Large-scale heterogeneous service systems with general packing constraints

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A service system with multiple types of customers, arriving according to Poisson processes, is considered. The system is heterogeneous in that the servers can also be of multiple types. Each customer has an independent, exponentially distributed service time, with the mean determined by its type. Multiple customers (possibly of different types) can be placed for service into one server, subject to 'packing' constraints, which depend on the server type. Service times of different customers are independent, even if served simultaneously by the same server. The large-scale asymptotic regime is considered such that the customer arrival rates grow to ∞. We consider two variants of the model. For the infinite-server model, we prove asymptotic optimality of the greedy random (GRAND) algorithm in the sense of minimizing the weighted (by type) number of occupied servers in steady state. (This version of GRAND generalizes that introduced by Stolyar and Zhong (2015) for homogeneous systems, with all servers of the same type.) We then introduce a natural extension of the GRAND algorithm for finite-server systems with blocking. Assuming subcritical system load, we prove existence, uniqueness, and local stability of the large-scale system equilibrium point such that no blocking occurs. This result strongly suggests a conjecture that the steady-state blocking probability under the algorithm vanishes in the large-scale limit.

Original languageEnglish (US)
Pages (from-to)61-83
Number of pages23
JournalAdvances in Applied Probability
Issue number1
StatePublished - Mar 1 2017


  • Queueing network
  • blocking
  • cloud computing
  • fluid limit
  • greedy random (GRAND) algorithm
  • heterogeneous service system
  • loss
  • packing constraint
  • stochastic bin packing

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics


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