Context-aware Mobile Computing in the Cloud: Context-driven Decision-making for Self-Adaptation
Context-bewuste mobiele berekeningen in de cloud: context-gedreven besluitvorming voor zelf-adaptatie,,
Naqvi, Syeda Nayyab Zia; R0306460
Decision optimization for computation oﬄoading is a novel research area which is gaining popularity due to the proliferation of advanced sensor-based mobile applications and the growing compute and data resources in advanced mobile devices. The work presented in this thesis ﬁts into the area of context-aware mobile application development in distributed cloud environment with a focus ondynamicdecisionsupport. Thisthesishighlightstheneedofanopportunistic dynamic decision-making towards the context-driven conﬁguration and selfadaptation for computation oﬄoading tackling the challenges of the varying resource performance trade-oﬀs, the inﬂuence of the runtime uncertainty in the context and the conﬂicting oﬄoading objectives. The presented decision support can ascertain the quality of its own decision applying directed probabilistic graphical models at runtime. Computation oﬄoading is an interesting research ﬁeld driven by the advanced connectivity and the resource-constrained devices. In literature, the focus of computation oﬄoading revolves around how, what, where and when to oﬄoad the data or processing. The how and what questions grabbed the attention of researchers most and it is a saturated area exploring diﬀerent techniques of code partitioning and state transitions. We have focused more the rest of the two focus areas due to the fast pace of technological advancements in mobile technology. Mobile devices are more powerful than ever. We have investigated their threshold to use the cloud resources for data-intensive use-cases and presented many resource-performance trade-oﬀs. An enhanced benchmarking framework is also contributed to benchmark the performance, memory scalability and energy consumption for context-aware applications. Context-aware conﬁgurations and adaptations enable the smartness and an ubiquitous interaction with the users characterized by the subjectivity and the dynamics owing to the mobility and available resources on the device. The key feature of smart mobile applications is that the data is generated on the mobile devices. The subjective context and QoS requirements regarding the type of the application, the level of the smartness, i.e. the frequency of the adaptations or the type of the context data, the device oﬀerings, and the context-driven conﬁguration of the sensors introduce resource performance trade-oﬀs on the ﬂy. Several dynamic decision models are designed to tackle the inherent uncertainty, subjectivity, and dynamism analysing the role of QoC in decision-making for context-aware self-adaptations. We further investigate and provide decision support under QoE requirements for smoother interaction maintaining the QoS in terms of lower latency under a Qo*-aware fashion. MAsCOT, an opportunistic decision support framework is presented for the opportunistic dynamic oﬄoading of loosely-coupled contextaware mobile applications. A prototypical approach is used to investigate and evaluate our approach for cloud-enabled smart mobile applications. The techniques and results presented in this thesis are at the forefronts of the self-adaptive dynamic decision support. The applicability and results of the performance overhead of directed PGMs with a retrospective and reﬂective behaviour encourage advanced and sophisticated mobile solutions for dynamic decision-making such as distributed or hybrid decision support.