Performance/availability modeling with the TANGRAM-II modeling environment
Performance Evaluation - Special issue on tools for performance evaluation
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
An integrated mobility and traffic model for resource allocation in wireless networks
WOWMOM '00 Proceedings of the 3rd ACM international workshop on Wireless mobile multimedia
Hidden Markov modeling for network communication channels
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Continuous-time hidden Markov models for network performance evaluation
Performance Evaluation
HMM profiles for network traffic classification
Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security
Predicting packet loss statistics with hidden Markov models
ACM SIGMETRICS Performance Evaluation Review
Early application identification
CoNEXT '06 Proceedings of the 2006 ACM CoNEXT conference
Internet traffic modeling by means of Hidden Markov Models
Computer Networks: The International Journal of Computer and Telecommunications Networking
A large-scale hidden semi-Markov model for anomaly detection on user browsing behaviors
IEEE/ACM Transactions on Networking (TON)
The TANGRAMII integrated modeling environment for computer systems and networks
ACM SIGMETRICS Performance Evaluation Review
Applications of machine learning to performance evaluation
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
| Hi-index | 0.00 |
The use of hidden Markov models (HMMs) has found widespread use in many different areas. This chapter focuses on HMMs applied to the performance evaluation of computer systems and networks. After presenting a brief review of background material on HMMs, applications such as channel delay and loss characteristics, traffic modeling and workload generation are surveyed. The power of HMMs as predictors of performance metrics is also highlighted. We conclude by presenting a few features of the module of the Tangram-II performance evaluation tool that is targeted to HMMs.