Input Modeling with Phase-Type Distributions and Markov Models

Theory and Applications

Nonfiction, Science & Nature, Mathematics, Applied, Statistics
Cover of the book Input Modeling with Phase-Type Distributions and Markov Models by Peter Buchholz, Jan Kriege, Iryna Felko, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Peter Buchholz, Jan Kriege, Iryna Felko ISBN: 9783319066745
Publisher: Springer International Publishing Publication: May 20, 2014
Imprint: Springer Language: English
Author: Peter Buchholz, Jan Kriege, Iryna Felko
ISBN: 9783319066745
Publisher: Springer International Publishing
Publication: May 20, 2014
Imprint: Springer
Language: English

Containing a summary of several recent results on Markov-based input modeling in a coherent notation, this book introduces and compares algorithms for parameter fitting and gives an overview of available software tools in the area. Due to progress made in recent years with respect to new algorithms to generate PH distributions and Markovian arrival processes from measured data, the models outlined are useful alternatives to other distributions or stochastic processes used for input modeling. Graduate students and researchers in applied probability, operations research and computer science along with practitioners using simulation or analytical models for performance analysis and capacity planning will find the unified notation and up-to-date results presented useful. Input modeling is the key step in model based system analysis to adequately describe the load of a system using stochastic models.

The goal of input modeling is to find a stochastic model to describe a sequence of measurements from a real system to model for example the inter-arrival times of packets in a computer network or failure times of components in a manufacturing plant. Typical application areas are performance and dependability analysis of computer systems, communication networks, logistics or manufacturing systems but also the analysis of biological or chemical reaction networks and similar problems. Often the measured values have a high variability and are correlated. It’s been known for a long time that Markov based models like phase type distributions or Markovian arrival processes are very general and allow one to capture even complex behaviors. However, the parameterization of these models results often in a complex and non-linear optimization problem. Only recently, several new results about the modeling capabilities of Markov based models and algorithms to fit the parameters of those models have been published.​

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Containing a summary of several recent results on Markov-based input modeling in a coherent notation, this book introduces and compares algorithms for parameter fitting and gives an overview of available software tools in the area. Due to progress made in recent years with respect to new algorithms to generate PH distributions and Markovian arrival processes from measured data, the models outlined are useful alternatives to other distributions or stochastic processes used for input modeling. Graduate students and researchers in applied probability, operations research and computer science along with practitioners using simulation or analytical models for performance analysis and capacity planning will find the unified notation and up-to-date results presented useful. Input modeling is the key step in model based system analysis to adequately describe the load of a system using stochastic models.

The goal of input modeling is to find a stochastic model to describe a sequence of measurements from a real system to model for example the inter-arrival times of packets in a computer network or failure times of components in a manufacturing plant. Typical application areas are performance and dependability analysis of computer systems, communication networks, logistics or manufacturing systems but also the analysis of biological or chemical reaction networks and similar problems. Often the measured values have a high variability and are correlated. It’s been known for a long time that Markov based models like phase type distributions or Markovian arrival processes are very general and allow one to capture even complex behaviors. However, the parameterization of these models results often in a complex and non-linear optimization problem. Only recently, several new results about the modeling capabilities of Markov based models and algorithms to fit the parameters of those models have been published.​

More books from Springer International Publishing

Cover of the book Regulation of Heat Shock Protein Responses by Peter Buchholz, Jan Kriege, Iryna Felko
Cover of the book Future Solar Energy Devices by Peter Buchholz, Jan Kriege, Iryna Felko
Cover of the book Psychiatry and Neuroscience Update by Peter Buchholz, Jan Kriege, Iryna Felko
Cover of the book The Bioarchaeology of Societal Collapse and Regeneration in Ancient Peru by Peter Buchholz, Jan Kriege, Iryna Felko
Cover of the book Memory and the Wars on Terror by Peter Buchholz, Jan Kriege, Iryna Felko
Cover of the book Interdisciplinary Studies in Pragmatics, Culture and Society by Peter Buchholz, Jan Kriege, Iryna Felko
Cover of the book Regenerative Medicine - from Protocol to Patient by Peter Buchholz, Jan Kriege, Iryna Felko
Cover of the book Spintronics-based Computing by Peter Buchholz, Jan Kriege, Iryna Felko
Cover of the book Innovations in Big Data Mining and Embedded Knowledge by Peter Buchholz, Jan Kriege, Iryna Felko
Cover of the book Value-ology by Peter Buchholz, Jan Kriege, Iryna Felko
Cover of the book The Physical Exam by Peter Buchholz, Jan Kriege, Iryna Felko
Cover of the book Applied Cryptography and Network Security by Peter Buchholz, Jan Kriege, Iryna Felko
Cover of the book History and Politics of Well-Being in Europe by Peter Buchholz, Jan Kriege, Iryna Felko
Cover of the book Computational Methods and Clinical Applications in Musculoskeletal Imaging by Peter Buchholz, Jan Kriege, Iryna Felko
Cover of the book Open Data for Education by Peter Buchholz, Jan Kriege, Iryna Felko
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy