# From Cyber-Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs
## 7 Edge Computing Digital Twins--Special Use Cases
### A Choice Modeling-Based DT for Edge Computing Platforms
#### 7.1.1 Modeling Behavioral Model for Day 3 Edge Computing Operations  
Wireless networks have become complex, and the evolution from the current 5G networks to 6G networks is going to increase the complexity, making network management a daunting task. This requires day 3 management, which are more strategic approaches to network operations that focus on intent-based networking. In wireless communication, day 2 operations can be viewed in terms of system operations throughout its life cycle, with its behavior being analyzed and optimized continuously [115]. Thus, day 2 services are aimed at improving the QoS and sharing perception and awareness information. On the other hand, day 3 operations are intent-based operations, which means that there is an addition of further sophistication to day 2 operations such as sharing intentions [116]. This kind of intent-based or intent-sharing kind of operation supports negotiation and cooperation, which opens the door to cooperative perception in wireless networking. Edge platforms are an emerging edge computing paradigm widely recognized as a promising solution towards meeting the diverse edge computing demands [111].  
Using this emerging paradigm, the operation of the traditional edge computing will be improved through the use of low-cost edge centers that use cognitive choice modeling. This is an intelligent edge management strategy for day 3 operations and beyond that use the concepts of cognitive choice modeling through prospect theory as shown in Figure 5 below.  
In Figure 5a above, the operation of edge platforms in terms of day 3 and beyond operations, where edge platforms are leveraged for traffic optimization, is shown. The formulation of the edge platform for day 3 operations is aimed at (i) improving user and gNB-server cooperation towards intelligent decision making in terms of user-gNB for traffic offloading and (ii) improve resource provisioning through on-demand container provisioning by the data center [31]. It must be noted that the data center hosts a number of virtualized computational resources that account for the total computational resources of the whole system. With this kind of on-demand provisioning of containerized computational resources, (i) QoS can be improved in terms of latency minimization, (ii) bandwidth can be increased, and (iii) there is no under/over-provisioning of computational resources. In terms of day 3 and beyond edge operations, system state changes dynamically, hence the behavior of the system must be monitored continuously for proper analysis. For this purpose, a DNN agent monitors the states of the different edge sites, i.e., edge site 1, edge site 2, up to edge site \(N\) as follows:\[s\triangleq\{s_{1}(t),s_{2}(t),\cdots,s_{N}(t)\}, \tag{1}\]  
are fed into the DNN agent as inputs, where the process of computing the behavioral model proceeds as follows:  
1. the DNN takes the states of the computational task queue, which are estimates of congestion as well as those of incident traffic types;
2. the output of the DNN is the behavioral model for the provisioning of the computational resources, which is as \(\phi(s(t),\sigma^{*}(t))\), with \(\varphi^{*}(t)\) as the control action that determines the provisioning of computational resources;
3. the behavioral model instructs the regional data center on the computational requirements of the different edge sites, which then launches containerized computational resources based on the respective demands.  
Therefore, by taking into account (i) the workload size (determined by queue lengths), (ii) cycle uncertainties, and (iii) unpredictable emergencies, a DT for the RAN can be designed.