Rethinking the Use of Models in Software Architecture
QoSA '08 Proceedings of the 4th International Conference on Quality of Software-Architectures: Models and Architectures
OPEDo: a tool for the optimization of performance and dependability models
ACM SIGMETRICS Performance Evaluation Review
The qnetworks toolbox: a software package for queueing networks analysis
ASMTA'10 Proceedings of the 17th international conference on Analytical and stochastic modeling techniques and applications
Stochastic comparisons for rooted butterfly networks and tree networks, with random environments
Information Sciences: an International Journal
Dynamic aspects and behaviors of complex systems in performance and reliability assessment
ACM SIGMETRICS Performance Evaluation Review
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We propose the Geometric Bounds (GB), a new family of fast and accurate non-iterative bounds on closed queueing network performance metrics that can be used in the on-line optimization of distributed applications. Compared to state-of-the-art techniques such as the Balanced Job Bounds (BJB), the GB achieve higher accuracy at similar computational costs, limiting the worst-case bounding error typically within 5%-13% when for the BJB it is usually in the range 15%-35%. Optimization problems that are solved with the GB bounds return solutions that are much closer to the global optimum than with existing bounds. We also show that the GB technique generalizes as an accurate approximation to closed fork-join networks commonly used in disk, parallel and database models, thus extending the applicability of the method beyond the optimization of basic product-form networks.