Naive-LSTM based services awareness of edge computing elastic optical networks
CSTR:
Author:
Affiliation:

1. Beijing SmartChip Microelectronics Technology Company Limited, Beijing 102200, China;2. Huakeyinnuo (Tianjin) Energy Technology Co., Ltd., Tianjin 300143, China[* This work has been supported by the National Key Research and Development Program of China

  • Article
  • | |
  • Metrics
  • |
  • Reference [17]
  • |
  • Related
  • | | |
  • Comments
    Abstract:

    Great challenges and demands are presented by increasing edge computing services for current elastic optical networks (EONs) to deal with serious diversity and complexity of these services. To improve the match degree between edge computing and optical network, the services awareness function is necessary for EON. This article proposes a Naive long short-term memory (Naive-LSTM) based services awareness strategy of the EON, where the Naive-LSTM model makes full use of the most simplified structure and conducts discretization of the LSTM model. Moreover, the proposed algorithm can generate the probability output result to determine the quality of service (QoS) policy of EONs. After well learning operation, these Naive-LSTM classification agents in edge nodes of EONs are able to perform services awareness by obtaining data traffic characteristics from services traffics. Test results show that the proposed approach is feasible and efficient to improve edge computing ability of EONs.

    Reference
    [1] JI Y, ZHANG J, WANG X, et al. Towards converged, collaborative and co-automatic (3C) optical networks[J]. Science China information sciences, 2018, 61(12):121301.
    [2] PELLW I, PAOLUCCI F, SONKOLY B et al. Latency-sensitive edge/cloud serverless dynamic deployment over telemetry-based packet-optical network[J]. IEEE journal on selected areas in communications, 2021, 39(9):2849-2863.
    [3] ALGHAMDI K, BRAUN R. Deploying hand offmechanism with the software defined network vs mobile IP for 5G network:a feasibility study[J]. Journal of advances in technology and engineering research, 2019, 5(2):79-84.
    [4] LI Y, ZENG Z, LI J, et al. Distributed model training based on data parallelism in edge computing-enabled elastic optical networks[J]. IEEE communications letters, 2021, 25(4):1241-1244.
    [5] YANG H, YAO Q, BAO B, et al. A 3-CS distributed federated transfer learning framework for intelligent edge optical networks[J]. Frontiers in communications and networks, 2021, 2(29):700912.
    [6] LI J, HUA N, ZHONG Z, et al. Flexible low-latency metro-access converged network architecture based on optical time slice switching[J]. Journal of optical communications and networking, 2019, 11(12):624-635.
    [7] VILALTA R, MANSO C, YOSHIKANE N, et al. Experimental evaluation of control and monitoring protocols for optical SDN networks and equipment[J]. Journal of optical communications and networking, 2021, 13(8):D1-D12.
    [8] BAGCI K, TEKALP A. SDN-enabled distributed open exchange:dynamic QoS-path optimization in multi-operator services[J]. Computer networks, 2019, 162:106845.1-106845.10.
    [9] BAI H, LI M, WANG D. Bayesian classifier based service-aware mechanism in 10G-EPON for smart power grid[J]. Acta photonica sinica, 2013, 42(6):668-673.
    [10] BAI H, CHEN W, WANG L, et al. Naive echo-state-network based services awareness algorithm of software defined optical networks[J]. China communications, 2020, 17(4):11-18.
    [11] CUI Z, HENRICKSON K, KE R, et al. Traffic graph convolutional recurrent neural network:a deep learning framework for network-scale traffic learning and forecasting[J]. IEEE transactions on intelligent transportation systems, 2019, 21(11):4883-4894.
    [12] GU R, ZHANG S, JI Y, et al. Network slicing and efficient ONU migration for reliable communications in converged vehicular and fixed access network[J]. Vehicular communications, 2018, 11(1):57-67.
    [13] HUI Y, JIE Z, ZHAO Y, et al. Service-aware resources integrated resilience for software defined data center networking based on IP over flexi-grid optical networks[J]. Optical fiber technology, 2015, 21(1):93-102.
    [14] ZHU R, ZHAO Y, HUI Y, et al. Dynamic time and spectrum fragmentation-aware service provisioning in elastic optical networks with multi-path routing[J]. Optical fiber technology, 2016, 32(12):13-22.
    [15] AGRAWAL A, VYAS U, BHATIA V, et al. SLA-aware differentiated QoS in elastic optical networks[J]. Optical fiber technology, 2017, 36(7):41-50.
    [16] AMARAL P, DINIS J, PINTO P, et al. Machine learning in software defined networks:data collection and traffic classification[C]//IEEE 24th International Conference on Network Protocols, November 8-11, 2016, Singapore. New York:IEEE, 2016:1-5.
    [17] ASSIS K, ALMEIDA R, SANTOS A, et al. Channel-based RSA approaches for QoS protection of slices over elastic optical networks[J]. IEEE access, 2022, (10):20714-20726.
    Related
    Cited by
Get Citation

HUO Chao, BAI Huifeng, YIN Zhibin, YAN Bo. Naive-LSTM based services awareness of edge computing elastic optical networks[J]. Optoelectronics Letters,2023,19(5):279-283

Copy
Share
Article Metrics
  • Abstract:319
  • PDF: 469
  • HTML: 0
  • Cited by: 0
History
  • Received:November 08,2022
  • Revised:January 19,2023
  • Online: June 19,2023
Article QR Code