Abstract

Network operators generally provide dedicated lightpaths for customers to meet the demand for high-quality transmission. Considering the variation of traffic load, customers usually rent peak bandwidth that exceeds the practical average traffic requirement. In this case, bandwidth provisioning is unmetered and customers have to pay according to peak bandwidth. Supposing that network operators could keep track of traffic load and allocate bandwidth dynamically, bandwidth can be provided as a metered service and customers would pay for the bandwidth that they actually use. To achieve cost-effective bandwidth provisioning, this paper proposes an autonomic bandwidth adjustment scheme based on data analysis of traffic load. The scheme is implemented in a software defined networking (SDN) controller and is demonstrated in the field trial of multi-vendor optical transport networks. The field trial shows that the proposed scheme can track traffic load and realize autonomic bandwidth adjustment. In addition, a simulation experiment is conducted to evaluate the performance of the proposed scheme. We also investigate the impact of different parameters on autonomic bandwidth adjustment. Simulation results show that the step size and adjustment period have significant influences on bandwidth savings and packet loss. A small value of step size and adjustment period can bring more benefits by tracking traffic variation with high accuracy. For network operators, the scheme can serve as technical support of realizing bandwidth as metered service in the future.

© 2017 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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  14. L. Velasco, F. Morales, L. Gifre, A. Castro, O. Gonzalez de Dios, and M. Ruiz, “On-demand incremental capacity planning in optical transport networks,” J. Opt. Commun. Netw. 8(1), 11–22 (2016).
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2017 (1)

F. Morales, M. Ruiz, L. Gifre, L. M. Contreras, V. Lopez, and L. Velasco, “Virtual network topology adaptability based on data analytics for traffic prediction,” J. Opt. Commun. Netw. 9(1), 35–45 (2017).

2016 (1)

2015 (3)

2010 (1)

C. Yeo, S. Venugopal, X. Chua, and R. Buyya, “Autonomic metered pricing for a utility computing service,” Future Gener. Comput. Syst. 26(8), 1368–1380 (2010).

2004 (1)

Y. Lee and B. Mukherjee, “Traffic engineering in next-generation optical networks,” IEEE Commun. Surveys Tuts. 6(3), 16–33 (2004).

2003 (1)

K. Zhu, H. Zhu, and B. Mukherjee, “Traffic engineering in multigranularity heterogeneous optical WDM mesh networks through dynamic traffic grooming,” IEEE Netw. 17(2), 8–15 (2003).

2001 (1)

D. Awduche and Y. Rekhter, “Multiprotocol lambda switching: combining MPLS traffic engineering control with optical crossconnects,” IEEE Commun. Mag. 39(3), 111–116 (2001).

Awduche, D.

D. Awduche and Y. Rekhter, “Multiprotocol lambda switching: combining MPLS traffic engineering control with optical crossconnects,” IEEE Commun. Mag. 39(3), 111–116 (2001).

Buyya, R.

C. Yeo, S. Venugopal, X. Chua, and R. Buyya, “Autonomic metered pricing for a utility computing service,” Future Gener. Comput. Syst. 26(8), 1368–1380 (2010).

Castro, A.

Chen, H.

Chua, X.

C. Yeo, S. Venugopal, X. Chua, and R. Buyya, “Autonomic metered pricing for a utility computing service,” Future Gener. Comput. Syst. 26(8), 1368–1380 (2010).

Contreras, L. M.

F. Morales, M. Ruiz, L. Gifre, L. M. Contreras, V. Lopez, and L. Velasco, “Virtual network topology adaptability based on data analytics for traffic prediction,” J. Opt. Commun. Netw. 9(1), 35–45 (2017).

Cui, Y.

Deng, J.

Gifre, L.

F. Morales, M. Ruiz, L. Gifre, L. M. Contreras, V. Lopez, and L. Velasco, “Virtual network topology adaptability based on data analytics for traffic prediction,” J. Opt. Commun. Netw. 9(1), 35–45 (2017).

L. Velasco, F. Morales, L. Gifre, A. Castro, O. Gonzalez de Dios, and M. Ruiz, “On-demand incremental capacity planning in optical transport networks,” J. Opt. Commun. Netw. 8(1), 11–22 (2016).

Gonzalez de Dios, O.

Han, J.

He, R.

Huo, X.

Ji, Y.

Jing, R.

Lee, Y.

Y. Lee and B. Mukherjee, “Traffic engineering in next-generation optical networks,” IEEE Commun. Surveys Tuts. 6(3), 16–33 (2004).

Li, H.

