Capacity planning for Web performance: metrics, models, and methods
Capacity planning for Web performance: metrics, models, and methods
Correlating resource demand information with ARM data for application services
Proceedings of the 1st international workshop on Software and performance
Parameter estimation for performance models of distributed application systems
CASCON '95 Proceedings of the 1995 conference of the Centre for Advanced Studies on Collaborative research
Queueing Networks and Markov Chains
Queueing Networks and Markov Chains
Tracking time-varying parameters in software systems with extended Kalman filters
CASCON '05 Proceedings of the 2005 conference of the Centre for Advanced Studies on Collaborative research
Parameter inference of queueing models for IT systems using end-to-end measurements
Performance Evaluation
Java Modelling Tools: an Open Source Suite for Queueing Network Modelling andWorkload Analysis
QEST '06 Proceedings of the 3rd international conference on the Quantitative Evaluation of Systems
Measuring CPU overhead for I/O processing in the Xen virtual machine monitor
ATEC '05 Proceedings of the annual conference on USENIX Annual Technical Conference
Using magpie for request extraction and workload modelling
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
Robust Workload Estimation in Queueing Network Performance Models
PDP '08 Proceedings of the 16th Euromicro Conference on Parallel, Distributed and Network-Based Processing (PDP 2008)
CPU demand for web serving: Measurement analysis and dynamic estimation
Performance Evaluation
Automatic request categorization in internet services
ACM SIGMETRICS Performance Evaluation Review
Tracking adaptive performance models using dynamic clustering of user classes
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
MODE: mix driven on-line resource demand estimation
Proceedings of the 7th International Conference on Network and Services Management
Statistical inference of software performance models for parametric performance completions
QoSA'10 Proceedings of the 6th international conference on Quality of Software Architectures: research into Practice - Reality and Gaps
Efficient experiment selection in automated software performance evaluations
EPEW'11 Proceedings of the 8th European conference on Computer Performance Engineering
Systematic adoption of genetic programming for deriving software performance curves
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
Integrating software performance curves with the palladio component model
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications
Constructing performance model of JMS middleware platform
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
Performance models of storage contention in cloud environments
Software and Systems Modeling (SoSyM)
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We propose a linear regression method and a maximum likelihood technique for estimating the service demands of requests based on measurement of their response times instead of their CPU utilization. Our approach does not require server instrumentation or sampling, thus simplifying the parameterization of performance models. The benefit of this approach is further highlighted when utilization measurement is difficult or unreliable, such as in virtualized systems or for services controlled by third parties. Both experimental results from an industrial ERP system and sensitivity analyses based on simulations indicate that the proposed methods are often much more effective for service demand estimation than popular utilization based linear regression methods. In particular, the maximum likelihood approach is found to be typically two to five times more accurate than utilization based regression, thus suggesting that estimating service demands from response times can help in improving performance model parameterization.