An Infrastructure Service Recommendation System for Cloud Applications with Real-time QoS Requirement Constraints

• Published in: IEEE systems journal Vol. 11; no. 4; pp. 2960 - 2970
• Main Authors: Zhang, Miranda, Ranjan, Rajiv, Menzel, Michael, Nepal, Surya, Strazdins, Peter, Wei Jie, Lizhe Wang
• Format: Journal Article
• Language: English
• Published: IEEE 01-12-2017
• Subjects: Cloud computing; Decision making; Decision support; Games; Optimization; Quality of service; Real-time systems; service selection; web-based services
• ISSN: 1932-8184; 1937-9234
• DOI: 10.1109/JSYST.2015.2427338

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.