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

Giuliano Casale, Juan F. Pérez, Weikun Wang

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number1821
Pages (from-to)80-98
Number of pages19
JournalPerformance Evaluation
Volume91
DOIs
StatePublished - Sep 1 2015
Externally publishedYes

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Hardware and Architecture
  • Computer Networks and Communications

Cite this