Decision‐making support for an autonomous software‐defined network orchestrator. Issue 1 (2nd December 2021)

• Record Type: Journal Article
• Authors: Saadon, Guy; Haddad, Yoram; Dreyfuss, Michael; Simoni, Noemie
• Is Part Of: IET networks. Volume 11:Issue 1(2022)
• Page Start: 13
• Page End: 26
• Publication Date: 2021-12-02
• Subjects: decision making -- next generation networks -- numerical analysis -- optimisation -- software defined networking -- telecommunication network management; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/ntw2.12031
• Languages: English
• ISSNs: 2047-4954

Abstract

Abstract: The deployment of 5G and IoT networks has already started. Considering their growing complexity, such networks require complete autonomy, that is, without human intervention. For this purpose, an orchestration application has been introduced in the field of network management. This application automates, coordinates, and manages network resources, supporting countless demands for services and applications. In this context, complex rule‐driven and artificial‐intelligence based methods are investigated to help orchestrators make accurate decisions in becoming a zero‐touch network. Thus, a decision‐making process at the orchestrator level is important. However, rules defined at the level of the orchestrator can generate delays and yield loops, contradictions, redundancies, or even deadlocks in a network. In this study, a novel mathematical function, located at the orchestrator of the network resources, is presented to support decision‐making in selecting the most relevant rule to apply under a contention scenario. A network requires permanent monitoring; thus, if some rules appear to be ineffective or dangerous for network autonomy in certain cases, they will be eliminated. Under this function, different applicable rules are compared, and the most efficient rule for optimally allocating the required resources is selected. Based on a numerical analysis, we show how the usage of this decision process creates a more resilient and autonomous network.