Rate-Adaptive Fog Service Platform for Heterogeneous IoT Applications

With the advancement of the Internet of Things (IoT) technologies, the number of heterogeneous IoT applications requiring a variety of resources and services is increasing dramatically. Recently, the introduction of fog computing has further unlocked the potential of real-time services within the IoT context. On the basis of fog architecture, we herein propose a novel rate-adaptive fog service platform aiming at heterogeneous services provisioning and optimized service rate allocation. By forming several service groups in the fog network in which each service could be adequately provisioned, service consumers would always benefit from the fact that the majority of services produced by the IoT applications are in their proximity and thus are delivered to the destination promptly. Taking advantage of the well-known network utility maximization (NUM) approach, a service rate-adaptive algorithm is developed to empower fog nodes working together to adjust service delivery rate dynamically. Throughout this process, the algorithm takes the current network condition and constraint into account to ensure the rate is calibrated in favor of providing satisfactory quality of service (QoS) to each service receiver at the same time. Compared to other resource allocation strategies that mainly focus on allocating resources for a single network service, our proposed platform is capable of not only dealing with both the elastic and inelastic services but also handling the abrupt network changes and converging back to the global optimum rapidly.