An Extensive Study of Scheduling the Task using Load Balance in Fog Computing

Abstract

The proliferation of IoT has resulted in a rise in the demand for services provided by the fog layer, a novel dispersed computing pattern that supplements cloud computing. The fog system enables location awareness and mobility assistance by extending storage and multiplication to the network’s edge, dramatically reducing the issue of service computing in delay-sensitive applications. More requests from more users means more stress for the VMs running in the fog layer. When it comes to fog networks, Load Balancing (LB) is crucial since it prevents some fog nodes from being under- or overworked. Fairly dividing up the fog layer’s burden across the available virtual machines (VMs) is now an absolute must. LB can enhance quality-of-service metrics including cost, response time, performance, and energy ingesting. Although there has been limited investigation of load complementary techniques in fog networks in recent years, no comprehensive analysis has been conducted to compile this information. This article takes a systematic look at the various load-balancing procedures in fog computing, categorizing it as either approximate, precise, fundamental, or hybrid. In addition, the study explores (Load Balancing) LB metrics, including the benefits and drawbacks of the techniques used for fog networks. There is also an examination of the methods and instruments used in the aforementioned evaluations of each research under consideration. The most unanswered questions and emerging tendencies for these algorithms are also covered. In the final section, the study suggests potential avenues for further research.