Based on the network management cost control and the virtual network reconfiguration algorithm, we designed the network overhead crowd optimization space and the management vector.
Secondly, based on the network management cost control and the reconstruction of the Internet virtualization, we designed the network overhead crowd optimization space and management vector.
Network management and control represents a potentially exciting area for investment in the application of artificial intelligence.
Scalability in network management and control and session scheduling is identified as the most important driver in the architecture construct.
The ability to program the network through the high-level abstractions defined by cloud/SDN API allow unprecedented dynamism in service provisioning, network management, and control.
Meanwhile, depending heavily on the low-performance pseudo-measurements, the SE's quality is probably too low to ensure an effective network management and control.
Motivated by the idea of divide-and-conquer, in this article, we first present a multiple cognitive agent-based divide-and-conquer network management and control architecture.
Wireless big data significantly challenges the current network management and control architecture, mathematical modeling techniques, and distributed algorithm design, in particular, in the promising cognitive, distributed, and ultra-dense networks.
Specifically, network management (NM) and control applications require access to and control of network device state.
Therefore, besides the conventional network management functions like admission control, congestion control, and handover, a CRN must implement at least two additional functions: spectrum sensing - detecting spectrum holes in the licensed spectrum bands; and spectrum sharing - deciding proper spectrum allocation scheme among existing PUs and SUs.
