A container deployment strategy for server clusters with different resource types

Abstract

The method of deploying microservices based on container technology is widely used in cloud environments. This method can realize the rapid deployment of microservices and improve the resource utilization of cloud datacenters. However, resource allocation and deployment of container-based microservices are key issues. With the continuous growth of computing-and storage-intensive services, it is necessary to consider the deployment of microservices of different business types. This study establishes a multi-objective optimization problem model with the similarity between containers and servers, load balance of clusters, and reliability of microservice execution as the optimization objectives. An improved artificial fish swarm algorithm is proposed for the container deployment of computing-and storage-intensive microservices. The comprehensive experimental results show that, compared with the existing deployment strategies, the matching degree between the container and server, cluster load balance value, service execution reliability, and other performance parameters are improved while shortening the running time of the algorithm. In addition, under the constraint of load balancing, the resource utilization of the computing and storage server clusters is improved.

FPHO: Fractional Pelican Hawks optimization based container consolidation in CaaS cloud

“…The average reaction time of our framework is 13.97% faster than systems without rescheduling, according to experiments. Ouyang et al, in Reference 37, proposed a container deployment strategy using a superior artificial fish swarm method containerizes computing‐ and storage‐intensive microservices. The thorough testing findings demonstrate that the method runs quicker and improves container‐server matching degree, cluster load balance value, service execution reliability, and other performance characteristics compared to current deployment techniques.…”