Mango: A model-driven approach to engineering green Mobile Cloud Applications

With the resource constrained nature of mobile devices and the resource abundant offerings of the cloud, several promising optimisation techniques have been proposed by the green computing research community. Prominent techniques and unique methods have been developed to offload resource/computation intensive tasks from mobile devices to the cloud. Most of the existing offloading techniques can only be applied to legacy mobile applications as they are motivated by existing systems. Consequently, they are realised with custom runtimes which incur overhead on the application. Moreover, existing approaches which can be applied to the software development phase, are difficult to implement (based on manual process) and also fall short of overall (mobile to cloud) efficiency in software qualityattributes or awareness of full-tier (mobile to cloud) implications.To address the above issues, the thesis proposes a model-driven architecturefor integration of software quality with green optimisation in Mobile Cloud Applications (MCAs), abbreviated as Mango architecture. The core aim of the architecture is to present an approach which easily integrates software quality attributes (SQAs) with the green optimisation objective of Mobile Cloud Computing (MCC). Also, as MCA is an application domain which spans through the mobile and cloud tiers; the Mango architecture, therefore, takesinto account the specification of SQAs across the mobile and cloud tiers, for overall efficiency. Furthermore, as a model-driven architecture, models can be built for computation intensive tasks and their SQAs, which in turn drives the development – for development efficiency. Thus, a modelling framework (called Mosaic) and a full-tier test framework (called Beftigre) were proposed to automate the architecture derivation and demonstrate the efficiency of Mango approach. By use of real world scenarios/applications, Mango has been demonstrated to enhance the MCA development process while achieving overall efficiency in terms of SQAs (including mobile performance and energy usage compared to existing counterparts)