# Software Architecture for Mobile Cloud Computing Systems
## 4 Frequency, Classification, and Mapping of the Existing Research
There may be a possibility to classify the studies under more than one theme if their research contributions relate to multiple themes. For example, the study [S51]--classified under the theme of _Mobile to Cloud Data Offloading_--proposes a solution to offload computation to cloud-based servers to optimize the energy efficiency of a mobile device. This means that this study could possibly be classified under two subthemes, namely _Computational Efficiency_ and _Energy Efficiency_. For example, in the abovementioned case, the study [S51] was classified under the sub-theme Energy Efficiency as the primary contribution of the research study is on optimizing the energy or battery resources of a mobile device by means of computational offloading (i.e., secondary contribution). In all such scenarios, when a study was the candidate for classification under multiple themes/sub-themes, we prioritized the primary contributions of such a study for its classification. In conclusion, to derive the classification scheme in Figure 4, each study has been classified based on its primary contribution, while any secondary contributions are ignored to avoid any overlaps of the research themes. The classification in Figure 4 guides the presentation of the results throughout the paper. ### Taxonomical Classification of Existing Research Themes  
The taxonomical classification of research (see Figure 4) organizes the research themes into various categories. These categories are based on the similarity or distinction of research contributions. However, in order to synthesize and interpret the results, there is a need to extend the classification scheme with a mapping of identified research themes, their sub-themes, and corresponding evidence (relevant studies) that is illustrated in Figure 5. Figure 5 provides a three-dimensional mapping of research that helps us to highlight and discuss:  
* **Research thematic facet**: It is based on generic classification of Figure 4 on the x-axis (left) that is referred to as a thematic facet of mapping in Figure 5.
* **Contribution facet**: It is based on thematic classification of Figure 4 highlighting the specific contributions of research on y-axis as the contribution facet of mapping. * **Temporal distribution facet**: It represents the temporal distribution of evidence on the x-axis (right) that is referred to as a temporal distribution facet of mapping. In Figure 5, the dark rectangles on horizontal axis (both at right and left) have two main purposes. First, the left side rectangles reflect the relevant studies as the identified evidence corresponding to individual themes and their subthemes. Second, the right side presents the yearly (temporal) distribution or publication frequency of the evidence. In Figure 5, the size of the rectangle on the horizontal axis is proportional to the quantity of the available evidence. For example, the biggest rectangle in Figure 5 reflects that computational efficiency (a sub-theme) is the most researched area with 40 studies that supports architectural solutions for offloading mobile data to cloud computing (generic theme). The bigger rectangles on horizontal-axis (right) represents the years 2015 to 2017 as most productive in terms of the total number of research publications (17, 16 and 17, respectively). The interpretation of the research mapping in Figure 5 is based on locating a given research theme (x-axis) that allows the identification and mapping of its sub-themes (y-axis) along with identification of each relevant study in the circle. For example, the representation of [S4] highlights that 'the study [S4] focuses on the monitoring and estimating energy consumption and performance of a mobile device to support its energy optimization (by offloading mobile data to the cloud)'. The mapping in Figure 5 has multiple interpretations based on the intent of the analysis.