An efficient task of floading mechanism using Genetic Operator Adaptive Firefly Algorithm (GOAFA) in an Edge-Cloud environment

Over the last few decades, cloud has been treated as the popular computing techniques over the internet. This cloud ecosystem should be reliable, and flexible with dynamic load balancing. The users on the internet are becoming billions and the resources are dynamically allocated to them in a demand fashion and their services have increased at an alarming rate. The load balancing is the major issue of cloud computing where the workload is dynamically distributed to every one (nodes) in the network. Moreover, edge computing assists with the cloud to enhance the system performance where the generated data are processed by edge computing nodes. Edge computing makes the computing resources that are closer to the user’s location of the cloud. This will decrease the response time and minimize the network bandwidth. Nevertheless, it is still a challenge to maintain the load balance in edge nodes while minimizing the event completion time. With this perspective, this paper proposed an efficient task offloading method to ensure the load balance of the edge-cloud environment with reduced propagation time and load balance variance. Technically, first, construct the system model and then defined the multi-objective problem using the proposed Meta heuristic algorithm. Afterward, proposed a Genetic Operator Adaptive Firefly Algorithm (GOAFA) to optimize the propagation time and solves issues on load balancing in Edge-cloud environment (ECE). Finally, few simulations are done to prove the efficiency of the proposed system model.