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An Approach of SLA Violation Predict...

Agarwal, Saurav.

An Approach of SLA Violation Prediction and QoS Optimization Using Regression Machine Learning Techniques.

Record Type:	Electronic resources : Monograph/item
Title/Author:	An Approach of SLA Violation Prediction and QoS Optimization Using Regression Machine Learning Techniques.
Author:	Agarwal, Saurav.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2020
Description:	93 p.
Notes:	Source: Masters Abstracts International, Volume: 82-01.
Notes:	Advisor: Yuan, Xiaobu.
Contained By:	Masters Abstracts International82-01.
Subject:	Computer science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27964233
ISBN:	9798641088105

An Approach of SLA Violation Prediction and QoS Optimization Using Regression Machine Learning Techniques.
Agarwal, Saurav.

An Approach of SLA Violation Prediction and QoS Optimization Using Regression Machine Learning Techniques. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 93 p.

Source: Masters Abstracts International, Volume: 82-01.

Thesis (M.Sc.)--University of Windsor (Canada), 2020.

This item must not be sold to any third party vendors.

Along with the acceptance of Service-Oriented Architecture (SOA) as a promising style of software design, the role that Quality of Service (QoS) plays in the success of SOA-based software systems has become much more significant than ever before. When QoS is documented as a Service-Level Agreement (SLA), it specifies the commitment between a service provider and a client, as well as monetary penalties in case of any SLA violations. To avoid and reduce the situations that may cause SLA violations, service providers need tools to intuitively analyze if their service design provokes SLA violations and to automatically guide them preventing SLA violations. Due to the dynamic nature of service interaction during the operation of SOA-based software systems, the avoidance of SLA violations requires prompt detection of potential violations before prevention takes place at real-time. To overcome the low latency time in practice, this thesis research develops an approach of using Machine Learning techniques to not only predict SLA violations but also prevent them by means of optimization. This research discusses the algorithm and framework, along with the results of the experiments, which will help to examine its usefulness for service providers working on the construction and refinement of services.

ISBN: 9798641088105Subjects--Topical Terms:

199325
Computer science.
Subjects--Index Terms:

Machine learning

An Approach of SLA Violation Prediction and QoS Optimization Using Regression Machine Learning Techniques.
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