Cost optimization model for multi-cloud network based on Kubernetes - Ming GAO; Ming LIU; Yangting CHEN; Weiming WANG (Dianxin kexue)
The cloud-native scheduling system, represented by Kubernetes, is widely used by cloud tenants in a multi-cloud environment.The problem of network observation becomes more and more serious, especially the cost of network traffic across cloud and region.In Kubernetes, the eBPF technology was introduced to collect the network data features of kernel state of operating system to solve the network observation problem, and then the network data features were modeled as QAP, a combination of heuristic and stochastic optimization was used to obtain the best near optimal solution in a real-time computing scenario.This model is superior to the Kubernetes native scheduler in the cost optimization of network resources, which is based on the scheduling strategy of computing resources only, and increases the complexity of scheduling links in a controllable range, effectively reduces the cost of network resources in a multi-cloud area deployment environment.