QD-AMVA: Evaluating Systems with Queue-Dependent Service Requirements
File(s)
Published version
Author(s)
Casale, G
Perez Bernal, J
Wang, W
Type
Journal 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 dependencies are challenging to analyze, a fact that discourages practitioners from characterizing
workload dependencies. 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.
requests running at a resource and their mean service requirements. However, multiclass performance models that
feature these dependencies are challenging to analyze, a fact that discourages practitioners from characterizing
workload dependencies. 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.
Date Issued
2015-07-02
Date Acceptance
2015-06-21
Citation
Performance Evaluation, 2015, 91, pp.80-98
ISSN
0166-5316
Publisher
Elsevier
Start Page
80
End Page
98
Journal / Book Title
Performance Evaluation
Volume
91
Copyright Statement
© 2015 The Authors. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
License URL
Publication Status
Published