A novel four-tier architecture for delay aware scheduling and load balancing in fog environment

Abstract

Fog computing paradigm is located between IoT devices and cloud paradigm that aim is to minimize the latency in terms of task scheduling and load balancing. To deal with the huge amount of data sensing from different IoT devices, in this paper we propose a four tiers architecture for delay aware scheduling and Load Balancing in the fog environment. Tier-1 is the bottom tier which consists of IoT devices. In the second tier, the applications (workloads) are categorized into two categories: High Priority (HP) and Low Priority (LP) by router based on the Dual Fuzzy Logic Algorithm. Fuzzifier considers four input metrics: task size, arrival time, minimum execution time and maximum completion time. A task with high priority is transmitted to the third tier (fog tier). In the third tier, a novel fog structure has been invoked namely Artificial Fractals consists of nodes. The fog nodes are clustered using K-means++ clustering algorithm. Each fog node is carried out several actions such as scheduling, monitoring, and communication. To schedule tasks within the fog node, we propose the Earliest Deadline First (EDF) task scheduling algorithm. The current usage of fog node is determined by Artificial Neural Network (ANN). If an IoT device does not get the required resource then the request is forwarded to the cloud tier. Our proposed work has been validated over a real-time VSOT (Video Surveillance/Object Tracking) application using iFogSim and the performance is evaluated in terms of response time, scheduling time, load balancing rate, delay, and energy consumption.