Traffic-Adaptive Inter Wavelength Load Balancing for TWDM PON

Memon, Kamran Ali; Zhang, Qi; Butt, Rizwan Aslam; Mohammadani, Khalid Hussain; Faheem, Muhammad; Ain, Noor Ul; Tian, Feng; Xin, Xiangjun

doi:10.1109/jphot.2019.2962498

Cited by 7 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [15,16] , the authors claim that ML technology will become an important means to providing indispensable technical support to further increase communication capacity and improve future communication stability by effectively identifying physical impairments in the network [17]. In the new generation passive optical networks (PONs) [18], ML-based algorithms have also been successfully applied, particularly to the load-balancing problem [19].…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Study of Machine Learning Application to Transmission Quality Assessment in Optical Networks

et al. 2023

View full text Add to dashboard Cite

This paper examines applying machine learning to the assessment of the quality of the transmission in optical networks. The motivation for research into this problem derives from the fact that the accurate assessment of transmission quality is key to an effective management of an optical network by a network operator. In order to facilitate a potential implementation of the proposed solution by a network operator, the training data for the machine learning algorithms are directly extracted from an operating network via a control plane. Particularly, this work focuses on the application of single class and binary classification machine learning algorithms to optical network transmission quality assessment. The results obtained show that the best performance can be achieved using gradient boosting and random forest algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

A Comprehensive Study of Machine Learning Application to Transmission Quality Assessment in Optical Networks

et al. 2023

View full text Add to dashboard Cite

show abstract

“…2), the RRUs are directly connected to the ONUs, the RRU requests would be buffered in the corresponding ONUs and transmitted to the OLT. Hence, for simplicity, it is reasonable to assume the arrival process of RRU requests follows Poisson process as in the ONUs [27][28][29]. Specifically, Poisson traffics for RRU requests with an average rate of λ packets/s are redirected to BBUs under the BBU controller.…”

Section: B Calculation Model Of Energy-saving Scheme For Bbu Poolmentioning

confidence: 99%

Delay-Aware Energy-Saving Strategies for BBU Pool in C-RAN: Modeling and Optimization

Zhu

Zeng

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The surging mobile traffic poses serious challenges for mobile operators and the advancement of information and communication technologies (ICT). The cost and energy consumption cannot support affordable mobile services and sustainable growth. As a new architecture, cloud-based radio access network (C-RAN) is proposed to confront these challenges. C-RAN is a deployment paradigm that seeks to isolate baseband unit (BBU) from its remote radio unit (RRU) in base station (BS), consolidating the BBUs into a common place (i.e. BBU pool). In the BBU pool, the computing resources provided by the BBUs can be dynamically assigned to RRUs on demand by the BBU controller. Thus, with the fluctuation of data traffic from RRUs, a part of BBUs can be dynamically turned on or off. As a result, the energy consumption of the BBU pool can be reduced correspondingly. In this paper, we design a Markov chainbased calculation model to formulate the energy-saving operation of the BBU pool with an active/sleep mode under dynamic RRU traffic load. On this basis, we propose two different BBU state transition strategies for the BBU pool, i.e., "Cautious ON, Bold OFF (CO-BF)" strategy and "Bold ON, Cautious OFF (BO-CF)" strategy. We formulate the key performance indicators in terms of the energy saving efficiency (ESE), the consequent packet queuing delay (PQD) and the normalized tradeoff cost as well. In extensive simulations, we investigate the effect of these key parameter indicators on the system performances of the BBU pool. Achieving the minimum normalized cost, an optimal operating strategy for the BBU pool can be determined by adjusting the parameter values of the (N, M). The simulation results show when RRU traffic load is light (e.g., λ = 0.1), the proposed BO-CF strategy can lead to the minimum normalized cost, which corresponds to ESE of up to 75.4% and PQD of 0.0097ms. For heavier load (e.g., λ = 0.4), the minimum normalized cost can be obtained along with ESE of up to 74.1% and PQD of 0.1241ms, when the CO-BF strategy is adopted. Hence, we observe that the BO-CF strategy is slightly better than the CO-BF for the light traffic load of the BBU pool, while the CO-BF strategy would be more suitable for the BBU pool with the heavier traffic load. It proves the efficacy of our proposed Markov chain-based calculation model to pursuit an optimal operating strategy for the delay-aware and energyefficient BBU pool.INDEX TERMS Cloud-based radio access network (C-RAN), Energy-saving, Sleep mode, QoS, Markov chain.

