Abstract
Network reliability, named multistate stochastic cloud/edge-based network (MCEN) reliability afterwards, is defined as the probability that demands can be satisfied for an MCEN. It can be regarded as a performance indicator of the MCEN to measure the service capability. The concept of existing algorithms is to produce all of minimal system-state vectors for calculating MCEN reliability. However, such concept cannot response MCEN reliability in time when the MCEN scale becomes complicated in the Industry 4.0 environment. For providing MCEN reliability for decision making immediately, an architecture of a deep neural network (DNN) is developed to propose a prediction model for MCEN reliability such that MCEN capability with varied data can be learned promptly. To train the reliability prediction model, MCEN information is transformed to the suitable format, and the related information for DNN setting, including the determination of related functions, are defined with appropriate hyperparameters by using Bayesian Optimization. An illustrative case and a practical case of Amazon Web Service are provided to demonstrate the prediction model for MCEN reliability to show the availability and the efficiency.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bai, G. H., Zuo, M. J., & Tian, Z. G. (2015). Ordering heuristics for reliability evaluation of multistate networks. IEEE Transactions on Reliability, 64(3), 1015–1023.
Ball, M. O. (1986). Computational complexity of network reliability analysis: An overview. IEEE Transactions on Reliability, 35(3), 230–239.
Bergstra, J., Bardenet, R., Bengio, Y., & Kégl, B. (2011). Algorithms for hyper-parameter optimization. In: 25th annual conference on neural information processing systems (NIPS 2011),
Birim, S., Kazancoglu, I., Mangla, S. K., Kahraman, A., & Kazancoglu, Y. (2022). The derived demand for advertising expenses and implications on sustainability: A comparative study using deep learning and traditional machine learning methods. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04429-x
Colbourn, C. J. (1987). The combinatorics of network reliability. Oxford University Press Inc.
Dastjerdi, A. V., & Buyya, R. (2016). Fog computing: Helping the internet of things realize its potential. Computer, 49(8), 112–116.
Ford, L. R., Jr., & Fulkerson, D. R. (2015). Flows in networks. Princeton University Press.
Glorot, X., Bordes, A., & Bengio, Y. (2011). Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics,
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., & Cai, J. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354–377.
Huang, C.-F., Huang, D.-H., & Lin, Y.-K. (2022a). System reliability analysis for a cloud-based network under edge server capacity and budget constraints. Annals of Operations Research, 312, 217–234.
Huang, D.-H., Chang, P.-C., & Lin, Y.-K. (2022b). A multi-state network to evaluate network reliability with maximal and minimal capacity vectors by using recursive sum of disjoint products. Expert Systems with Applications, 193, 116421.
Hudson, J. C., & Kapur, K. C. (1985). Reliability bounds for multistate systems with multistate components. Operations Research, 33(1), 153–160.
Khan, L. U., Yaqoob, I., Tran, N. H., Kazmi, S. A., Dang, T. N., & Hong, C. S. (2020). Edge-computing-enabled smart cities: A comprehensive survey. IEEE Internet of Things Journal, 7(10), 10200–10232.
Lin, J. S., Jane, C. C., & Yuan, J. (1995). On reliability evaluation of a capacitated-flow network in terms of minimal pathsets. Network, 25, 131–138.
Lin, Y. K. (2001). A simple algorithm for reliability evaluation of a stochastic-flow network with node failure. Computers & Operations Research, 28(13), 1277–1285.
Lin, Y.-K., & Huang, C.-F. (2016). Reliability evaluation according to a routing scheme for multi-state computer networks under assured accuracy rate. Annals of Operations Research, 244(1), 221–240.
Liu, T., Bai, G., Tao, J., Zhang, Y.-A., & Fang, Y. (2021). An improved bounding algorithm for approximating multistate network reliability based on state-space decomposition method. Reliability Engineering & System Safety, 210, 107500.
Luo, S., & Choi, T.-M. (2021). Great partners: how deep learning and blockchain help improve business operations together. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04101-4
Moitra, D., & Mandal, R. K. (2019). Automated AJCC staging of non-small cell lung cancer (NSCLC) using deep convolutional neural network (CNN) and recurrent neural network (RNN). Health Information Science and Systems, 7(1), 1–12.
Rushdi, A. M. A., & Amashah, M. H. (2021). Conventional and improved inclusion-exclusion derivations of symbolic expressions for the reliability of a multi-state network. Asian Journal of Research in Computer Science, 7(4), 21–45. https://doi.org/10.9734/ajrcos/2021/v8i130191
Şaylı, M., & Yılmaz, E. (2017). Anti-periodic solutions for state-dependent impulsive recurrent neural networks with time-varying and continuously distributed delays. Annals of Operations Research, 258(1), 159–185.
Yarlagadda, R. A. O., & Hershey, J. O. H. N. (1991). Fast algorithm for computing the reliability of a communication network. International Journal of Electronics Theoretical and Experimental, 70(3), 549–564.
Yeh, C. T. (2019). An improved NSGA2 to solve a bi-objective optimization problem of multi-state electronic transaction network. Reliability Engineering & System Safety, 191, 106578.
Yeh, C. T., & Fiondella, L. (2017). Optimal redundancy allocation to maximize multi-state computer network reliability subject to correlated failures. Reliability Engineering & System Safety, 166, 138–150.
Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., & Jue, J. P. (2019). All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture, 98, 289–330.
Zhao, X., Cai, J., Mizutani, S., & Nakagawa, T. (2020). Preventive replacement policies with time of operations, mission durations, minimal repairs and maintenance triggering approaches. Journal of Manufacturing Systems, 61, 819–829.
Zhao, X., Li, B., Mizutani, S., & Nakagawa, T. (2021). A revisit of age-based replacement models with exponential failure distributions. IEEE Transactions on Reliability. https://doi.org/10.1109/TR.2021.3111682
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., & Torr, P. H. (2015). Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE international conference on computer vision
Acknowledgements
Funding was provided by Ministry of Science and Technology, Taiwann (Grant No. MOST-111-2222-E-029-003, MOST-109- 2221-E-035-049-MY3, and MOST-109-2221-E-009-067-MY3).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Huang, DH., Huang, CF. & Lin, YK. A reliability prediction model for a multistate cloud/edge-based network based on a deep neural network. Ann Oper Res (2022). https://doi.org/10.1007/s10479-022-04931-w
Accepted:
Published:
DOI: https://doi.org/10.1007/s10479-022-04931-w