Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
New computer methods for global optimization
New computer methods for global optimization
Mean value analysis for queueing network models with intervals as input parameters
Performance Evaluation
Performance evaluation of an enterprise JavaBean server implementation
Proceedings of the 2nd international workshop on Software and performance
Interval parameters for capturing uncertainties in an EJB performance model
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Performance Modelling of Communication Networks and Computer Architectures (International Computer S
Performance Modelling of Communication Networks and Computer Architectures (International Computer S
Interval Arithmetic for Computing Performance Guarantees in Client-Server Software
ICCI '91 Proceedings of the International Conference on Computing and Information: Advances in Computing and Information
An Architecture for Distributed Enterprise Data Mining
HPCN Europe '99 Proceedings of the 7th International Conference on High-Performance Computing and Networking
Interval-Based Performance Analysis of Computing Systems
MASCOTS '95 Proceedings of the 3rd International Workshop on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
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During early phases of design and implementation, not all the parameter values of a performance model are usually known exactly. In related research contributions, intervals have been proposed as a means to capture parameter uncertainties. Existing model solution algorithms can be adapted to interval parameters by replacing conventional arithmetic by interval arithmetic. However, the so-called dependency problem may cause extremely wide intervals for the computed performance measures. Interval splitting has been proposed as a technique to overcome this problem. In this work, we give an overview of existing splitting algorithms and propose the use of a selective splitting method that significantly reduces the computational complexity of interval evaluations. Moreover, the exploitation of partial monotonicity properties to further decrease the computational complexity is discussed. The proposed methods are illustrated along the lines of two examples: a small performance model of the multiple access with collision avoidance by invitation (MACA-BI) protocol for ad hoc wireless mobile networks and a more complex model of an Enterprise JavaBeans (EJB) server implementation.