Designing Process Replication and Activation: A Quantitative Approach
IEEE Transactions on Software Engineering
An Efficient Algorithm for Aggregating PEPA Models
IEEE Transactions on Software Engineering
Dynamic resource allocation for shared data centers using online measurements
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
QoS-Aware Middleware for Web Services Composition
IEEE Transactions on Software Engineering
Model-Based Performance Prediction in Software Development: A Survey
IEEE Transactions on Software Engineering
A Queuing Model for Service Selection of Multi-classes QoS-aware Web Services
ECOWS '05 Proceedings of the Third European Conference on Web Services
Adaptive Service Composition in Flexible Processes
IEEE Transactions on Software Engineering
Performance Model Estimation and Tracking Using Optimal Filters
IEEE Transactions on Software Engineering
Enhanced Modeling and Solution of Layered Queueing Networks
IEEE Transactions on Software Engineering
Performance model driven QoS guarantees and optimization in clouds
CLOUD '09 Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing
ArcheOpterix: An extendable tool for architecture optimization of AADL models
MOMPES '09 Proceedings of the 2009 ICSE Workshop on Model-Based Methodologies for Pervasive and Embedded Software
Qos-driven runtime adaptation of service oriented architectures
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
SALSA: QoS-aware load balancing for autonomous service brokering
Journal of Systems and Software
Performance prediction of web service workflows
QoSA'07 Proceedings of the Quality of software architectures 3rd international conference on Software architectures, components, and applications
Dynamic QoS Management and Optimization in Service-Based Systems
IEEE Transactions on Software Engineering
Model-Based Software Performance Analysis
Model-Based Software Performance Analysis
PerOpteryx: automated application of tactics in multi-objective software architecture optimization
Proceedings of the joint ACM SIGSOFT conference -- QoSA and ACM SIGSOFT symposium -- ISARCS on Quality of software architectures -- QoSA and architecting critical systems -- ISARCS
Energy-aware capacity scaling in virtualized environments with performance guarantees
Performance Evaluation
Fluid Analysis of Queueing in Two-Stage Random Environments
QEST '11 Proceedings of the 2011 Eighth International Conference on Quantitative Evaluation of SysTems
Scalable Differential Analysis of Process Algebra Models
IEEE Transactions on Software Engineering
Energy-Aware Autonomic Resource Allocation in Multitier Virtualized Environments
IEEE Transactions on Services Computing
Fluid Rewards for a Stochastic Process Algebra
IEEE Transactions on Software Engineering
Software Architecture Optimization Methods: A Systematic Literature Review
IEEE Transactions on Software Engineering
A Fluid Model for Layered Queueing Networks
IEEE Transactions on Software Engineering
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When performance characteristics are taken into account in a software design, models can be used to identify optimal configurations of the system's parameters. Unfortunately, for realistic scenarios, the cost of the optimization is typically high, leading to computational difficulties in the exploration of large parameter spaces. This paper proposes an approach to provably exact parameter-space pruning for a class of models of large-scale software systems analyzed with fluid techniques, efficient and scalable deterministic approximations of massively parallel stochastic models. We present a result of monotonicity of fluid solutions with respect to the model parameters, and employ it in the context of optimization programs with evolutionary algorithms by discarding candidate configurations a priori, i.e., without ever solving them, whenever they are proven to give lower fitness than other configurations. An extensive numerical validation shows that this approach yields an average twofold runtime speed-up compared to a baseline optimization algorithm that does not exploit monotonicity. Furthermore, we find that the optimal configuration is within a few percent from the true one obtained by stochastic simulation, whose solution is however orders of magnitude more expensive.