Phase-Type Fitting Using HyperStar

Reinecke, Philipp; Krauß, Tilman; Wolter, Katinka

doi:10.1007/978-3-642-40725-3_13

Cited by 16 publications

(8 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The function HTMODEL from Algorithm 1 performs the holding-time modelling for state s ′ i of the CTMC(S ′ , π ′ , R ′ ) model from Theorem 2. HTMODEL applies Reinecke et al's cluster-based PHD fitting approach [32], [33], [34] to the holding times sample i (10) (lines 2-16), uses this PHD to derive the parameters of the refined CTMC(S ′′ , π ′′ , R ′′ ) (lines [17][18][19][20], and returns this CTMC in line 21.…”

Section: B Stage Ii: Holding-time Modellingmentioning

confidence: 99%

Accurate Analysis of Quality Properties of Software with Observation-Based Markov Chain Refinement

Paterson

Călinescu

2017

2017 IEEE International Conference on Software Architecture (ICSA)

View full text Add to dashboard Cite

We introduce a tool-supported method for the automated refinement of continuous-time Markov chains (CTMCs) used to assess quality properties of component-based software. Existing research focuses on improving the efficiency of CTMC analysis and on identifying new applications for this analysis. As such, ensuring that the analysis is accurate by using CTMCs that closely model the behaviour of the analysed software has received relatively little attention. Our new method addresses this gap by refining the high-level CTMC model of a component-based software system based on observations of the execution times of its components. Our refinement method reduced analysis errors by 77-90.3% for a service-based system implemented using six public web services from three different providers, improving the accuracy of the analysis and significantly reducing the risk of invalid software engineering decisions.

show abstract

Section: B Stage Ii: Holding-time Modellingmentioning

confidence: 99%

Accurate Analysis of Quality Properties of Software with Observation-Based Markov Chain Refinement

Paterson

Călinescu

2017

2017 IEEE International Conference on Software Architecture (ICSA)

View full text Add to dashboard Cite

show abstract

“…Expolynomials permit the representation of common distributions (e.g., Erlang, uniform) and enable a variety of approaches to fit data (e.g., moments [70], shape [45], [57]) obtained in different manners (e.g., estimated from real measurements, synthetically generated, derived from system specification). For instance, in the example of software rejuvenation, we assume that the failure time has been repeatedly observed, obtaining measurements lower than x 1 = 72 h (3 days), x 2 = 144 h (6 days), and x 3 = 216 h (9 days) with frequency p 1 = 0.001, p 2 = 0.006, and p 3 = 0.016, respectively; and, with frequency p 4 = 0.984, measurements larger than x 3 , for which the mean value was 672 h (28 days).…”

Section: Deriving the Stochastic Parameters Of The Modelmentioning

confidence: 99%

The ORIS Tool: Quantitative Evaluation of Non-Markovian Systems

Paolieri

Biagi

Carnevali

et al. 2021

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

We present the next generation of ORIS, a toolbox for quantitative evaluation of concurrent models with non-Markovian timers. The tool shifts its focus from timed models to stochastic ones, it includes a new graphical user interface, new analysis methods and a Java Application Programming Interface (API). Models can be specified as Stochastic Time Petri Nets (STPNs) through the graphical editor, validated using an interactive token game, and analyzed through several techniques to compute instantaneous or cumulative rewards. STPNs can also be exported as Java code to conduct extensive parametric studies through the Java library, now distributed as open-source. A well-engineered software architecture allows the user to implement new features for STPNs, new modeling formalisms, and new analysis methods. The most distinctive features of ORIS include transient and steady-state analysis of STPNs modeling Markov Regenerative Processes (MRPs), and transient analysis of STPNs modeling generalized semi-Markov processes. ORIS also supports state-space analysis of Time Petri Nets (TPNs), simulation of STPNs, and standard analysis techniques for continuous-time Markov chains or MRPs with at most one non-exponential timer in each state. We illustrate the general workflow for the application of ORIS to the modeling and evaluation of non-functional requirements of software-intensive systems.

show abstract

“…HyperStar [8] is a software tool for fitting phase-type distributions to data sets. Phase-type distributions have the desirable property that they can approximate any distribution with non-negative support arbitrarily closely.…”

Section: Fitting Phase-type Distributions With Hyperstarmentioning

confidence: 99%

Finding Optimal Timetables for Edinburgh Bus Routes

Vissat¹,

Clark²,

Gilmore³

2015

Electronic Notes in Theoretical Computer Science

View full text Add to dashboard Cite

We present a novel application of stochastic simulation and model-checking methods to determining whether bus services are fulfilling their service-level agreement to provide on-time departures of buses from stops sufficiently often. We use open data on predicted bus arrival times to parameterise a stochastic model of a particular bus route from Edinburgh city centre out to suburban and rural areas to the south of the city. We validate and then analyse our stochastic model using both simulation and model-checking methods. Finally, we complete an optimisation study on the model and discover a better timetable for the service which would expose the bus service operator to less financial risk of penalties being applied by the regulatory authorities which define standards for bus service, punctuality and reliability.

show abstract

Phase-Type Fitting Using HyperStar

Cited by 16 publications

References 7 publications

Accurate Analysis of Quality Properties of Software with Observation-Based Markov Chain Refinement

Accurate Analysis of Quality Properties of Software with Observation-Based Markov Chain Refinement

The ORIS Tool: Quantitative Evaluation of Non-Markovian Systems

Finding Optimal Timetables for Edinburgh Bus Routes

Contact Info

Product

Resources

About