research-article

HyperStar2: Easy Distribution Fitting of Correlated Data

Authors:

Zhihao Shang,

Tianhui Meng,

Katinka WolterAuthors Info & Claims

ICPE '17: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering

Pages 139 - 142

https://doi.org/10.1145/3030207.3030243

Published: 17 April 2017 Publication History

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Abstract

In this paper, we present HyperStar2, a tool for fitting Markov Arrival Processes (MAPs) to empirical data. HyperStar2 uses a two-step approach, where the first step is cluster-based fitting of phase-type distributions and the second step is the construction of a correlation matrix. In the first step, we use the cluster-based algorithm for Hyper-Erlang distribution fitting from HyperStar hyper2012. Based on the Hyper-Erlang fitting result and the clusters of samples, in the second step we construct the correlation matrix.

The tool targets engineers and scientists who need distribution fitting for non-standard distributions but have little interest in the underlying theory. Therefore the tool has a GUI that offers graphical presentation of the data, the fitted distribution and the empirical as well as theoretical autocorrelation. We discuss the use of HyperStar2 in common application scenarios and the fitting algorithms behind it.

We provide some numerical examples, which show the abilities and limits of the fitting tool. We find that HyperStar2 can fit distributions very well but for some auto-correlation structures ProFiDo provides better results for auto-correlation.

References

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Cited By

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Bause FBlume ABuchholz PPuzicha ATimmermann A(2024)Adaption of Stochastic Models (ASMo) - A Tool for Input Modeling -Quantitative Evaluation of Systems and Formal Modeling and Analysis of Timed Systems10.1007/978-3-031-68416-6_5(72-89)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-68416-6_5
Wu HShang ZPeng GWolter K(2020)A Reactive Batching Strategy of Apache Kafka for Reliable Stream Processing in Real-time2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE5003.2020.00028(207-217)Online publication date: Oct-2020
https://doi.org/10.1109/ISSRE5003.2020.00028
Wu HShang ZWolter K(2019)Performance Prediction for the Apache Kafka Messaging System2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)10.1109/HPCC/SmartCity/DSS.2019.00036(154-161)Online publication date: Aug-2019
https://doi.org/10.1109/HPCC/SmartCity/DSS.2019.00036

Index Terms

HyperStar2: Easy Distribution Fitting of Correlated Data
1. Mathematics of computing
  1. Probability and statistics
    1. Stochastic processes
      1. Markov processes

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Published In

ICPE '17: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering

April 2017

450 pages

ISBN:9781450344043

DOI:10.1145/3030207

General Chairs:
Walter Binder
Università della Svizzera Italiana, Switzerland
,
Vittorio Cortellessa
Università dell'Aquila, Italy
,
Program Chairs:
Anne Koziolek
Karlsruhe Institute of Technology, Germany
,
Evgenia Smirni
College of William & Mary, USA
,
Meikel Poess
Oracle Corporation, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 April 2017

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ICPE '17

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ICPE '17: ACM/SPEC International Conference on Performance Engineering

April 22 - 26, 2017

L'Aquila, Italy

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ICPE '17 Paper Acceptance Rate 27 of 83 submissions, 33%;

Overall Acceptance Rate 252 of 851 submissions, 30%

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Cited By

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Bause FBlume ABuchholz PPuzicha ATimmermann A(2024)Adaption of Stochastic Models (ASMo) - A Tool for Input Modeling -Quantitative Evaluation of Systems and Formal Modeling and Analysis of Timed Systems10.1007/978-3-031-68416-6_5(72-89)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-68416-6_5
Wu HShang ZPeng GWolter K(2020)A Reactive Batching Strategy of Apache Kafka for Reliable Stream Processing in Real-time2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE5003.2020.00028(207-217)Online publication date: Oct-2020
https://doi.org/10.1109/ISSRE5003.2020.00028
Wu HShang ZWolter K(2019)Performance Prediction for the Apache Kafka Messaging System2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)10.1109/HPCC/SmartCity/DSS.2019.00036(154-161)Online publication date: Aug-2019
https://doi.org/10.1109/HPCC/SmartCity/DSS.2019.00036

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HyperStar: Phase-Type Fitting Made Easy

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