In cloud architectures, the microservice model divides an application into a set of loosely coupled and collaborative fine-grained services. As a lightweight virtualization technology, the container supports the encapsulation and deployment of microservice applications. Despite a large number of solutions and implementations, there remain open issues that have not been completely addressed in the deployment and management of the microservice containers. An effective method for container resource scheduling not only satisfies the service requirements of users but also reduces the running overhead and ensures the performance of the cluster. In this paper, a multi-objective optimization model for the container-based microservice scheduling is established, and an ant colony algorithm is proposed to solve the scheduling problem. Our algorithm considers not only the utilization of computing and storage resources of the physical nodes but also the number of microservice requests and the failure rate of the physical nodes. Our algorithm uses the quality evaluation function of the feasible solutions to ensure the validity of pheromone updating and combines multi-objective heuristic information to improve the selection probability of the optimal path. By comparing with other related algorithms, the experimental results show that the proposed optimization algorithm achieves better results in the optimization of cluster service reliability, cluster load balancing, and network transmission overhead.INDEX TERMS Ant colony algorithm, cloud computing, container scheduling, microservices, multi-objective optimization.

Performance evaluation of modern cloud data centers has attracted considerable research attention among both cloud providers and cloud customers. In this paper, we investigate the heterogeneity of modern data centers and the service process used in these heterogeneous data centers. Using queuing theory, we construct a complex queuing model composed of two concatenated queuing systems and present this as an analytical model for evaluating the performance of heterogeneous data centers. Based on this complex queuing model, we analyze the mean response time, the mean waiting time, and other important performance indicators. We also conduct simulation experiments to confirm the validity of the complex queuing model. We further conduct numerical experiments to demonstrate that the traffic intensity (or utilization) of each execution server, as well as the configuration of server clusters, in a heterogeneous data center will impact the performance of the system. Our results indicate that our analytical model is effective in accurately estimating the performance of the heterogeneous data center.

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In recent years, Docker container technology is being applied in the field of cloud computing at an explosive speed. The scheduling of Docker container resources has gradually become a research hotspot. Existing big data computing and storage platforms apply with traditional virtual machine technology, which often results in low resource utilization, a long time for flexible scaling and expanding clusters. In this paper, we propose an improved container scheduling algorithm for big data applications named Kubernetes-based particle swarm optimization(K-PSO). Experimental results show that the proposed K-PSO algorithm converges faster than the basic PSO algorithm, and the running time of the algorithm is cut in about half. The K-PSO container scheduling algorithm and algorithm experiment for big data applications are implemented in the Kubernetes container cloud system. Our experimental results show that the node resource utilization rate of the improved scheduling strategy based on K-PSO algorithm is about 20% higher than that of the Kube-scheduler default strategy, balanced QoS priority strategy, ESS strategy, and PSO strategy, while the average I/O performance and average computing performance of Hadoop cluster are not degraded.