Consumed resources for each virtual machine.

Lightweight container technology has emerged as a fundamental component of cloud-native computing, with the deployment of containers and the balancing of loads on virtual machines representing significant challenges. This paper presents an optimization strategy for container deployment that consists of two stages: coarse-grained and fine-grained load balancing. In the initial stage, a greedy algorithm is employed for coarse-grained deployment, facilitating the distribution of container services across virtual machines in a balanced manner based on resource requests. The subsequent stage utilizes a genetic algorithm for fine-grained resource allocation, ensuring an equitable distribution of resources to each container service on a single virtual machine. This two-stage optimization enhances load balancing and resource utilization throughout the system. Empirical results indicate that this approach is more efficient and adaptable in comparison to the Grey Wolf Optimization (GWO) Algorithm, the Simulated Annealing (SA) Algorithm, and the GWO-SA Algorithm, significantly improving both resource utilization and load balancing performance on virtual machines.

轻量级容器技术已成为云原生计算（cloud-native computing）的核心基础组件，而容器部署与虚拟机负载均衡始终是颇具挑战性的研究课题。本文提出一种面向容器部署的优化策略，该策略包含两个阶段：粗粒度（coarse-grained）与细粒度（fine-grained）负载均衡。在初始阶段，本策略采用贪心算法实现粗粒度部署，基于资源请求量将容器服务均衡分布至各虚拟机节点；后续阶段则借助遗传算法开展细粒度资源分配，确保单台虚拟机上的各容器服务均可获得公平的资源配比。该两阶段优化方案可有效提升整个系统的负载均衡性能与资源利用率。实验结果表明，相较于灰狼优化（Grey Wolf Optimization, GWO）算法、模拟退火（Simulated Annealing, SA）算法与GWO-SA算法，本方法具备更优的效率与适应性，可显著提升虚拟机的资源利用率与负载均衡表现。