n-Dimensional Prediction of RT-SOA QoS

Service-Orientation has long provided an effective mechanism to integrate heterogeneous systems in a loosely coupled fashion as services. However, with the emergence of Internet of Things (IoT) there is a growing need to facilitate the integration of real-time services executing in non-controlled, non-real-time, environments such as the Cloud. As such there has been a drive in recent years to develop mechanisms for deriving reliable Quality of Service (QoS) definitions based on the observed performance of services, specifically in order to facilitate a Real-Time Quality of Service (RT-QoS) definition. Due to the overriding challenge in achieving this is the lack of control over the hosting Cloud system many approaches either look at alternative methods that ignore the underlying infrastructure or assume some level of control over interference such as the provision of a Real-Time Operating System (RTOS). There is therefore a major research challenge to find methods that facilitate RT-QoS in environments that do not provide the level of control over interference that is traditionally required for real-time systems. This thesis presents a comprehensive review and analysis of existing QoS and RT-QoS techniques. The techniques are classified into seven categories and the most significant approaches are tested for their ability to provide QoS definitions that are not susceptible to dynamic changing levels of interference. This work then proposes a new n-dimensional framework that models the relationship between resource utilisation, resource availability on host servers, and the response-times of services. The framework is combined with real-time schedulability tests to dynamically provide guarantees on response-times for ranges of resource availabilities and identifies when those conditions are no longer suitable. The proposed framework is compared against the existing techniques using simulation and then evaluated in the domain of Cloud computing where the approach demonstrates an average overallocation of 12%, and provides alerts across 94% of QoS violations within the first 14% of execution progress.