An Energy-Aware Task Offloading and Load Balancing for Latency-Sensitive IoT Applications in the Fog-Cloud Continuum

With the voluminous information being produced by the Internet of Things (IoT) smart gadgets, the consumers with their countless service requests are also growing rapidly. As there is a huge distance between the IoT devices and the Cloud datacenter, some latency is incurred in the communication between the IoT devices and the Cloud datacenter. This latency can be reduced by introducing a Fog layer in between the Cloud and the IoT layer and therefore, it is paramount to offload those tremendous data to leverage the overloaded storage and computation to the Cloud-based systems and Fog-assisted nodes. Moreover, these heavy computations consume significant energy from the distributed Fog servers as well as Cloud datacenters. Therefore, this work addresses the task migration problem in a Fog-Cloud system and load balancing to reduce the latency rate, energy utilized and service time while increasing the resource utilization for latency-sensitive systems. This paper uses a Fuzzy logic algorithm for determining the target layers for offloading considering the resource heterogeneity and the system requirements (i.e., network bandwidth, task size, resource utilization and latency sensitivity). A Binary Linear-Weight JAYA (BLWJAYA) task scheduling algorithm has been proposed to map the incoming IoT requests to computation-rich Fog nodes/virtual machines (VMs). Numerous experimental simulations have been carried out to appraise the efficacy of the suggested method and it is evident that the suggested method outperforms other baselines with an approximate improvement of 26.2%, 12%, 7%, 8.63% and 6% for Resource utilization, Service rate, Latency rate, Energy consumption and Load balancing rate. The presented approach is generic and scalable concerning addressing the unpredictability of data and the associated latency due to the task offloading criteria within the Fog layer.