LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Casale, Giuliano; Sansottera, Andrea; Cremonesi, Paolo (2016)
Publisher: Elsevier BV
Languages: English
Types: Article
Subjects: Business & Economics, 2ND-ORDER, VOICE, MD Multidisciplinary, CHAINS, ARRIVAL PROCESSES, Technology, Management Science and Operations Research, Information Systems and Management, Counting process, Marked Markov-modulated Poisson process, Trace, Operations Research & Management Science, PERFORMANCE, Science & Technology, Management, Social Sciences, Operations Research, Modelling and Simulation, Fitting, QUEUE
Markov-modulated Poisson processes (MMPPs) are stochastic models for fitting empirical traces for simulation, workload characterization and queueing analysis purposes. In this paper, we develop the first counting process fitting algorithm for the marked MMPP (M3PP), a generalization of the MMPP for modeling traces with events of multiple types. We initially explain how to fit two-state M3PPs to empirical traces of counts. We then propose a novel form of composition, called interposition, which enables the approximate superposition of several two-state M3PPs without incurring into state space explosion. Compared to exact superposition, where the state space grows exponentially in the number of composed processes, in interposition the state space grows linearly in the number of composed M3PPs. Experimental results indicate that the proposed interposition methodology provides accurate results against artificial and real-world traces, with a significantly smaller state space than superposed processes.
  • No references.
  • No related research data.
  • No similar publications.

Share - Bookmark

Published in

Funded by projects

  • EC | DICE

Cite this article