Exploring the impact of iaccuracy and imprecision of QoS assumptions on proactive constraint-based QoS prediction for service orchestrations

Ivanovic, Dragan and Carro Liñares, Manuel and Hermenegildo, Manuel V. (2012). Exploring the impact of iaccuracy and imprecision of QoS assumptions on proactive constraint-based QoS prediction for service orchestrations. In: "4th International Workshop on Principles of Engineering Service-Oriented Systems (PESOS)", 04 Jun 2012, Zurich. ISBN 978-1-4673-1755-9. pp. 29-35.

Description

Title: Exploring the impact of iaccuracy and imprecision of QoS assumptions on proactive constraint-based QoS prediction for service orchestrations
Author/s:
  • Ivanovic, Dragan
  • Carro Liñares, Manuel
  • Hermenegildo, Manuel V.
Item Type: Presentation at Congress or Conference (Article)
Event Title: 4th International Workshop on Principles of Engineering Service-Oriented Systems (PESOS)
Event Dates: 04 Jun 2012
Event Location: Zurich
Title of Book: 2012 4th International Workshop on Principles of Engineering Service-Oriented Systems (PESOS)
Date: 2012
ISBN: 978-1-4673-1755-9
Subjects:
Freetext Keywords: Service orchestration; Quality of service; Prediction; Constraints; Quality of prediction; Experimental validation
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Constraint-based Quality of Service (QoS) prediction is a method for predicting violations of Service Level Agreements (SLAs) in an executing instance of a service orchestration. It uses assumptions about the ranges of QoS values for component services in the orchestration. Experiments suggest that the method, when given correct component QoS assumptions, produces highly accurate predictions according to a series of quality-of-prediction metrics, and that it does so well ahead of the time when the prediction is to happen. We study the behavior of this method when the component QoS assumptions become incorrect or too vague. We conclude that the effect is a graceful deterioration in prediction quality, unless gross (order-of-magnitude) imprecisions are introduced. However, the method is very sensitive to the loss of information on the lower bounds for component QoS values, since the knowledge of the upper bounds is not sufficient for failure prediction

More information

Item ID: 52877
DC Identifier: http://oa.upm.es/52877/
OAI Identifier: oai:oa.upm.es:52877
Official URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6225936
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 30 Oct 2018 13:56
Last Modified: 30 Oct 2018 13:58
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