Name
Abstract | ||
|---|---|---|
When analyzing real life stochastic systems in most cases is easier, cheaper and more effective to use analytical models rather than studying the physical system or a simulation model of it. The stochastic modeling is a powerful tool that helps the analysis and optimization of stochastic systems. However the use of stochastic modeling is not widely spread in today's industries and among practitioners. This lack of acceptance is caused by two main reasons the first being the curse of dimensionality, which is defined by the number of states required to describe a system. This number grows exponentially as the size of the system increases. The second reason is the lack of user-friendly and efficient software packages that allow the modeling of the problem without involving the user with the implementation of the solution algorithms to solve it. The curse of dimensionality is a constant problem that has been addressed by different approaches through time, but it is not intended within the scope of our work; our focus is on the latter issue. We propose a generic solver that enables the user to focus on modeling without getting involved in the complexity required by the solution methods. We design an object oriented framework for stochastic modeling with four components namely, jMarkov which models Markov Chains, jQBD which models Quasi Birth and Death Processes, jPhase which models Phase Types Distributions and jMDP which models Markov Decision Processes. We concentrate all our effort on creating a software that allows the user to model any kind of system like a Markov Chain, QBD or MDP with fairly basic knowledge of programming. To this end we separate the modeling part from the solution algorithms; therefore the user only needs to mathematically model the problem and the software will do the rest. However, we leave the package with the possibility that experienced users can code their own solution algorithms; this is done since the package only contains the most common algorithms found in the literature. The software does not use external plain files like '.txt' or '.dat' written with specific commands, but rather it is based on OOP (Object Oriented Programming). The main advantages of it include implementation in Java framework, which allows the computational representation of the model to be very similar to its mathematical representation such that it would become natural to pass from one to another. Also the program possesses the usual characteristics of Java such as the use of inheritance and abstraction. Finally, Java is a high level computational language so the user doesn't need to be concerned about technical problems. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2185395.2185439 | SIGMETRICS Performance Evaluation Review |
Keywords | Field | DocType |
stochastic modeling,models quasi birth,experienced user,stochastic system,stochastic modeling tool,modeling part,models markov chains,models phase types distributions,solution algorithm,real life stochastic system,models markov decision processes,jmarkov package,object oriented programming,simulation model,curse of dimensionality,phase type distribution,markov chain,stochastic model,markov decision process,mathematical model | Java collections framework,Object-oriented programming,Markov model,Computer science,Markov chain,Markov decision process,Theoretical computer science,Software,Solver,Java | Journal |
Volume | Issue | Citations |
39 | 4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Cote | 1 | 0 | 0.34 |
| German Riano | 2 | 8 | 1.76 |
| Raha Akhavan-Tabatabaei | 3 | 66 | 11.78 |
| Juan F. Pérez | 4 | 106 | 11.80 |
| Andres Sarmiento | 5 | 2 | 1.04 |
| Julio Goez | 6 | 0 | 0.34 |