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The widespread use of mobile devices such as smartphones and tablets is accompanied by an ever increasing market for mobile applications, including resource demanding services like speech-or face recognition, that formerly were restricted to stationary devices. But as mobile devices remain comparatively limited in terms of resources (e.g., computation, storage and battery life), current approaches for augmentation have advocated the integration of cloud servers as well as other nearby devices to provide scalable computation-and storage resources to mobile end users. A current solution is the so-called computation offloading, which is the most prominent strategy used in Mobile Cloud Computing (MCC) and its successor known as Mobile Edge Computing (MEC). While MCC and MEC are receiving increasing attention, current work is often not able to cope with the quickly and constantly changing context (e.g., intermittent connectivity) of mobile devices. Therefore, this paper presents the evaluation of CloudAware, a context-adaptive mobile middleware for MCC as well as MEC that supports automated context adaptation by linking the distribution features of mobile middleware with context-aware self-adaptation techniques. In particular, we present a system software infrastructure and a data mining process which facilitate the development of elastic, scalable and context-adaptive mobile applications and present their evaluation using real usage data provided by the Nokia Mobile Data Challenge (MDC) dataset.
Orsini et al. (Thu,) studied this question.