Machine learning based dynamic load balancing DWBA scheme for TWDM PON

Abstract The time and wavelength division passive optical network (TWDM PON) provides broadband services to residential, commercial and industrial users at four different wavelengths. Typically, the industrial users are active in day time and inactive in evening while the residential customers are expected to have quite opposite behavior. This disparity will create an imbalance of traffic load on the wavelengths of TWDM PON. Like IEEE PONs, a load proportional wavelength activation approach cannot work for ITU compliant PONs as ITU standard requires all the wavelengths to be active simultaneously. Moreover, the ONUs cannot be assigned bandwidth on two different wavelengths simultaneously and the migration of an ONU from one wavelength to another causes a tuning delay due to a handshaking mechanism between the OLT and the ONU. Therefore, this work proposes an efficient load balancing dynamic wavelength and bandwidth assignment (LB-DWBA) scheme for TWDM PON to maintain a balanced load between the heavily loaded and least loaded ONUs using a regression based machine learning approach. Simulation results show that LB-DWBA scheme not only reduces the traffic load of heavily loaded wavelengths but also minimizes the bandwidth waste from the excess assigned bandwidth assigned to ONUs using type-5 (T5) traffic class. Overall, the LB-DWBA scheme reduces the average upstream delays of type-2 (T2), type-3 (T3) and type-4 (T4) by 18.9%, 24% and 43% respectively.