pfqn_ca(), pfqn_bs(), pfqn_comom()

• Linearizer methods: pfqn_linearizer(), pfqn_linearizerms(), pfqn_gflinearizer()

• Bounds and approximations: pfqn_qzgblow(), pfqn_qzgbup(), pfqn_xzabalow(), pfqn_xzabaup()

• LCFS queueing networks: pfqn_lcfsqn_mva(), pfqn_lcfsqn_nc(), pfqn_lcfsqn_ca()

• Maximum entropy: me_oqn() - Maximum Entropy for open queueing networks

Product-form queueing network (PFQN) algorithms.

Native Python implementations of analytical algorithms for product-form queueing networks, including Mean Value Analysis (MVA), normalizing constant methods, and various approximation techniques.

Key algorithms:

pfqn_mva: Standard Mean Value Analysis pfqn_ca: Convolution Algorithm pfqn_nc: Normalizing Constant methods pfqn_bs: Balanced System analysis pfqn_aql: Approximate queue lengths