# Distributed Load-Balancing Method for CCA Parallel Component Applications
## 2 Materials and Methods
### Aggregation of the Computing Nodes
The platform's resource-management node deploys these proxies onto the corresponding computing nodes, while the data flow analysis engine gathers information and dependencies of component instances by analyzing the business function components' code. Users manually select a sub-cluster, and the platform's resource-management node deploys the component code to all nodes within that sub-cluster. An application is initiated by a startup node within the sub-cluster. During the application's execution, the startup node consults the data-flow analysis result XML file and communicates the component instances ready for execution among low-load nodes in the sub-cluster. These low-load nodes decide to accept component instances based on their current load status and execute them using the local CCAFFEINE framework. Our PCDDB method can allocate component instances to computing nodes with minimal overhead, thereby enhancing the performance of parallel component applications, optimizing the efficiency of platform computing resources, and increasing the system's overall throughput. Experimental results indicate that, compared to existing static load-balancing methods and centralized load-balancing strategies, PCDDB can fully leverage the system's computing resources to improve performance and scalability at a low cost. Our current research is focused on the allocation of CPU as the primary computing resource. Future work will explore the impact of memory and other resource allocations on load-balancing. Moreover, when the number of computational nodes in a server cluster exceeds 50 and the number of applications deployed on a daily basis is relatively limited, the promotion of the load-balancing method proposed in this paper is likely to be significantly reduced, due to the potentially excessive utilization of computational resources. Given that the method relies on the distributed dissemination of component instance information throughout the cluster, it becomes inapplicable when network fluctuations or failures occur. However, barring these two scenarios, the proposed method can be effectively extended to load-balancing processes of other types of parallel component applications beyond CCA-based systems, provided that the server cluster is constrained by computational resources but enjoys a robust network environment. Conceptualization, L.G. ; methodology, L.G. ; software, L.G. ; validation, X.G. ; formal analysis, F.L. ; investigation, F.L. ; resources, F.L. ; writing--original draft preparation, L.G. ; writing--review and editing, L.G. ; supervision, L.G. ; project administration, L.G. ; funding acquisition, F.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 62003004. No new data were created or analyzed in this study. Data sharing is not applicable to this article. Author [PERSON] was employed by the company Beijing Power Node Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Abbreviations

The following abbreviations are used in this manuscript: \begin{tabular}{l l} CCA & Common component architecture \\ PCDDB & Parallel component dynamic and distributed balance \\ SIDL & Scientific interface definition language \\ \end{tabular} 

## References

* (1) The Common Component Architecture Forum. Available online: [[https://cca-forum.github.io/cca-forum/overview/index.html](https://cca-forum.github.io/cca-forum/overview/index.html)]([https://cca-forum.github.io/cca-forum/overview/index.html](https://cca-forum.github.io/cca-forum/overview/index.html)) (accessed on 1 February 2024). * (2) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]: Optimisation of component applications within a Grid environment. _Parallel Comput._**2002**, _28_, 1753-1772.