Chaotic Particle Swarm Algorithm for QoS Optimization in Smart Communities

In smart communities, computer networks carry a large number of real-time computing tasks such as smart property, smart parking, smart home, etc., and these services are characterized by large data transmission and high concurrency, which require high real-time performance of the network. How to ensure the real-time network to reduce the task scheduling delay is the key problem to be solved when QoS optimization of smart community network. To this end, this paper deeply analyzes the characteristics of smart community task scheduling, firstly, establishes a community computing task scheduling model with computation time and computation cost as the optimization goal; then, proposes the optimization strategy of Chaotic Particle Swarm Algorithm for the stochastic nature of highly concurrent tasks, i.e., based on the basic algorithm of Particle Swarm to add the chaotic strategy in the initialization of the population and the optimization means of the adaptive factor in order to avoid falling into the local optimal and improve the optimization speed; finally, the time and cost overheads under different number of tasks are compared through simulation experiments, and the simulation results verify the effectiveness of the improved algorithm proposed in this paper in network QoS optimization.