A Secure and Distributed Placement for Quality of Service-aware IoT Requests in Fog-Cloud of Things: A Novel Joint Algorithmic Approach

The fast proliferation of internet-enabled devices generates massive amounts of data every day from every aspect of life. These internet-enabled devices lack the storage, processing power, and capacity necessary to handle and store this massive amount of accurate and volumetric data. Cloud computing has been proposed as a compelling substitute to process these requests. However, the ingress traffic to the Cloud is huge which causes latency overhead due to the gap that exists between end devices and the Cloud datacentre. Additionally, processing these dynamic and heterogeneous requests with disparate requirements is a computationally NP-hard problem. In this regard, Fog computing appears to be an appealing solution to surpass the aforementioned challenges as a complementary to Cloud. Therefore, this research integrated a Fog layer between the end devices and Cloud datacenters enabling a collaborative computation framework. For the classification of requests and determining the target layers for processing, this research suggests an Adaptive Neuro-Fuzzy Inference System (ANFIS). Furthermore, an improved Honey Badger Algorithm (IHBA) is implemented for scheduling those requests at the target layer. To improve the convergence rate of the traditional HBA, a chaos mapping function has been implemented with an Opposition-based Learning (OBL) method. The proposed task consolidation approach has been validated using extensive simulations for QoS parameters on real-world benchmark datasets. With percentage improvements of 13.69%, 28.4%, 33.85%, 7.66%, 26.48%, and 5.63% for makespan, resource utilization, energy consumption, service delay, service cost, and delay violation, respectively, the obtained simulation results show that the suggested algorithm works better than the current state-of-the-art.