Читать книгу Fog Computing - Группа авторов - Страница 60

To achieve optimal operation in MFC, the system demands autonomous adjustment and rapid redeployment based on context-awareness and the real-time system process analysis. In particular, considering an MFC system with the large-scale deployment of mFog and iFog nodes, manned optimization becomes impractical and inefficient. In order to overcome such an issue, the system needs to introduce a certain level of self-aware mechanisms to the fog nodes. Specifically, at an early stage, the system manager can preconfigure the basic knowledge to the fog nodes that help the fog nodes to identify the situation at runtime and to adjust or to redeploy the fog service. While the system continuously operates, the fog nodes should support edge intelligence mechanism in which the fog nodes together with the back-end cloud can study the historical records of the operation in order to identify the weak parts and to perform adjustment and redeployment automatically. For example, by enabling edge intelligence on the UAV-Fog nodes, the UAV-Fog nodes are capable of learning when and where to adjust their location, when and where they should migrate or redeploy their services, or when they should reserve their computational resources in order to provide the best QoE to the tenant-side clients.