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QoS-Aware Self-Adapting Resource Utilisation Framework for Distributed Stream Management Systems

Authors:
Tarjana Yagnik
Feng Chen
Laleh Kasraian

Keywords: Data streams management; data stream processing; quality of service; QoS; resource allocation; prediction; scheduling

Abstract:
The last decade witnessed plenty of Big Data processing and applications including the utilisation of machine learning algorithms and techniques. Such data need to be analysed under specific Quality of Service (QoS) constraints for certain critical applications. Many frameworks have been proposed for QoS management and resource allocation for the various Distributed Stream Management Systems (DSMS), but lack the capability of dynamic adaptation to fluctuations in input data rates. This paper presents a novel QoS-Aware, Self-Adaptive, Resource Utilisation framework for Data Streams, which utilises instantaneous reactions with proactive actions. This research focuses on the load monitoring and analysis parts of the framework. By applying real-time analytics on performance and QoS metrics, the predictive models can assist in adjusting resource allocation strategies. The experiments were conducted to collect the various metrics and analyse them to reduce their dimensions and identify the most influential ones regarding the QoS and resource allocation schemes.

Pages: 1 to 9

Copyright: Copyright (c) IARIA, 2021

Publication date: April 18, 2021

Published in: conference

ISSN: 2519-8386

ISBN: 978-1-61208-842-6

Location: Porto, Portugal

Dates: from April 18, 2021 to April 22, 2021