HyperStar: Phase-Type Fitting Made Easy

Reinecke, Philipp; Krauß, Tilman; Wolter, Katinka

doi:10.1109/qest.2012.29

Cited by 29 publications

(25 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerical PH fitting, e.g. with the use of Expectation Maximisation Algorithms, is a frequently investigated problem [2], and various tools exist, HyperStar [20], which we have chosen, is reported to be efficient at fitting spikes as in case of our distribution. Figure 1 presents the cumulative distribution function (cdf) of IP packet lengths obtained from this trace and its approximation with the use of an hyperErlang distribution having three Erlang distributions with a variable number of phases, up to 3000.…”

Section: Distribution Of Ip Packetsmentioning

confidence: 99%

“…Here we introduce to a Markov model details which were previously reserved to simulation models: a real distribution of IP packets and self-similar nature of packet flows. To obtain numerical results we use standard software: HyperStar [20] to approximate measured distributions with phase-type ones, enabling the use of Markov chains and Prism [11] to study transient states of a complex Markov model. We use also existing Markovian models of selfsimilar traffic [1].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Delays in IP Routers, a Markov Model

Czachórski

Domański²,

Domańska

et al. 2016

Communications in Computer and Information Science

View full text Add to dashboard Cite

Abstract. Delays in routers are an important component of end-to-end delay and therefore have a significant impact on quality of service. While the other component, the propagation time, is easy to predict as the distance divided by the speed of light inside the link, the queueing delays of packets inside routers depend on the current, usually dynamically changing congestion and on the stochastic features of the flows. We use a Markov model taking into account the distribution of the size of packets and self-similarity of incoming flows to investigate their impact on the queueing delays and their dynamics.

show abstract

Section: Distribution Of Ip Packetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Delays in IP Routers, a Markov Model

Czachórski

Domański²,

Domańska

et al. 2016

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…The software tool HyperStar [13] combines an efficient parameter fitting algorithm for phase-type distributions with a user-friendly interface. After choosing the dataset, the user is asked to visually identify peaks in the data, which would then roughly correspond to the means of the individual Erlang branches.…”

Section: Hyperstarmentioning

confidence: 99%

Patch-based Modelling of City-centre Bus Movement with Phase-type Distributions

Reijsbergen

Gilmore

Hillston

2015

Electronic Notes in Theoretical Computer Science

View full text Add to dashboard Cite

We propose a methodology for constructing a stochastic performance model of a public transportation network using real-world data. Our main data source consists of Automatic Vehicle Location (AVL) measurements of buses in the Edinburgh region. Although the data has a relatively low frequency, we can use it to parameterise a model in which a bus moves between predefined patches in the city. We fit the probability distributions of the sojourn times in the patches to phase-type distributions using the tool HyperStar. We then translate the output from HyperStar to a model of a complete part of a bus route expressed in the reactive modules language of the PRISM model checker. Finally, we demonstrate how we can use the numerical techniques implemented in PRISM to answer meaningful questions about the performance of the bus network in the context of a case study involving the addition of trams to a busy section of Edinburgh's city centre.

show abstract

“…In the second step, the processing characteristics of the services must be captured and modelled as phase-type distributions, which will then be included as the service-time distributions in the model. Adequate service-time distributions would typically be obtained by measuring processing times and fitting phase-type distributions to the samples, using one of several well-known tools [13,17,15]. The impact of restart can then be evaluated in the third step using Equation 15.…”

Section: Applicationsmentioning

confidence: 99%

“…Adequate service-time distributions would typically be obtained by measuring processing times and fitting phase-type distributions to the samples, using one of several well-known tools [13,17,15]. The impact of restart can then be evaluated in the third step using Equation 15. For instance, the designer or operator of the system may be interested in whether the system will be stable under restart, and for which range of restart intervals stability holds.…”

Section: Applicationsmentioning

confidence: 99%

Multiple class G-networks with restart

Fourneau

Wolter

Reinecke

et al. 2013

Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering

Self Cite

View full text Add to dashboard Cite

Restart is a common technique for improving response-times in complex systems where the causes of delays can either not be discerned, or not be addressed by the user. With restart, the user aborts a running job that exceeds a deadline, and resubmits it to the system immediately. In many common scenarios, this approach can reduce the response-times that the user experiences. Restart has been well-studied for scenarios where only one user applies restart, and typically in cases where queueing effects can be neglected. In this paper we approach the question of restart in a scenario where restart is applied by many users in a system that can be modelled as an open queueing network. We apply the GNetworks formalism to this problem. We use negative customers to model the abortion and retry of a request. The open G-network uses multiple classes with phase-type distributed service times. This allows the approximation of a preemptive repeat different behaviour as it is natural for multiple restarts of a request. We compute the response time of a request and show that an optimal restart interval can be found. The results are compared with simulation.

show abstract

HyperStar: Phase-Type Fitting Made Easy

Cited by 29 publications

References 10 publications

Delays in IP Routers, a Markov Model

Delays in IP Routers, a Markov Model

Patch-based Modelling of City-centre Bus Movement with Phase-type Distributions

Multiple class G-networks with restart

Contact Info

Product

Resources

About