Abstract. The paper presents a survey of out-of-core methods avail-able for the analysis of large Markov chains on single workstations. First, we discuss the main sparse matrix storage schemes and review iterative methods for the solution of systems of linear equations typically used in disk-based methods. Next, various out-of-core approaches for the steady state solution of CTMCs are described. In this context, serial out-of-core algorithms are outlined and analysed with the help of their imple-mentations. A comparison of time and memory requirements for typical benchmark models is given.
AbstractThis paper presents algorithms and experimental results for model-checking continuous-time M...
We present new algorithms for the solution of large structured Markov models whose infinitesimal gen...
This thesis addresses problems which arise during performance evaluation of parallel and distributed...
Computer systems are ubiquitous in almost all spheres of our life, motivat-ing the need for them to ...
AbstractStochastic modeling formalisms such as stochastic Petri nets, generalized stochastic Petri n...
In recent years, disk-based approaches to the analysis of Markov models have proved to be an effecti...
Purpose – Markov chains and queuing theory are widely used analysis, optimization and decision-makin...
In this paper we present data structures and distributed algorithms for CSL model checking-based per...
Stochastic performance models provide a formal way of capturing and analysing the complex dynamic be...
One of the roadblocks to greater application of Markov chains is that non-numerically sophisticated ...
Reliability and dependability modeling can be employed during many stages of analysis of a computing...
Continuous time Markov chains (CTMCs) are among the most fundamental mathematical structures used fo...
AbstractDespite considerable effort, the state-space explosion problem remains an issue in the analy...
Reliability and dependability modeling can be employed during many stages of analysis of a computing...
This dissertation concerns analytical methods for assessing the performance of concurrent systems. M...
AbstractThis paper presents algorithms and experimental results for model-checking continuous-time M...
We present new algorithms for the solution of large structured Markov models whose infinitesimal gen...
This thesis addresses problems which arise during performance evaluation of parallel and distributed...
Computer systems are ubiquitous in almost all spheres of our life, motivat-ing the need for them to ...
AbstractStochastic modeling formalisms such as stochastic Petri nets, generalized stochastic Petri n...
In recent years, disk-based approaches to the analysis of Markov models have proved to be an effecti...
Purpose – Markov chains and queuing theory are widely used analysis, optimization and decision-makin...
In this paper we present data structures and distributed algorithms for CSL model checking-based per...
Stochastic performance models provide a formal way of capturing and analysing the complex dynamic be...
One of the roadblocks to greater application of Markov chains is that non-numerically sophisticated ...
Reliability and dependability modeling can be employed during many stages of analysis of a computing...
Continuous time Markov chains (CTMCs) are among the most fundamental mathematical structures used fo...
AbstractDespite considerable effort, the state-space explosion problem remains an issue in the analy...
Reliability and dependability modeling can be employed during many stages of analysis of a computing...
This dissertation concerns analytical methods for assessing the performance of concurrent systems. M...
AbstractThis paper presents algorithms and experimental results for model-checking continuous-time M...
We present new algorithms for the solution of large structured Markov models whose infinitesimal gen...
This thesis addresses problems which arise during performance evaluation of parallel and distributed...