# CommC: A Multi-Purpose COMModity Hardware Cluster
## 6 Conclusions and Future Work
Our own deployment, consisting of 16 physical nodes, has not encountered any noticeable delays or discrepancies. The cost analysis presented in this paper highlights the significant financial advantages of the CommC system over cloud services, even when factoring in additional costs, such as operational expenses and hardware failures. While the comparison primarily focuses on initial expenses and general operational costs, it is important to note that long-term factors like power consumption, cooling, periodic hardware upgrades, and occasional hardware failures do not substantially diminish the cost-efficiency of the CommC system. Even when accounting for these additional expenses, the overall affordability remains superior to cloud-based alternatives, where recurring costs such as subscription fees and data transfer charges often accumulate rapidly over time. Furthermore, the flexibility to avoid vendor lock-in and the ability to use open-source tools further enhance the system's financial viability. By carefully managing operational expenses and leveraging commodity hardware, the CommC system provides a robust and cost-effective alternative to traditional cloud services, even when considering the potential for additional costs. In the future, we aim to enhance the CommC system by focusing on two primary areas of improvement. First, we aim to enhance resource allocation through intelligent automation powered by machine learning algorithms. These algorithms will dynamically analyze workload patterns to optimize resource distribution, ensuring higher efficiency and improved utilization of the underlying hardware. This approach will minimize the need for manual intervention, making the system more adaptable to varying demands. Additionally, we plan to develop an API that securely integrates with the laboratory's Proxmax cluster to automate processes, generate telemetry, and efficiently manage resources for multiple users. Furthermore, we intend to implement a machine learning model capable of predicting future resource utilization based on historical usage trends, leveraging auto-regressive and moving average techniques to enhance forecasting accuracy. Second, we intend to develop a user-friendly platform for monitoring and accessing the various services hosted within the CommC environment. This platform would simplify administrative tasks by providing an intuitive interface for visualizing resource usage, managing services, and configuring system parameters. It would also enhance the user experience by allowing seamless access to hosted services, thereby lowering the technical barrier for end-users. These enhancements are expected to improve the scalability, usability, and overall efficiency of the CommC system, paving the way for broader adoption across diverse organizational settings. Conceptualization, A.B. ; methodology, A.B. and N.-R.K.; software, A.B. ; validation, A.B., N.-R.K. and C.M. ; formal analysis, A.B. ; investigation, A.B. ; resources, A.B., N.-R.K. and C.M. ; data curation, A.B. and N.-R.K.; writing--original draft preparation, A.B. and N.-R.K.; writing--review and editing, A.B., N.-R.K. and C.M. ; visualization, A.B. and N.-R.K.; supervision, C.M. ; project administration, C.M. ; funding acquisition, C.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The code and system configurations required to replicate the CommC system are publicly available on GitHub and can be accessed using the following link: System Code Repository. The datasets used for the individual experiments mentioned in this paper are not included, as their availability and details are provided in the respective research papers that describe those experiments. These datasets, along with their specific usage, are thoroughly documented in those papers, which are beyond the scope of this work. References to these papers can be found in the bibliography section or will be released in the near future, as they are work in progress.