Abstract

This study presents a dynamic inter wavelength migration scheme for the optical network units (ONUs) employing linear regression machine learning method to equalize the traffic volume on all the wavelengths in time and wavelength division multiplexed passive optical network (TWDM PON). The proposed traffic-adaptive wavelength and bandwidth assignment (TA-WBA) scheme not only decreases upstream traffic delays but also offers 2.3% and 30% less delay on the wavelengths balancing the excessive load and 7% less upstream bandwidth waste, when evaluated against other load-balancing scheme.

Highlights

  • The demand for high-speed data services such as Netflix, Bit Torrent, YouTube, and Hulu is rapidly growing, and the overall trend suggests that global internet traffic would hit hundreds of exabytes around 2021 [1]

  • This work is inspired by the earlier similar studies [11], [12] presented for traffic load balancing in TWDM PON, which opted for an extreme end traffic balancing approach

  • The mλ and Cy are computed from Eq (2) and Eq (3) using the recorded traffic load values (Q(i )) recorded during the last service interval (SI) for each λ(i ), where ‘M’ is the total active wavelengths of TWDM PON being used by the OLT

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Summary

Introduction

The demand for high-speed data services such as Netflix, Bit Torrent, YouTube, and Hulu is rapidly growing, and the overall trend suggests that global internet traffic would hit hundreds of exabytes around 2021 [1]. The domestic users’ traffic is expected to be minimal in the morning, due to users being out to their work, and at peak in the evening, as they will be back home This heterogeneity trend in the use of traffic could give rise to a traffic load imbalance on the various TWDM PON wavelengths serving residential and industrial areas. This work is inspired by the earlier similar studies [11], [12] presented for traffic load balancing in TWDM PON, which opted for an extreme end traffic balancing approach In these approaches, the ONU with the highest traffic load of the most burdened wavelength is transferred to the other wavelength having the minimum traffic load. This wavelength switching mechanism adds the tuning delay in the US transmissions

Traffic Load Based Classification of Wavelengths
Calculation of Error Vectors
Formation of ONU Migration Queue
Inter-Wavelength ONU Migration
Simulation Parameter Setting
Results and Discussion
Conclusion
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