Fundamentals of queueing theory (2nd ed.).
Fundamentals of queueing theory (2nd ed.).
Workload Service Requirements Analysis: A Queueing Network Optimization Approach
MASCOTS '02 Proceedings of the 10th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Queues with Workload-Dependent Arrival and Service Rates
Queueing Systems: Theory and Applications
Analytic modeling of multitier Internet applications
ACM Transactions on the Web (TWEB)
Performance modeling and system management for multi-component online services
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
Exploiting nonstationarity for performance prediction
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
A real-time adaptive control of autonomic computing environments
CASCON '07 Proceedings of the 2007 conference of the center for advanced studies on Collaborative research
CPU demand for web serving: Measurement analysis and dynamic estimation
Performance Evaluation
Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities
Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities
Performance management for cluster-based web services
IEEE Journal on Selected Areas in Communications
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
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Performance modeling of software systems is vital for predictive analysis of their performance and capacity planning of the host environment. Robust performance prediction and efficient capacity planning highly depend on an accurate estimation of the underlying model parameters. AMBIENCE, which is a prototype tool developed at IBM Research, makes use of the powerful Inferencing technique to generate a workload-independent parameters based performance model. However, modern software systems are quite complex in design and may exhibit variable service times and overheads at changing workloads. In this work, we extend the Inferencing technique for generating workload-dependent service time and CPU overhead based performance models. We call this extended form as Enhanced Inferencing. Implementation of Enhanced Inferencing in AMBIENCE shows significant improvement of the order of 26 times over Inferencing. We further present a case study where Enhanced Inferencing provides a quantitative performance difference between consolidated and partitioned software system installations. Ability to carry out such evaluations can have significant impact on capacity planning of software systems that are characterized by workload-dependent model parameters.