Li, J.

Lin, Y.

Lopez, V.

F. Morales, M. Ruiz, L. Gifre, L. M. Contreras, V. Lopez, and L. Velasco, “Virtual network topology adaptability based on data analytics for traffic prediction,” J. Opt. Commun. Netw. 9(1), 35–45 (2017).

Ma, Y.

Morales, F.

F. Morales, M. Ruiz, L. Gifre, L. M. Contreras, V. Lopez, and L. Velasco, “Virtual network topology adaptability based on data analytics for traffic prediction,” J. Opt. Commun. Netw. 9(1), 35–45 (2017).

L. Velasco, F. Morales, L. Gifre, A. Castro, O. Gonzalez de Dios, and M. Ruiz, “On-demand incremental capacity planning in optical transport networks,” J. Opt. Commun. Netw. 8(1), 11–22 (2016).

Mukherjee, B.

Y. Lee and B. Mukherjee, “Traffic engineering in next-generation optical networks,” IEEE Commun. Surveys Tuts. 6(3), 16–33 (2004).

K. Zhu, H. Zhu, and B. Mukherjee, “Traffic engineering in multigranularity heterogeneous optical WDM mesh networks through dynamic traffic grooming,” IEEE Netw. 17(2), 8–15 (2003).

Peng, Y.

Rekhter, Y.

D. Awduche and Y. Rekhter, “Multiprotocol lambda switching: combining MPLS traffic engineering control with optical crossconnects,” IEEE Commun. Mag. 39(3), 111–116 (2001).

Ruiz, M.

F. Morales, M. Ruiz, L. Gifre, L. M. Contreras, V. Lopez, and L. Velasco, “Virtual network topology adaptability based on data analytics for traffic prediction,” J. Opt. Commun. Netw. 9(1), 35–45 (2017).

L. Velasco, F. Morales, L. Gifre, A. Castro, O. Gonzalez de Dios, and M. Ruiz, “On-demand incremental capacity planning in optical transport networks,” J. Opt. Commun. Netw. 8(1), 11–22 (2016).

Velasco, L.

F. Morales, M. Ruiz, L. Gifre, L. M. Contreras, V. Lopez, and L. Velasco, “Virtual network topology adaptability based on data analytics for traffic prediction,” J. Opt. Commun. Netw. 9(1), 35–45 (2017).

L. Velasco, F. Morales, L. Gifre, A. Castro, O. Gonzalez de Dios, and M. Ruiz, “On-demand incremental capacity planning in optical transport networks,” J. Opt. Commun. Netw. 8(1), 11–22 (2016).

Venugopal, S.

C. Yeo, S. Venugopal, X. Chua, and R. Buyya, “Autonomic metered pricing for a utility computing service,” Future Gener. Comput. Syst. 26(8), 1368–1380 (2010).

Wang, W.

Xiao, M.

Yang, H.

Yeo, C.

C. Yeo, S. Venugopal, X. Chua, and R. Buyya, “Autonomic metered pricing for a utility computing service,” Future Gener. Comput. Syst. 26(8), 1368–1380 (2010).

Yu, X.

Yu, Y.

Zhang, C.

Zhang, J.

Zhao, Y.

Zheng, H.

Zhu, H.

K. Zhu, H. Zhu, and B. Mukherjee, “Traffic engineering in multigranularity heterogeneous optical WDM mesh networks through dynamic traffic grooming,” IEEE Netw. 17(2), 8–15 (2003).

Zhu, K.

K. Zhu, H. Zhu, and B. Mukherjee, “Traffic engineering in multigranularity heterogeneous optical WDM mesh networks through dynamic traffic grooming,” IEEE Netw. 17(2), 8–15 (2003).

Future Gener. Comput. Syst. (1)

C. Yeo, S. Venugopal, X. Chua, and R. Buyya, “Autonomic metered pricing for a utility computing service,” Future Gener. Comput. Syst. 26(8), 1368–1380 (2010).

IEEE Commun. Mag. (1)

D. Awduche and Y. Rekhter, “Multiprotocol lambda switching: combining MPLS traffic engineering control with optical crossconnects,” IEEE Commun. Mag. 39(3), 111–116 (2001).

IEEE Commun. Surveys Tuts. (1)

Y. Lee and B. Mukherjee, “Traffic engineering in next-generation optical networks,” IEEE Commun. Surveys Tuts. 6(3), 16–33 (2004).

IEEE Netw. (1)

K. Zhu, H. Zhu, and B. Mukherjee, “Traffic engineering in multigranularity heterogeneous optical WDM mesh networks through dynamic traffic grooming,” IEEE Netw. 17(2), 8–15 (2003).

