QD-AMVA: Evaluating systems with queue-dependent service requirements Academic Article


  • Abstract Workload measurements in enterprise systems often lead to observe a dependence between the number of requests running at a resource and their mean service requirements. However, multiclass performance models that feature these dependences are challenging to analyze, a fact that discourages practitioners from characterizing workload dependences. We here focus on closed multiclass queueing networks and introduce QD-AMVA, the first approximate mean-value analysis (AMVA) algorithm that can efficiently and robustly analyze queue-dependent service times in a multiclass setting. A key feature of QD-AMVA is that it operates on mean values, avoiding the computation of state probabilities. This property is an innovative result for state-dependent models, which increases the computational efficiency and numerical robustness of their evaluation. Extensive validation on random examples, a cloud load-balancing case study and comparison with a fluid method and an existing AMVA approximation prove that QD-AMVA is efficient, robust and easy to apply, thus enhancing the tractability of queue-dependent models.

publication date

  • 2015/9/1


  • 91


  • Approximation
  • Closed Queueing Networks
  • Computational Efficiency
  • Computational efficiency
  • Dependent
  • Evaluation
  • Fluid
  • Fluids
  • Industry
  • Load Balancing
  • Mean Value
  • Mean Value Analysis
  • Model
  • Multi-class
  • Multiclass Queueing Networks
  • Performance Model
  • Queue
  • Queueing networks
  • Requirements
  • Resource allocation
  • Resources
  • Robustness
  • Tractability
  • Value engineering
  • Workload

International Standard Serial Number (ISSN)

  • 0166-5316

number of pages

  • 19

start page

  • 80

end page

  • 98