Architecting Green Mobile Cloud Apps

Jaachimma Chinenyeze, Samuel; Liu, Xiaodong

Abstract

With the resource-constrained nature of mobile devices, and the resource-abundant offerings of the cloud, several promising optimization 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 realized with custom runtimes, which incurs 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 quality attributes or awareness of full-tier (mobile to cloud) implications.

To address the above issues, this chapter first examines existing approaches to highlight key sources of overhead in the current methods of MCA implementation and evaluation. It then proposes key architectural considerations for implementing and evaluating MCA applications which easily integrate software quality attributes with the green optimization objective of Mobile Cloud Computing—in other words, minimizing overhead. The solution proposed in the chapter builds on the benefits of already existing software engineering concepts, such as Model-Driven Engineering and Aspect-oriented Programming for MCA implementation, and Behavior-Driven Development and full-tier test coverage concepts for MCA evaluation.

• Online Publication Date: Oct 6, 2021
• Publication Date: Nov 10, 2021
• Deposit Date: Nov 26, 2021
• Publicly Available Date: Oct 7, 2023
• Publisher: Springer
• Pages: 183-214
• Book Title: Software Sustainability
• Chapter Number: 8
• ISBN: 978-3-030-69969-7
• DOI: https://doi.org/10.1007/978-3-030-69970-3_8
• Public URL: http://researchrepository.napier.ac.uk/Output/2824699