Continuous Integration/ Continuous Delivery Optimization on Network Automation using Gray Wolf Optimizer

Ronald Adrian, Anni Karimatul Fauziyyah, Sahirul Alam


Continuous Integration/ Continuous Delivery is the latest method used in network automation. In-network programming has helped network admins a lot in managing all their devices. One of the real-time networks needs to force network admins to be able to provide data quickly. Deployment speed can be increased to provide up-to-date data or network configuration. To tackle these problems, we propose implementing the GWO algorithm in the Continuous Integration/Continuous Delivery process. This algorithm is proven to be superior in the speed of finding the value of the objective function compared to other similar algorithms. The results obtained indicate that the convergence time is faster by 74%. This value has an impact on increasing program deployment speed by 41.2%. These results indicate that the GWO algorithm can be an alternative to increasing the speed of Continuous Integration/ Continuous Delivery.

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O. Arouk and N. Nikaein, “5G Cloud-Native: Network Management & Automation,” Proceedings of IEEE/IFIP Network Operations and Management Symposium 2020: Management in the Age of Softwarization and Artificial Intelligence, NOMS 2020, Apr. 2020, doi: 10.1109/NOMS47738.2020.9110392.

X. Jin and F. Servant, “CIBench: A Dataset and Collection of Techniques for Build and Test Selection and Prioritization in Continuous Integration,” Proceedings - International Conference on Software Engineering, pp. 166–167, May 2021, doi: 10.1109/ICSE-COMPANION52605.2021.00070.

A. Poth, M. Werner, and X. Lei, “How to Deliver Faster with CI/CD Integrated Testing Services?,” Communications in Computer and Information Science, vol. 896, pp. 401–409, 2018, doi: 10.1007/978-3-319-97925-0_33/COVER.

A. Alnafessah, A. U. Gias, R. Wang, L. Zhu, G. Casale, and A. Filieri, “Quality-Aware DevOps Research: Where Do We Stand?,” IEEE Access, vol. 9, pp. 44476–44489, 2021, doi: 10.1109/ACCESS.2021.3064867.

M. H. Nadimi-Shahraki, S. Taghian, S. Mirjalili, H. Zamani, and A. Bahreininejad, “GGWO: Gaze cues learning-based grey wolf optimizer and its applications for solving engineering problems,” Journal of Computational Science, vol. 61, p. 101636, May 2022, doi: 10.1016/j.jocs.2022.101636.

J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 - International Conference on Neural Networks, Nov. 1995, vol. 4, no. 6, pp. 1942–1948. doi: 10.1109/ICNN.1995.488968.

X.-S. Yang, “A New Metaheuristic Bat-Inspired Algorithm,” in Studies in Computational Intelligence, vol. 284, J. R. González, D. A. Pelta, C. Cruz, G. Terrazas, and N. Krasnogor, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 65–74. doi: 10.1007/978-3-642-12538-6_6.

S. Mirjalili, “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm,” Knowledge-Based Systems, vol. 89, pp. 228–249, 2015, doi: 10.1016/j.knosys.2015.07.006.

R. Adrian, S. Sulistyo, I. W. Mustika, and S. Alam, “A Controllable RSU and Vampire Moth to Support the Cluster Stability in VANET,” International Journal of Computer Networks & Communications, vol. 13, no. 3, pp. 79–95, May 2021, doi: 10.5121/ijcnc.2021.13305.

M. R. Pratama and D. Sulistiyo Kusumo, “Implementation of Continuous Integration and Continuous Delivery (CI/CD) on Automatic Performance Testing,” 2021 9th International Conference on Information and Communication Technology, ICoICT 2021, pp. 230–235, Aug. 2021, doi: 10.1109/ICOICT52021.2021.9527496.

Y. Wang, M. v. Mäntylä, Z. Liu, and J. Markkula, “Test automation maturity improves product quality—Quantitative study of open source projects using continuous integration,” Journal of Systems and Software, vol. 188, p. 111259, Jun. 2022, doi: 10.1016/j.jss.2022.111259.

W. Zhang, X. Fei, and B. Wang, “Research on Specific Harmonic Elimination Technology Based on Improved Gravity Search Algorithm,” pp. 1499–1504, Aug. 2022, doi: 10.1109/ICMA54519.2022.9855983.

W. W. Koczkodaj et al., “Combating harmful Internet use with peer assessment and differential evolution,” 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), pp. 1–6, Jul. 2022, doi: 10.1109/ICECET55527.2022.9873437.

D. Sobania, D. Schweim, and F. Rothlauf, “A Comprehensive Survey on Program Synthesis with Evolutionary Algorithms,” IEEE Transactions on Evolutionary Computation, pp. 1–1, Mar. 2022, doi: 10.1109/TEVC.2022.3162324.

S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, pp. 46–61, Mar. 2014, doi: 10.1016/j.advengsoft.2013.12.007.

J. Adhikary and S. Acharyya, “Randomized Balanced Grey Wolf Optimizer (RBGWO) for solving real life optimization problems,” Applied Soft Computing, vol. 117, p. 108429, Mar. 2022, doi: 10.1016/j.asoc.2022.108429.


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