Edge cloud computing service composition based on modified bird swarm optimization in the internet of things

The rapid development of cloud platforms provides a large amount of cloud service resources, which have similar functions and different values of QoS attribute. QoS-based service composition in the edge cloud computing environment faces the main problem that how to combine different cloud services to make global QoS value of service composition to reach the maximization, which is under the premise of meeting the local QoS requirements of edge users. In this paper, the modified bird swarm optimization algorithm is put forward, which introduces the two-order oscillating equation and the historical position memory of the birds on the basis of basic birds swarm optimization. It improves the dynamic parameter mechanism of bird feeding and migration, and enriches the diversity of the birds when moving, and improves the global search ability of the algorithm. By analyzing the results of service combination simulation without local QoS restriction and local QoS restriction, the algorithm can minimize the overall execution time cost of the request under the QoS restriction.