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.
Translated title of the contribution | QD-AMVA: Evaluación de sistemas con requisitos de servicio dependientes de la cola |
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Original language | English (US) |
Article number | 1821 |
Pages (from-to) | 80-98 |
Number of pages | 19 |
Journal | Performance Evaluation |
Volume | 91 |
DOIs | |
State | Published - Sep 1 2015 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Software
- Modeling and Simulation
- Hardware and Architecture
- Computer Networks and Communications