Optimal Task Offloading and Resource Allotment Towards Fog-Cloud Architecture

Advancement in IoT research encouraged the generation of ubiquitous computing devices and changed their way of utilization. The high spawning of data through these computing devices and its computation demands the evolvement of a new paradigm. Instead of using a centralized cloud-based system, a new distributed computing environment has been emerged for feature computational and storage capabilities at the periphery of the network called fog computing. The main idea behind fog computing is to utilize the processing and storage resources of fog nodes by offloading the computation-intensive IoT tasks. In the fog-enabled Cloud/IoT architecture, resource-constraint IoT devices offload their resource-hungry tasks to nearby deployed fog nodes to reduce the processing time and fast execution. Fog enabled processing guarantees fast response time and better energy management. Therefore, we are proposing a optimal offloading decision strategy and resource allocation scheme in fog atmosphere by minimizing the total energy consumption of IoT users concerning the latency of communication and computation. To optimally offload the tasks to fog node an optimization problem is designed and an improved version of branch-and-bound tree is proposed for its evaluation. Some of the metrics we consider for performance evaluation are average task latency, average energy consumption per task and average remaining energy per IoT node. Simulation results show that using proposed offloading decision strategy, network performance improve significantly.