show abstract

“…When the wireless medium is not busy, the STA transmits frames directly to the wireless medium without waiting for the time-period termed as back-off time (T BO ), resulting in a delay of transmission of a random time-period, T BO . A value of the T BO is uniformly obtained using Equation (1), where the R Counter , known as back-off (BO) random counter, is chosen between zero and CW-1 using Equation (2), and T Slot is the Slot-Time parameter for the BO process. In case of unsuccessful transmission, STA repeats the BO process and the size of CW by a factor of 2 N -1 in each re-transmission up to the maximum contention window (CW Max ) size, following Equation (3) [30].…”

Section: Distributed Channel Schemas Of 80211ac and Xgponmentioning

confidence: 99%

“…With the rapid increase in bandwidth-hungry applications, Internet devices, and the emergence of online business and gaming, the demand for high-speed internet with mobility is continuously growing [1]. The requirement of mobility and higher bandwidth at the same time requires the integration of wired and wireless broadband technologies.…”

Section: Introductionmentioning

confidence: 99%

Highest Cost First-Based QoS Mapping Scheme for Fiber Wireless Architecture

et al. 2020

View full text Add to dashboard Cite

The combination of a high-speed wireless network with passive optical network technologies has led to the evolution of a modern integrated fiber wireless (FiWi) access network. Compared to broadband wireless networks, the FiWi network offers higher bandwidth with improved reliability and reduced maintenance costs due to the passive nature of passive optical network (PON). Since the quality of service (QoS) is a baseline to deploy high-speed FiWi broadband access networks, therefore, it is essential to analyze and reduce the typical problems (e.g., bandwidth and delay) in the high-speed next-generation networks (NGANs). This study investigates the performance of a fiber wireless architecture where a 10-Gigabit-capable passive optical network (XGPON) and fifth generation of wireless local area network (WLAN) (i.e., IEEE 802.11ac) are integrated. Both technologies take benefits from each other and have pros and cons concerning the QoS demands of subscribers. The proposed work offers a very flexible QoS scheme for the different types of services of 5G WLAN and XGPON with the help of the highest cost first (HCF) algorithm, which leads to reduced upstream delays for delay-sensitive applications. The simulation results show that the HCF algorithm boosts the performance of the dynamic bandwidth assignment (DBA) scheme and results in up to 96.1%, 90.8%, and 55.5% reduced upstream (US) delays for video: VI(T2), background: BK(T3), and best effort: BE(T4) traffic in enhanced-distributed-channel-access (EDCA) mode. Compared to earlier work, the HCF and immediate allocation with the colorless grant (IACG) DBA combination results in the reduction of up to 54.8% and 53.4% mean US delays. This happens because of 50% to 65% better bandwidth assignment by the IACG DBA process due to efficient mapping by the HCF algorithm.

show abstract

Traffic-Adaptive Inter Wavelength Load Balancing for TWDM PON

Cited by 7 publications

References 13 publications

A Comprehensive Study of Machine Learning Application to Transmission Quality Assessment in Optical Networks

A Comprehensive Study of Machine Learning Application to Transmission Quality Assessment in Optical Networks

Delay-Aware Energy-Saving Strategies for BBU Pool in C-RAN: Modeling and Optimization

Highest Cost First-Based QoS Mapping Scheme for Fiber Wireless Architecture

Contact Info

Product

Resources

About