J. Lightwave Technol. (1)

J. Opt. Commun. Netw. (4)

Other (13)

F. Morales, M. Ruiz, and L. Velasco, “Core VNT adaptation based on the aggregated metro-flow traffic model prediction,” in Proceedings of Optical Fiber Communication Conference (Optical Society of America, 2017), paper M2G.5.

OpenDaylight [Available Online]: https://www.opendaylight.org/

Wikipedia, RESTful API, https://en.wikipedia.org/wiki/Representational_state_transfer

Wikipedia, POST, https://en.wikipedia.org/wiki/POST_(HTTP) .

JSON (JavaScript Object Notation), http://www.json.org/ .

Wikipedia, GET, https://en.wikipedia.org/wiki/GET_(HTTP) .

“Energy efficiency analysis of the reference systems, areas of improvements and target breakdown,” EARTH Deliverable D2.3, 2012.

Y. Li, Y. Zhao, X. Yu, H. Chen, R. Jing, C. Yu, and R. Cui, “Field trial of data analysis-based autonomic bandwidth adjustment in software defined multi-vendor OTN networks”, in Proceedings of Optical Fiber Communication Conference (Optical Society of America, 2017), paper W1D.4.

J. Zhang, Y. Zhao, H. Yang, Y. Ji, H. Li, Y. Lin, G. Li, J. Han, Y. Lee, and T. Ma, “First demonstration of enhanced software defined networking (eSDN) over elastic grid (eGrid) optical networks for data center service migration,” in Proceedings of Optical Fiber Communication Conference (Optical Society of America, 2013), paper PDP5B.1.

R. Jing, C. Zhang, Y. Ma, J. Li, X. Huo, Y. Zhao, J. Han, J. Wang, and S. Fu, “Experimental demonstration of hierarchical control over multi-domain OTN networks based on extended OpenFlow protocol,” in Proceedings of Optical Fiber Communication Conference (Optical Society of America, 2015), paper W4J.4.

J. Hu, D. Qian, and T. Wang, “Energy Efficient OFDM Transceiver based on Traffic Tracking and Adaptive Bandwidth Adjustment”, in Proceedings of European Conference on Optical Communication (Optical Society of America, 2011), paper We.10.P1.53.

F. Morales, M. Ruiz, and L. Velasco, “Virtual network topology reconfiguration based on big data analytics for traffic prediction”, in Proceedings of Optical Fiber Communication Conference (Optical Society of America, 2016), paper Th3I.5.

L. Gifre, L. M. Contreras, V. López, and L. Velasco, “Big data analytics in support of virtual network topology adaptability”, in Proceedings of Optical Fiber Communication Conference (Optical Society of America, 2016), paper W3F.6.

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Figures (10)

Fig. 1
Fig. 1 Illustration of bandwidth as metered service.
Fig. 2
Fig. 2 Flow chart of DA-ABA scheme in single period.
Fig. 3
Fig. 3 Field trial setup in multi-vendor optical transport networks.
Fig. 4
Fig. 4 Field trial results: (a) web view of DA-ABA application; (b) JSON object of DA-ABA parameters; (c) wireshark capture for starting DA-ABA scheme.
Fig. 5
Fig. 5 (a) Web view of inquiring the adjustment record; (b) JSON object of adjustment record information; (c) wireshark capture for inquiring the adjustment record.
Fig. 6
Fig. 6 Bandwidth adjustment monitored by network analyzer.
Fig. 7
Fig. 7 Results of DA-ABA scheme with different adjustment periods.
Fig. 8
Fig. 8 Results of DA-ABA scheme with different step sizes of bandwidth adjustment.
Fig. 9
Fig. 9 Results of DA-ABA scheme with different threshold number of sampling points.
Fig. 10
Fig. 10 Results of DA-ABA scheme with different upper and lower thresholds.

Tables (1)

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Table 1 Notations

Equations (8)

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α i = { 1 b i p + b c 0 e l s e , i [ 1 , L ]
β i = { 1 b i p b c 0 e l s e , i [ 1 , L ]
b i d = b i / ( 1 r i ) , r i 1
p + ( b c + n 1 Δ b ) i = 1 L b i d α i / i = 1 L α i
b max b c + n 1 Δ b
p ( b c n 2 Δ b ) i = 1 L b i β i / i = 1 L β i
b min b c n 2 Δ b
C = t = 1 24 ( 1 b t ) δ t 0.5

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