Article
| Authors | Gabriel Orsini, Dirk Bade, Winfried Lamersdorf |
| Title | Generic Context Adaptation for Mobile Cloud Computing Environments (extended version) |
| Published in | Journal of Ambient Intelligence and Humanized Computing |
| Publisher | Springer |
| Medium | Volume 9 Number 1 |
| Date | 2018 |
| Pages | 61-71 |
| Document nr (ISBN/DOI) | 10.1007/s12652-017-0526-0 |
| URL | http://link.springer.com/article/10.1007/s12652-017-0526-0 |
| Abstract | Markets for mobile applications offer myriads of apps ranging from simple to quite demanding ones. The latter are on the rise since every new generation of smartphones is equipped with more resources (CPU, memory, bandwidth, energy) to even allow re-source-demanding services like speech- or face recognition to be executed locally on a device. But compared to their stationary counterparts, mobile devices remain comparatively limited in terms of resources. Because of this, current approaches aim at extending mobile device capabilities with computation and storage resources offered by cloud services or other nearby devices. This paradigm, known as Mobile Cloud Computing (MCC), is challenged by the dynamically changing context of mobile devices, which developers are required to take into account to decide, e.g., which application parts are when to offload. To rise to such and similar challenges we introduce the concept of Generic Context Adaptation (GCA), a data mining process that facilitates the adaptation of (mobile) applications to their current and future context. Moreover, we evaluate our approach with real usage data provided by the Nokia Mobile Data Challenge (MDC) as well as with CloudAware, a context-adaptive mobile middleware for MCC that supports automated and context-aware self-adaptation techniques. |
| Document |  |
| Other formats | Din 1501 |
