Zhang, Miranda, Ranjan, Rajiv, Menzel, Michael, Nepal, Surya, Strazdins, Peter, Jie, Wei ORCID: https://orcid.org/0000-0002-5392-0009 and Wang, Lizhe (2017) An infrastructure service recommendation system for cloud applications with real-time QoS requirement constraints. IEEE Systems Journal, 11 (4). pp. 2960-2970. ISSN 1932-8184

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Abstract

The proliferation of cloud computing has revolutionized the hosting and delivery of Internet-based application services. However, with the constant launch of new cloud services and capabilities almost every month by both big (e.g., Amazon Web Service and Microsoft Azure) and small companies (e.g., Rackspace and Ninefold), decision makers (e.g., application developers and chief information officers) are likely to be overwhelmed by choices available. The decision-making problem is further complicated due to heterogeneous service configurations and application provisioning QoS constraints. To address this hard challenge, in our previous work, we developed a semiautomated, extensible, and ontology-based approach to infrastructure service discovery and selection only based on design-time constraints (e.g., the renting cost, the data center location, the service feature, etc.). In this paper, we extend our approach to include the real-time (run-time) QoS (the end-to-end message latency and the end-to-end message throughput) in the decision-making process. The hosting of next-generation applications in the domain of online interactive gaming, large-scale sensor analytics, and real-time mobile applications on cloud services necessitates the optimization of such real-time QoS constraints for meeting service-level agreements. To this end, we present a real-time QoS-aware multicriteria decision-making technique that builds over the well-known analytic hierarchy process method. The proposed technique is applicable to selecting Infrastructure as a Service (IaaS) cloud offers, and it allows users to define multiple design-time and real-time QoS constraints or requirements. These requirements are then matched against our knowledge base to compute the possible best fit combinations of cloud services at the IaaS layer. We conducted extensive experiments to prove the feasibility of our approach.

Item Type:	Article
Identifier:	10.1109/JSYST.2015.2427338
Additional Information:	© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords:	Quality of service, Real-time systems, Games, Cloud computing, Decision making, Optimization
Subjects:	Computing
Depositing User:	WEI JIE
Date Deposited:	07 Mar 2016 12:13
Last Modified:	04 Nov 2024 12:19
URI:	https://repository.uwl.ac.uk/id/eprint/1731

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