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

• Published in: Sustainable computing informatics and systems Vol. 24; p. 100355
• Main Authors: Sharma, Shivi, Saini, Hemraj
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.12.2019
• Subjects: Artificial fractals; Fog computing; Load balancing; Object tracking; Task scheduling; Video surveillance; Fog computing; Task scheduling; Artificial fractals; Video surveillance; Object tracking; Load balancing
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2019.100355

•In tier-1, IoT devices are placed and the application of IoT devices and workloads are transmitted either to fog or cloud tier.•In tier-2, the applications from the IoT devices are divided by router. The classified applications are: high priority and low priority based on dual fuzzy logic algorithm.•In tier-3, a novel fog computing structure is proposed to overcome the node failures, to predict current usage and time interval.•In tier-4, long running tasks are executed and analyzed the performance of proposed architecture with the previous approaches.•Our proposed architecture is evaluated in terms of response time, scheduling time, load balancing rate, delay and energy consumption. 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.