Dynamic QoS optimization architecture for cloud-based DDDAS

Chen, T. ORCID: 0000-0001-5025-5472, Bahsoon, R. and Theodoropoulos, G., 2013. Dynamic QoS optimization architecture for cloud-based DDDAS. Procedia Computer Science, 18, pp. 1881-1890. ISSN 1877-0509

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Abstract

Cloud computing urges the need for novel on-demand approaches, where the Quality of Service (QoS) requirements of cloud-based services can dynamically and adaptively evolve at runtime as Service Level Agreement (SLA) and environment changes. Given the unpredictable, dynamic and on-demand nature of the cloud, it would be unrealistic to assume that optimal QoS can be achieved at design time. As a result, there is an increasing need for dynamic and self- adaptive QoS optimization solutions to respond to dynamic changes in SLA and the environment. In this context, we posit that the challenge of self-adaptive QoS optimization encompasses two dynamics, which are related to QoS sensitivity and conflicting objectives at runtime. We propose novel design of a dynamic data-driven architecture for optimizing QoS influenced by those dynamics. The architecture leverages on DDDAS primitives by employing distributed simulations and symbiotic feedback loops, to dynamically adapt decision making metaheuristics, which optimizes for QoS tradeoffs in cloud-based systems. We use a scenario to exemplify and evaluate the approach.

Item Type: Journal article
Alternative Title: Dynamic Data-Driven Architecture for adaptive QoS optimization in the cloud
Description: 2013 International Conference on Computational Science
Publication Title: Procedia Computer Science
Creators: Chen, T., Bahsoon, R. and Theodoropoulos, G.
Publisher: Elsevier
Date: 2013
Volume: 18
ISSN: 1877-0509
Identifiers:
NumberType
10.1016/j.procs.2013.05.357DOI
S1877050913005000Publisher Item Identifier
Divisions: Schools > School of Science and Technology
Depositing User: Linda Sullivan
Date Added: 29 Jan 2018 10:58
Last Modified: 06 Feb 2018 15:35
URI: http://irep.ntu.ac.uk/id/eprint/32576

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