Читать книгу Fog Computing - Группа авторов - Страница 81

Moving computational resources from the cloud closer to the end nodes stand in the center of the fog and edge paradigm. Therefore, novel resource management to fully utilize the available resources and process applications in close proximity of the user is imperative to the successful adoption of these systems. Since IoT devices are resource-constrained devices, applying resource management techniques at the edge will allow edge nodes to optimize their resource utilization (e.g. energy-aware smart devices that increase their battery levels by of loading computation to other nearby nodes), improve data privacy, and enable devices to collaborate and share resources to process IoT applications.

A taxonomy of resource management at the edge, based on the current state-of-the-art research in this area, is presented in [28]. According to this classification, a total of five different categories are identified considering the objective of the technique.

The first category refers to resource estimation and represents one of the fundamental requirements in resource management, i.e. the capability of estimating how many resources a certain task requires. This is important for handling the uncertainties found in an IoT network and providing at the same time a satisfactory QoS for deployed IoT applications. The second category is represented by resource discovery and aims to aid the user to discover available resources already deployed at the edge. Resource discovery complements resource estimation by keeping the pool of available computational resources updated.

Once the system can estimate and discover resources, a third category appears having the purpose of allocating IoT applications in close proximity to the users. This technique, called resource allocation, utilizes the knowledge of available resources to map parts of the applications at different edge devices such that its requirements are met. There are two different perspectives of the allocation: (1) it represents the initial deployment to the edge of the network, deciding where to map the application; and (2) it serves as a migration technique by self-adapting when a node has failed. Moreover, one challenge arises when sharing resources between distributed edge devices, i.e. a close collaboration between nodes enforced by security and privacy is required. Solving this challenge creates the fourth category, i.e. resource sharing.

Finally, the last technique is called resource optimization and is obtained by combining the aforementioned resource management approaches. The main objective is to optimize the usage of available resources at the edge according to the IoT application constraints. Usually, the developer creates the QoS requirement of his application before deploying it to the edge.