Multi-task equilibrium scheduling of Internet of Things: A rough set genetic algorithm

Abstract

The Internet of Things (IoT) server suffers from numerous business traffic with network bandwidth growth, resulting in downtime. Providing business support without affecting the user experience is the primary problem that IoT companies need to consider and solve in the face of traffic impact. This paper proposes a load balancing scheduling algorithm based on Particle Swarm Optimization Genetic Algorithm (PSO-GA) for IoT clusters. The algorithm uses CPU occupancy rate, memory occupancy rate, network bandwidth occupancy rate, and disk Input and Output (IO) occupancy rate to comprehensively measure the server node load and establish a resource balance model. The fitness function is used to quantify the influence as the basis of weight adjustment. Then, the Particle Swarm Optimization (PSO) algorithm uses the disturbance factor and contraction operator. The optimized algorithm is used to calculate the optimal solution of the fitness function and obtain the optimal weight. Finally, the PSO-GA algorithm is simulated, tested, and compared with the other three load balancing algorithms. As seen from the test results of response delay, throughput, request error rate, and resource utilization, the performance of this algorithm is improved by more than 5% compared with the performance of the traditional method, and the optimization ability is improved obviously. The research content of this paper provides a new way to alleviate the network load, reduce the server overload, congestion, downtime, and other problems, and realize the multi-task balanced scheduling of IoT.