Statistical analysis methods application for a task distributor selection in a distributed computing system
DOI:
https://doi.org/10.31649/1999-9941-2024-60-2-122-133Keywords:
task distribution, optimization, computing systems, statistical analysis, user profiles, prioritizationAbstract
Abstract. This paper focuses on optimizing the task distribution process in distributed computing systems. By applying statistical analysis methods, a strategy has been developed to automate the selection of task performers, improving the efficiency of task distribution, daily productivity, and employee satisfaction. The research shows that the optimized approach reduced the average processing time for specific user requests from 34 to 31 minutes, which is 7% more effective compared to random task allocation, thereby enhancing service quality and overall productivity.
The proposed unified model for optimized task distribution considers key factors such as internal user profiles, their workload levels, task priority, interaction among performers, and other available system resources. This model balances employee competencies with the speed of task processing, significantly improving the system's overall performance.
Particular attention is given to the methodology based on Salesforce CRM tools, which allows for the effective use of historical data on employee performance to identify the most suitable task performers. Combined with statistical data analysis methods, this approach not only optimizes task distribution but also enables accurate time prediction for task completion, identification of process anomalies, and the development of flexible distribution strategies. Considering both competencies and productivity ensures high-quality task execution, reduces processing time, and minimizes workload, which is critical for the efficient operation of distributed systems.
In overall, the proposed study confirms that the use of statistical analysis and CRM tools enhances the efficiency of distributed computing systems. This opens opportunities for the implementation of optimized task distribution strategies across various sectors, especially in the context of the growing volume of data and the complexity of business processes.
References
Amaral, C.J., Hübner, J.F. & Cranefield, S. (2023). Generating and choosing organisations for multi-agent systems. Auton Agent Multi-Agent Syst, 37, 41. https://doi.org/10.1007/s10458-023-09623-8.
Canestrino, R., Magliocca, P., & Li, Y. (2022). The Impact of Language Diversity on Knowledge Sharing Within International University Research Teams: Evidence From TED Project. Frontiers in Psychology, Vol. 13. https://doi.org/10.3389/fpsyg.2022.879154
Celber, D. (2023). Generating and Choosing Organizations for Multi-Agent Systems. Autonomous Agents and Multi-Agent Systems. Retrieved from https://link.springer.com/article/10.1007/s10458-023-09623-8.
Churkina, O., Nazareno, L., & Zullo, M. (2023). The labor market outcomes of bilinguals in the United States: Accumulation and returns effects. PLoS ONE 18(6): e0287711. https://doi.org/10.1371/journal.pone.0287711
Dery, K., & Sebastian, I. (2017). Employee Experience: Culture, Engagement and Leadership in the Digital Age. MIT Center for Information Systems Research. Retrieved from https://cisr.mit.edu/publication/2017_0601_EmployeeExperience_DerySebastian.
Fu, H., Yu, S., Tiwari, S., Littman, M., & Konidaris, G. (2022). Meta-learning parameterized skills. arXiv preprint, arXiv:2206.03597.
Gao, Z.-F., Zhou, K., Liu, P., Zhao, W. X., & Wen, J.-R. (2023). Small Pre-trained Language Models Can be Fine-tuned as Large Models via Over-Parameterization. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Vol.1, (pp. 3819–3834). https://doi.org/10.18653/v1/2023.acl-long.212
Gupta, S. K., & Verma, M. (2022). A Comprehensive Review of Statistical Approaches in Task Scheduling for Distributed Systems. Advances in Statistical Computing.
Gupta, S., & Bharti, D. (2020). Application of Statistical Techniques in Project Monitoring and Control. In: Kapur, P.K., Singh, O., Khatri, S.K., Verma, A.K. (eds) Strategic System Assurance and Business Analytics. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-15-3647-2_7.
Hu, Q., Wu, X. & Dong, S. (2023). A Two-Stage Multi-Objective Task Scheduling Framework Based on Invasive Tumor Growth Optimization Algorithm for Cloud Computing. J Grid Computing 21, 31 https://doi.org/10.1007/s10723-023-09665-y
Huang, H., Liu, H., Xia, C., Mei, H., Gao, X., & Liang, B. (2023). Sampling-based time-optimal path parameterization with jerk constraints for robotic manipulation. Robotics and Autonomous Systems, Vol. 170, p. 104530). Elsevier BV. https://doi.org/10.1016/j.robot.2023.104530
Ivanisenko, I. N., & Radivilova, T. A. (2015). Survey of major load balancing algorithms in distributed system. In 2015 Information Technologies in Innovation Business Conference (ITIB) IEEE. (pp. 89–92). https://doi.org/10.1109/itib.2015.7355061
Jones, M. (2021). Exploring the Role of Statistical Analysis in Distributed System Optimization. International Journal of Distributed Systems.
Lee, J. H., & Kim, T. Y. (2023). Optimization Strategies for Distributed Task Scheduling Using Statistical Methods. Applied Computing.
Li, J., Cong, M., Liu, D. and Du, Y. (2023). Enhanced task parameterized dynamic movement primitives by GMM to solve manipulation tasks. Robotic Intelligence and Automation, Vol. 43, No. 2, pp. 85-95. https://doi.org/10.1108/RIA-07-2022-0199.
Mahmood, Y., Meier, A., & Schmidt, J. (2023). Parameterized Complexity of Logic-based Argumentation in Schaefer’s Framework. ACM Transactions on Computational Logic, Vol. 24, Issue 3, pp. 1–25. ACM. https://doi.org/10.1145/3582499
Michaud F. A statistical review of delay analysis methods used over the last decade. Retrieved from: https://www.hka.com/a-statistical-review-of-delay-analysis-techniques-used-over-the-last-decade/.
Perez-Villeda, H., Piater, J., & Saveriano, M. (2023). Learning and extrapolation of robotic skills using task-parameterized equation learner networks. Robotics and Autonomous Systems, Vol. 160, p. 104309. Elsevier BV. https://doi.org/10.1016/j.robot.2022.104309.
Pham, X. L., & Yang, Z. (2023). Task Scheduling in Distributed Computing Environments with Heuristic Algorithms. Journal of Parallel and Distributed Computing.
Shi, H., Jiang, L., Zheng, J., & Zeng, J. (2023). Self-Parameterization Based Multi-Resolution Mesh Convolution Networks. In Computer-Aided Design (Vol. 162, p. 103550). Elsevier BV. https://doi.org/10.1016/j.cad.2023.103550
Trautmann, M., Voelcker-Rehage, C. & Godde, B. (2011). Fit between workers’ competencies and job demands as predictor for job performance over the work career. ZAF 44, 339–347 https://doi.org/10.1007/s12651-011-0078-2
Wang, H., & Lin, Y. (2023). Dynamic Task Allocation for Cloud Computing Systems Using Hybrid Algorithms. Cloud Computing Journal.
Xie, T., Yin, M., Zhu, X., Sun, J., Meng, C., & Bei, S. (2023). A Fast and Robust Lane Detection via Online Re-Parameterization and Hybrid Attention. Sensors, Vol. 23, Issue 19, p. 8285). MDPI AG. https://doi.org/10.3390/s23198285.
Zhang, L. (2023). The Changing Role of Managers. American Journal of Sociology, Vol. 129, Issue 2, pp. 439–484. University of Chicago Press. https://doi.org/10.1086/727145
Downloads
-
PDF (Українська)
Downloads: 7