# DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework
## 3 Proposed System
### Proposed Framework of Distributed Dynamic Fog Computing System (DD-Fog)  
This framework is designed to integrate cloud structures (MEC and fog), a network infrastructure built on the basis of SDN/NFV technologies (Figure 1), taking into account the level of orchestration. Thus, the combined infrastructure of network and computing resources allows implementing new approaches to the distribution of computations, including at the network edge and on the devices themselves. In this case, we consider an approach to management through the northbound infrastructure interface (application layer of the computational resource orchestrator, application layer of the SDN/NFV network controller (s)). This feature allows you to implement the business logic of both network and service operator services. However, in addition, due to the inherent level of abstraction from physical resources, it is possible to create management applications-services that implement the system and business logic of the operator for managing devices of the controlled infrastructure and resources. In this case, as has already been stated in several scientific articles [16; 17; 18; 19], as well as documents of international standardizing organizations (SDO's), the path of automation, as well as intellectualization of the network, lies through the development of services, data processing algorithms that belong to the Artificial Intelligence class. Thus, the monitoring and control system, which is an infrastructure application (software complex), which implements in itself algorithms of machine \begin{table}
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**Existing Works** & **Problems Addressed** & **Proposed Solutions** \\ \hline \multirow{6}{*}{[14]} & Mobile devices cannot always meet the requirements for organizing of high resource consuming applications, and capability of fog servers are still limited due to the high cost of deployment. & * & To address this issues, we proposed a dynamic policy for mobile cloud DMCCP clusters. \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
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Table 1: _Cont._intelligence as algorithms, makes it possible to automate to a certain extent and, moreover, make the infrastructure intelligent, without the need for constant control and management by people, highly qualified specialists. The resolution of this task will reduce such an indicator as OPEX by reducing the staff of qualified network administrators. Furthermore, it will also allow you to qualitatively change the network, in terms of their reaction to actions from the outside (traffic growth, change in the traffic profile)--the network will adjust to current needs, redistribute the load, taking into account the QoS rules. In addition, such a network will take into account its power characteristics, monitor and provide predictive analytics for the need to increase/decrease its power parameters (network resources, computing resources of the combined cloud infrastructure). As a use case Figure 2 describes the microservices live migration based on the fog resources allocation. The color of the microservices means the different types of services. At the moment, given the global growth of various applications and their demand from users, new software development architectures have been being changed and proposed. The need for new architectural approaches is dictated by high load, complexity, multicomponent solutions, as well as requirements for the speed of service and development of systems. One of the most actively spreading architectural approaches to the development of high-load applications is microservice architecture. This architecture is designed to solve the problem of creating complex systems by decomposition into independent modules.