Secure Delay Aware Scheduling and Load Balancing along with Reduced Energy for Deadline Sensitive Applications in Fog Computing Environment

Abstract

Fog Computing (FC) is the extension of cloud computing (CC) to meet the need of modern age technologies like the Internet of Things (IoT), Artificial Intelligence (AI), 5G, and other such aspects. Fog Computing extends its services to cloud computing by storing the data on Fog Nodes (FN) locally instead of increasing the burden on the cloud. The chief feature of Fog Computing is to present the user with the most ideal elucidation which is efficient and fast. The major critical issues that Fog Computing experience are that of load balancing (LB) and energy consumption (EC). The present thesis has designed a framework for scheduling deadline sensitive applications to provide an efficient solution for Load balancing using optimization techniques and to minimizing energy consumption in Fog Computing. Further, it has focused on designing the Four-Tier Architecture in fog computing environment for Delay Aware Scheduling and Load Balancing (DASLB) and developed a model to provide Fog-Assisted Task-Allocation and Secure De-duplication (FATASD) using Two-Fitness based One-to-One Matching Algorithm (2FBO2) and Multi-Objective Whale Optimization Algorithm (MoWo) in Cluster-based IIoT (Industrial Internet of Things). Fog-assisted has been a topic of interest in the research community. With the upsurge in the utilization of IoT devices, the transmission of duplicate data increased. To avoid this, the present research has used Cluster-based IIoT for the implementation of de-duplication and task allocation on fog layers. The first objective of the present study is to design a framework for scheduling deadline sensitive applications. This has been achieved in the study in two stages. In the first stage, an effective solution has been designed for load balancing using optimization techniques. The present study has thus focused on the drop in the runtime of schedule length and minimization of energy consumption rate.