a dynamic load balancing architecture for fog computing using tree base resource arrangement and flexible task prioritization

a greater community of researchers widely studies fog computing as it reduces the massive data flow to the existing cloud-connected network and performs better for real-time systems that expect a quick response. as the fog layer plays a significant role in a fog-cloud system, all of the devices participating in fog computing must be balanced with appropriate load to upstretched the system performance. the proposed method is founded on a tree-based dynamic resources arrangement mechanism that refreshes the fog clusters created using fuzzy c mean (fcm) to increase the speed of resource allocation. with the help of fuzzy rule-based load calculation and intra-cluster job allocation, the load inside the group is maintained. the system also has the facility of inter-cluster job forwarding, which works on demand. a novel load balancing strategy, real-time flexi forwarded cluster refreshing system (rtfrs) is proposed by which all the tasks can be handled efficiently within the fog cloud system. the proposed system is designed so that overall complexity is not upraised and becomes suitable for fog computing architecture with low processing capacity by maintaining the quality of service. experimental results show that the proposed model outperforms standard methods and algorithms used in fog computing concerning average turnaround time, average waiting time, resource utilization, average failure rate, and the load on the gateway.