# A Model-Driven Approach for Solving the Software Component Allocation Problem
## 7 Conclusions and Future Work  
In this paper, we have introduced a novel model-driven framework tackling the component allocation problem for both single- and multi-objective cases. The proposed solution was evaluated in the experiments based on several significant works in the area of software resource and computing infrastructure management. The results were compared to other solutions based on linear optimization tackling the CAP problem and other methods, such as GA and ACO, on the other side. Based on our experimental results, it can be concluded that our approach shows a faster execution time than GA (up to 3 orders of magnitude) and ACO (up to 5 orders of magnitude). Moreover, according to our results, it is also faster than several frameworks based on single-objective linear optimization, especially for larger models, up to two orders of magnitude. Furthermore, we extended several of the existing works with a multi-objective approach, achieving additional benefits, such as improved performance, while maintaining the infrastructure management costs and energy consumption as small as possible. Finally, we also include the optimal solution visualization workflow, which can help in decision-making in the case of multi-objective allocation.  
To apply the proposed framework in solving a component allocation problem, the problem must be formulated as MOCAP (see Equations (1) and (2)). This requires the resource consumptions to have linear relationships with the possible allocations. Then, the modeler needs to create a model transformation into a model which conforms to the CAP meta-model. Although our framework may not be applicable to all allocation problems, we believe that the meta-model itself can be extended to accommodate a wider-variety of such problems. This would require extending the solver as well.  
In future work, we would like to further exploit the results obtained as outcome of the optimization process for the purpose of code generation relying on domain-specific meta-models, such as the construction of virtual machine and container management commandsin the case of software resource allocation or software-defined radio commands for a telecommunications case study. Additionally, the extension and adoption of the proposed approach in other domains is also planned, such as optimal resource planning during the COVID-19 pandemic. Finally, leveraging the synergy of optimization-based component allocation with predictions performed against the data collected during the system usage relevant to several allocation constraints can be considered in order to develop a new methodology for proactive resource planning.  
Conceptualization, I.A.-A. and J.B.; Methodology, I.A.-A. and J.B.; Software, I.A.-A. and J.B.; Validation, I.A.-A. and N.P.; Formal Analysis, I.A.-A. and J.B.; Data Curation, I.A.-A.; Writing--Original Draft Ppreparation, I.A.-A., J.B. and N.P.; Writing--Review and Editing, I.A.-A., J.B., and N.P.; Visualization, I.A.-A. and J.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.