# Deep Learning Cluster Structures for Management Decisions: The Digital CEO +
## 1 Introduction  
Our brain takes decisions in a structured way while performing several functions at the same time. Our brain learns about the environment from our five senses; it stores memories to preserve our identity; it makes judgements on different situations; it protects itself against external threats or attacks. Our brain is formed by clusters of neurons [1] specialized in learning from different senses where information is transmitted as positive and negative spikes or impulses. It functions with two types of memories [2]; short-term memory is used for quick decisions and task-related actions whereas long-term memory preserves our identity and security. Another brain duality consists of its two operation modes [3]; consciousness under normal activities and unconsciousness under emergency situations such as being under external attack or routine operations like storing information while sleeping.  
This paper presents the association of the most complex biological system; our brain with the most complex artificial system represented in large data networks: The Internet; the information infrastructure of the Big Data and the Web. The link between both of them is the Random Neural Network (RNN). Data networks collect information from users and transmit it to different locations; to perform this activity, they are required to make routing decisions based on different Quality of Service metrics while storing routing tables in memory under the threat of Cyber-attacks.  
This paper proposes a Deep Learning (DL) Cluster Structure for Management Decisions that emulates the way the brain learns and makes choices and combines different Learning Algorithms. The proposed model combines the Random Neural Network Reinforcement Learning for fast localdecisions and DL for long-term memory to remember network identity: QoS metrics (Delay, Loss and Bandwidth) and Cyber keys (User, Packet and Node). In addition, this paper includes a layer of DL management clusters (QoS, Cyber and CEO) that take the final routing decision based on the inputs from the DL QoS clusters and RNN Reinforcement Learning algorithm.  
The Deep Learning Cluster Structures has been applied in the Cognitive Packet Network (CPN) for Quality of Service metrics and Cyber Security keys in Management Decisions based on packet routing and flow control. The RNN Reinforcement Learning Algorithm is chosen under normal or conscious operations due to its fast and adaptable routing learning as short memory whereas DL clusters are selected under external cyber-attacks. Deep Learning clusters take routing decisions based on the long-term memory in unconsciousness operation as safe and resilient, although inefficient and inflexible, routing.  
A concepts review of Cybersecurity, Deep Learning and Deep Reinforcement Learning with their associated literature research is described in Section 2. The mathematical model of the Deep Learning clusters Structures for management decisions is defined in Section 3. The implementation of the QoS, Cyber and Management Clusters is presented on Section 4. The validation of the proposed model under different QoS and Cyber scenarios in small (nine nodes, one decision layer), medium (16 nodes, two decision layers) and large (25 nodes, three decision layers) is described in Section 5. Final discussion and bibliography are shared in Section 6 and References respectively.