Dr. Boyuan Yan Profile

Dr. Boyuan Yan

at Beijing Univ of Posts and Telecommunications

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Publications (2)

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Proceedings Article | 14 February 2019 Paper

Deep-learning-based failure prediction with data augmentation in optical transport networks

Lihua Cui, Yongli Zhao, Boyuan Yan, Dongmei Liu, Jie Zhang

Proceedings Volume 11048, 110482I (2019) https://doi.org/10.1117/12.2523136

KEYWORDS: Evolutionary algorithms, Failure analysis, Optical networks, Data modeling, Neural networks, Networks, Artificial intelligence, Performance modeling, Clocks, Telecommunications

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Failures in optical transport networks usually result in lots of services being interrupted and a huge economic loss. If the failures can be predicted in advance, some actions can be conducted to avoid the above adverse consequences. Deep learning is a good technology of artificial intelligence, which can be used in many scenarios to replace humans’ activities. Event prediction is a typical scenario, where deep learning can be used based on a large dataset. Therefore, deep learning can be used in optical transport networks for failure prediction. However, dataset construction is an important problem for deep learning in optical transport networks, because there may be not enough data in reality. This paper proposes a deep-learning-based failure prediction (DLFP) algorithm that constructs available dataset based on data-augmentation for data training. DLFP algorithm is composed of alarm compression, data augmentation, and fully-connected back-propagation neural network (FCNN) algorithm. Besides, a benchmark algorithm (BA) without data augmentation is introduced. A training model is constructed based on massive real performance data and related alarm data within one month, which are collected from national backbone synchronous digital hierarchy (SDH) network with 274 nodes and 487 links in China. Then the training model is used with test dataset to verify the performance in terms of prediction accuracy. Evaluation results show that the proposed algorithm is able to reach better performance for failure prediction compared with the benchmark without data augmentation.

Proceedings Article | 12 December 2018 Paper

Geography-based Service Function Path (SFP) provisioning in inter-datacenter elastic optical networks

Boyuan Yan, Yongli Zhao, Ying Wang, Xiaosong Yu, Wei Wang, Jie Zhang

Proceedings Volume 10849, 108490X (2018) https://doi.org/10.1117/12.2505463

KEYWORDS: Optical networks, Information technology, Computer simulations, Gadolinium, Geography, Photonics, Optical communications, Telecommunications, Associative arrays

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Inside a service function chain (SFC), traffic flow follows a certain route, namely a service function path (SFP), to travel through each service function (SF) entity. A SFP consists of several end-to-end segments, whose source and destination are named anchor node (AN). SFs are located in multiple datacenters (DCs), and inter-DC light-paths need to be provisioned between separated SFs. In this paper, we introduce geography information of optical nodes and DCs, define special geographic distance between ANs in inter-DC elastic optical networks (EONs). Then following minimal geographic distance principle, we propose a geography-based SFP provisioning solution, which contains two heuristic algorithms, named geography-based shortest path and first-fit algorithm (GSP-FF) and geography-based k-shortest paths and first-fit algorithm (GK-FF). These algorithms can compress AN selection procedure extremely in fixed time, which cost little time for the AN selection of resource allocation. And benchmark algorithm use Dijkstra shortest path calculation and first-fit FS selection to allocate IT resources in DCs and FS resources in EONs. Then GSP-FF and GKFF are proposed to provision SFPs efficiently. In our simulation, we compare our proposed algorithms with benchmark algorithm deeply on blocking probability, running time, average hops, average geographic distance, et al. under different traffic load and other simulation environment. We also analyze the trend and reason for the performance difference among these algorithms. According detailed evaluation, simulation proves that the proposed algorithms in this paper could use geographic information efficiently, and achieve lower blocking probability with lower running time compared with the benchmark algorithm.

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