Περίληψη:
In the era of Industrial Internet of Things (IIoT) and Industry 4.0, an immense volume of heterogeneous network devices will coexist and contend for shared network resources, in order to satisfy the very challenging IIoT applications, requiring ultra-reliable and ultra-low latency communications. Although novel key enablers, such as Network Slicing, Software Defined Networking (SDN) and Network Function Virtualization (NFV) have already offered significant advantages towards more efficient and flexible network and resource management approaches, the particular characteristics of IIoT applications pose additional burdens, mainly due to the complex wireless environments, high number of heterogeneous network devices, sensors, user equipments (UEs), etc., which may stochastically demand and contend for the -often scarce -computing and communication resources of industrial environments. To this end, this paper introduces PRIMATE, a novel, Artificial Intelligence (AI)-driven framework for the profiling of the networking behavior of such UEs, devices, users and things, which is able to operate in conjunction with already standardized or forthcoming, AI-based network resource management processes towards further gains. The novelty and potential of the proposed work lies on the fact that instead of attempting to either predict raw network metrics in a reactive manner, or predict the behavior of specific network entities/devices in an isolated manner, a big data-driven classification approach is introduced, which models the behavior of any network device/user from both a macroscopic, as well as service-specific perspective. The extended evaluation at the last part of this work shows the validity and viability of the proposed framework. IEEE
Συγγραφείς:
Koursioumpas, N.
Barmpounakis, S.
Stavrakakis, I.
Alonistioti, N.
Λέξεις-κλειδιά:
5G mobile communication systems; Application programs; Artificial intelligence; Big data; Forecasting; Heterogeneous networks; Internet of things; Learning systems; Natural resources management; Network function virtualization, Aidriven networking; Context-Aware; Context-aware profiling; Low-latency communication; Network devices; Network resource; Prediction algorithms; Resource allocation.; Resources allocation; Shared network, Resource allocation
