Machine learning-Based traffic offloading in fog networks
| Autor: | Radu-Ioan Ciobanu, George-Eduard Zaharia, Ciprian Dobre, Tiberiu-Alex-Irinel Şoşea |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Network packet business.industry Computer science 020208 electrical & electronic engineering 02 engineering and technology User requirements document Data rate 020901 industrial engineering & automation Wide area Hardware and Architecture Modeling and Simulation 0202 electrical engineering electronic engineering information engineering Cellular network business Software Computer network |
| Zdroj: | Simulation Modelling Practice and Theory. 101:102045 |
| ISSN: | 1569-190X |
| DOI: | 10.1016/j.simpat.2019.102045 |
| Popis: | Wi-Fi offloading in fog networks is believed to be one of the best ways to solve the significant data increase in cellular networks, since nodes located close by are used as relays for offloading traffic and computations. This alarming growth has affected these networks and has put its mark on their performance. Some operators might try upgrading the wide area networks, but in most scenarios, this is not the most cost-effective and optimal solution. These operators would benefit more from intelligent offloading solutions. Therefore, we can use Wi-Fi networks to send some of the packets, thus releasing cellular networks and decongesting traffic. This paper deals with offering a complete offloading solution and presents various profiles aimed at different purposes: saving the battery, getting the maximum data rate, or balancing the two, as well as offering a simulator that reproduces the behavior of the devices in an environment as close to reality as possible. Through extensive analysis, we show that the proposed solutions are able to improve certain metrics based on user requirements. |
| Databáze: | OpenAIRE |
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