Deep Learning Cluster Structures for Management Decisions: The Digital CEO.

Area of Science:

• Computer Science
• Artificial Intelligence
• Network Engineering

Background:

• Traditional network routing struggles with complex, dynamic environments.
• Emulating biological learning mechanisms offers potential for more adaptive network management.

Purpose of the Study:

• To present a novel Deep Learning (DL) Cluster Structure for management decisions in networks.
• To emulate brain-like information processing and decision-making for packet routing.

Main Methods:

• The proposed model integrates Random Neural Network (RNN) Reinforcement Learning for local decisions and Deep Learning for memory.
• The DL Cluster Structure was applied to Cognitive Packet Network (CPN) routing, utilizing Quality of Service (QoS) and Cyber Security metrics.
• A management layer of DL clusters (QoS, Cyber, CEO) made final routing decisions.

Main Results:

• The DL Cluster Structure demonstrated promising performance in simulations across various network sizes and scenarios.
• The model effectively integrated QoS and security metrics for adaptive routing.
• The system showed an ability to learn and make informed packet routing decisions.

Conclusions:

• The developed DL Cluster management structure represents a significant step towards brain-emulating network systems.
• This approach offers a new mechanism for intelligent packet transmission, learning, and routing.
• The findings suggest a viable pathway for creating more autonomous and adaptive communication networks.