  Proliferation of cloud computing has revolutionized 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, Microsoft Azure) and small companies (e.g. Rackspace, Ninefold),
decision makers (e.g. application developers, CIOs) are likely to be
overwhelmed by choices available. The decision making problem is further
complicated due to heterogeneous service configurations and application
provisioning Quality of Service (QoS) constraints. To address this hard
challenge, in our previous work we developed a semi-automated, extensible, and
ontology-based approach to infrastructure service discovery and selection based
on only design time constraints (e.g., renting cost, datacentre location,
service feature, etc.). In this paper, we extend our approach to include the
real-time (run-time) QoS (endto- end message latency, end-to-end message
throughput) in the decision making process. Hosting of next generation
applications in domain of on-line interactive gaming, large scale sensor
analytics, and real-time mobile applications on cloud services necessitates
optimization of such real-time QoS constraints for meeting Service Level
Agreements (SLAs). To this end, we present a real-time QoS aware multi-criteria
decision making technique that builds over well known Analytics Hierarchy
Process (AHP) 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 possible
best fit combinations of cloud services at IaaS layer. We conducted extensive
experiments to prove the feasibility of our approach.
