Proceedings of the 8th International Workshop on Adaptive and Reflective MIddleware 2009
DOI: 10.1145/1658185.1658189
|View full text |Cite
|
Sign up to set email alerts
|

Using machine learning to maintain pub/sub system QoS in dynamic environments

Abstract: Quality-of-service (QoS)-enabled publish/subscribe (pub/sub) middleware provides powerful support for scalable data dissemination. It is hard, however, to maintain specified QoS properties (such as reliability and latency) in dynamic environments (such as disaster relief operations or power grids). For example, managing QoS manually is often not feasible in dynamic systems due to (1) slow human response times, (2) the complexity of managing multiple interrelated QoS settings, and (3) the scale of the systems b… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
8
0

Year Published

2010
2010
2022
2022

Publication Types

Select...
4
1
1

Relationship

0
6

Authors

Journals

citations
Cited by 8 publications
(8 citation statements)
references
References 9 publications
0
8
0
Order By: Relevance
“…Hoffert et al. [34] used machine learning methods for QoS parameter prediction. For example, neural networks and decision trees are good at protocol classification.…”
Section: Related Workmentioning
confidence: 99%
“…Hoffert et al. [34] used machine learning methods for QoS parameter prediction. For example, neural networks and decision trees are good at protocol classification.…”
Section: Related Workmentioning
confidence: 99%
“…Another proactive approach is presented by Hoffert et al [58] who point out the difficulty in maintaining the Quality of Service (QoS) properties (such as reliability and latency) in dynamic environments such as disaster relief operations or power grids. They state that the challenge arises from the slow human response times, and the complexity of managing multiple interrelated QoS settings.…”
Section: Strategymentioning
confidence: 99%
“…Supervised learning algorithms make predictions based on a set of examples. Classifiers, decision trees, neural networks, and regression are some examples of supervised learning that can be seen in the work of Hoffert et al [62]. Unsupervised learning occurs where labelled examples are not available.…”
Section: Factors That Influence Adaptationmentioning
confidence: 99%
See 1 more Smart Citation
“…For those pub‐sub systems with QoS parameters, Hoffert et al . provide machine learning techniques for configuring the QoS parameters . Behnel et al .…”
Section: Related Workmentioning
confidence: 99%