Correlating resource demand information with ARM data for application services
Proceedings of the 1st international workshop on Software and performance
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
The Use of Optimal Filters to Track Parameters of Performance Models
QEST '05 Proceedings of the Second International Conference on the Quantitative Evaluation of Systems
Detecting performance anomalies in global applications
WORLDS'05 Proceedings of the 2nd conference on Real, Large Distributed Systems - Volume 2
Exploiting nonstationarity for performance prediction
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Performance impacts of autocorrelated flows in multi-tiered systems
Performance Evaluation
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
Performance Model Estimation and Tracking Using Optimal Filters
IEEE Transactions on Software Engineering
The Art of Capacity Planning: Scaling Web Resources
The Art of Capacity Planning: Scaling Web Resources
Automated anomaly detection and performance modeling of enterprise applications
ACM Transactions on Computer Systems (TOCS)
Linear grouping using orthogonal regression
Computational Statistics & Data Analysis
Performance criteria and measurement for a time-sharing system
IBM Systems Journal
Real-time performance modeling for adaptive software systems
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
Service time estimation with a refinement enhanced hybrid clustering algorithm
ASMTA'10 Proceedings of the 17th international conference on Analytical and stochastic modeling techniques and applications
Integrated estimation and tracking of performance model parameters with autoregressive trends
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Tracking adaptive performance models using dynamic clustering of user classes
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Achieving application-centric performance targets via consolidation on multicores: myth or reality?
Proceedings of the 21st international symposium on High-Performance Parallel and Distributed Computing
On the complexity of locating linear facilities in the plane
Operations Research Letters
DEC: Service Demand Estimation with Confidence
IEEE Transactions on Software Engineering
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According to the utilization law, throughput and utilization are linearly related and their measurements can be used for the indirect estimation of service demands. In practice, however, hardware and software modifications as well as non-modeled loads due to periodic maintenance activities make the estimation process difficult and often impossible without manual intervention to analyze the data. Due to configuration changes, real world datasets show that workload and utilization measurements tend to group themselves into multiple linear clusters. To estimate the service demands of the underlying performance models, the different configurations have to be identified. In this paper, we present an algorithm that, exploiting the timestamps associated with each throughput and utilization observation, identifies the different configurations of the system and estimates the corresponding service demands. Our proposal is based on robust estimation and inference techniques and is therefore suitable to analyze contaminated datasets. Moreover, not only sudden and occasional changes of the system, but also recurring patterns in the system's behavior, due for instance to scheduled maintenance tasks, are detected. An efficient implementation of the algorithm has been made publicly available and, in this paper, its performance is assessed on synthetic as well as on experimental data.