Читать книгу The Smart Cyber Ecosystem for Sustainable Development - Группа авторов - Страница 51

SDN has strengthened applying programmatic principles on network, allowing network administrators to have precise, flexible, and innovative control of the network and thus reducing operational expenses.

The SDN architecture provides an opportunity to more efficient application of cognitive network concepts in a centralized system, leading to self-aware networks. The adoption of SDN-based systems highly depends on their success in providing solutions to problems that could not be solved by traditional network architectures and protocols [12].

Applying ML techniques with SDN is considered to be effective for the following reasons [13]:

 The recent advanced developments in computing and the accompanying advanced processors, thus creating a new opportunity to apply promising learning techniques.

 It is well known that ML algorithms depend on data. The SDN controller has a holistic view on the network and is able to collect different network data, simplifying the application of ML algorithms.

 Based on the ability of the SDN to act in real time and deal with historical data, ML techniques can create intelligence in the controller unit, by conducting data analysis relying on analyzed data in decision-making and thus improving the network and its services.

 The programmatically feature of SDN can help to find optimal solutions to network problems such as configuration and resource allocation. Thus, ML algorithms can be implemented in real time.