For many years, passive optical networks (PONs) have received a considerable amount of attraction regarding their potential for providing broadband connectivity to almost every citizen, especially in remote areas where fiber optics can attract people to populate regions that have been abandoned. Error-free connectivity without dropouts can offer new opportunities to communicate, earn money and enjoy cultural events. Transmission speeds are multigigabit with distances of a few tens of kilometers; these specifications were previously reserved for high-speed and long-haul backbone networks. PONs can also support a new class of applications, such as accurate time transfer or distributed fiber sensing and follow new trends in open networking. An outline of past and current standards and standards that have been proposed for the latest generation of multigigabit PONs is provided.

This paper presents an implementation of the parallelization of genetic algorithms. Three models of parallelized genetic algorithms are presented, namely the Master–Slave genetic algorithm, the Coarse-Grained genetic algorithm, and the Fine-Grained genetic algorithm. Furthermore, these models are compared with the basic serial genetic algorithm model. Four modules, Multiprocessing, Celery, PyCSP, and Scalable Concurrent Operation in Python, were investigated among the many parallelization options in Python. The Scalable Concurrent Operation in Python was selected as the most favorable option, so the models were implemented using the Python programming language, RabbitMQ, and SCOOP. Based on the implementation results and testing performed, a comparison of the hardware utilization of each deployed model is provided. The results’ implementation using SCOOP was investigated from three aspects. The first aspect was the parallelization and integration of the SCOOP module into the resulting Python module. The second was the communication within the genetic algorithm topology. The third aspect was the performance of the parallel genetic algorithm model depending on the hardware.

This article presents a numerical implementation of the activation process for gigabit and 10 gigabit next generation and Ethernet passive optical networks. The specifications are completely different because GPON, XG-PON and NG-PON2 were developed by the International Telecommunication Union, whereas Ethernet PON was developed by the Institute of Electrical and Electronics Engineers. The speed of an activation process is the most important in a blackout scenario because end optical units have a timer after expiration transmission parameters are discarded. Proper implementation of an activation process is crucial for eliminating inadvisable delay. An OLT chassis is dedicated to several GPON (or other standard) cards. Each card has up to eight or 16 GPON ports. Furthermore, one GPON port can operate with up to 64/128 ONUs. Our results indicate a shorter duration activation process (due to a shorter frame duration) in Ethernet-based PON, but the maximum split ratio is only 1:32 instead of up to 1:64/128 for gigabit PON and newer standards. An optimization improves the reduction time for the GPON activation process with current PLOAM messages and with no changes in the transmission convergence layer. We reduced the activation time from 215 ms to 145 ms for 64 ONUs.

Sophisticated dynamic bandwidth allocation (DBA) algorithms can dramatically improve or worsen throughput and delay in whole networks. It is very important to choose the right DBA algorithm. Our work tests static assignment and three DBA algorithms, namely GigaPON Access Network DBA, Hybrid Reporting Allocation, and modified Max–Min Fair. All tests were made on our simulator of ten-gigabit passive optical network DBA specially developed for testing DBA algorithms. The tests verify delay of each optical network unit and amount of waste with bandwidth. This paper describes how the used DBA algorithms work and the processes involved in DBA algorithms.

This article presents a design of a database model used to gather and analyze data frames transmitted over gigabit passive optical network (GPON) in the downstream direction. An issue with this kind of system is the difficulty in analyzing a transmission on the optical part which is caused by the difference among devices using Ethernet frames technology and passive optical network technology with usage gigabit encapsulation method. In this article, a principle of the downstream direction is described. Next, the design of the database model for the analysis of transmitted data is discussed. Based on the design and implementation of the database, a script capable of processing data gathered by a programmable network card is proposed. The script for physical layer operation, admission, and maintenance (PLOAM) messages analysis is written in the Python programming language.

Modern genetic algorithms are derived from natural laws and phenomenons and belong to evolutionary algorithms. Genetic algorithms are, by their very nature, suitable for parallel processing that leads to increased speed and to optimization. The paper deals with selected ways of parallelization of genetic algorithms with subsequent implementation. Parallelization brings an increase in algorithm speed and load distribution, which is compared to a serial model. Python language is used for demonstration. Four Python modules have been selected to provide parallel processing. They are the Global One — Population Master-Slave Model, the One-Population Fine-Grained Model, the Multi-Population Coarse-Grained Model, and the Hierarchical Model.