Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
A decomposition approach for stochastic reward net models
Performance Evaluation
Solutions of large and non-Markovian performance models
Solutions of large and non-Markovian performance models
The MVA priority approximation
ACM Transactions on Computer Systems (TOCS)
An Introduction to Operating Systems
An Introduction to Operating Systems
SPNP: Stochastic Petri Net Package
PNPM '89 The Proceedings of the Third International Workshop on Petri Nets and Performance Models
Theory, Volume 1, Queueing Systems
Theory, Volume 1, Queueing Systems
Annals of Software Engineering
State Space Construction and Steady--State Solution of GSPNs on a Shared--Memory Multiprocessor
PNPM '97 Proceedings of the 6th International Workshop on Petri Nets and Performance Models
| Hi-index | 0.00 |
Operating systems which implement a dynamic priority mechanism are very common. Nevertheless, it is very difficult to develop an accurate analytical model to evaluate their performance, mainly due to the different forms of dependency between the various constituent parts. We introduce a black box modeling approach which allows us to identify and decompose the different functions of the operating system into elementary subsystems from which submodels are developed. Interactions between submodels are taken into account while developing the system model. We then apply this modeling technique to the study of operating systems with dynamic priorities. In particular, we investigate the performance of the BS2000 operating system, which implements a dynamic priority mechanism, by means of a stochastic reward net (SRN) model. The results are then compared against an approximate model in which a static priority mechanism is assumed. Real system measurements are also carried out in order to validate the analytical results.