Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data
File(s)
Accepted version
Author(s)
Wang, W
Casale, G
Ajay Kattepur
Manoj Nambiar
Type
Conference Paper
Abstract
Resource demand estimation is essential for the application of analyical models, such as queueing networks, to real-world systems. In this paper, we investigate maximum likelihood (ML) estimators for service demands in closed queueing networks with load-independent and load-dependent service times. Stemming from a characterization of necessary conditions for ML estimation, we propose new estimators that infer demands from queue-length measurements, which are inexpensive metrics to collect in real systems. One advantage of focusing on queue-length data compared to response times or utilizations is that confidence intervals can be rigorously derived from the equilibrium distribution of the queueing network model. Our estimators and their confidence intervals are validated against simulation and real system measurements for a multi-tier application.
Date Issued
2016-03-12
Date Acceptance
2015-11-17
Citation
Proceedings of the 7th ACM/SPEC on International Conference on Performance Engineering, 2016, pp.3-14
ISBN
978-1-4503-4080-9
Publisher
ACM
Start Page
3
End Page
14
Journal / Book Title
Proceedings of the 7th ACM/SPEC on International Conference on Performance Engineering
Copyright Statement
© 2016 ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record is available at http://dx.doi.org/10.1145/2851553.2851565
Sponsor
Commission of the European Communities
Grant Number
644869
Source
7th ACM/SPEC International Conference on Performance Engineering
Publication Status
Published
Start Date
2016-03-12
Finish Date
2016-03-18
Coverage Spatial
Delft