An application based on a microservice architecture with a set of independent, fine-grained modular services is desirable, due to its low management cost, simple deployment, and high portability. This type of container technology has been widely used in cloud computing. Several methods have been applied to container-based microservice scheduling, but they come with significant disadvantages, such as high network transmission overhead, ineffective load balancing, and low service reliability. In order to overcome these disadvantages, in this study, we present a multi-objective optimization problem for container-based microservice scheduling. Our approach is based on the particle swarm optimization algorithm, combined parallel computing, and Pareto-optimal theory. The particle swarm optimization algorithm has fast convergence speed, fewer parameters, and many other advantages. First, we detail the various resources of the physical nodes, cluster, local load balancing, failure rate, and other aspects. Then, we discuss our improvement with respect to the relevant parameters. Second, we create a multi-objective optimization model and use a multi-objective optimization parallel particle swarm optimization algorithm for container-based microservice scheduling (MOPPSO-CMS). This algorithm is based on user needs and can effectively balance the performance of the cluster. After comparative experiments, we found that the algorithm can achieve good results, in terms of load balancing, network transmission overhead, and optimization speed.

中文翻译：

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基于容器的微服务调度的多目标并行粒子群优化算法

基于微服务架构并具有一组独立的、细粒度的模块化服务的应用程序是可取的，因为其管理成本低、部署简单、可移植性高。这种容器技术在云计算中得到了广泛的应用。基于容器的微服务调度已经应用了多种方法，但都存在网络传输开销高、负载均衡效果不佳、服务可靠性低等明显缺点。为了克服这些缺点，在本研究中，我们提出了一个基于容器的微服务调度的多目标优化问题。我们的方法基于粒子群优化算法、组合并行计算和帕累托​​最优理论。粒子群优化算法收敛速度快，参数少，以及许多其他优点。首先，我们详细介绍了物理节点、集群、本地负载均衡、故障率等方面的各种资源。然后，我们讨论我们在相关参数方面的改进。其次，我们创建了多目标优化模型，并使用多目标优化并行粒子群优化算法进行基于容器的微服务调度（MOPPSO-CMS）。该算法基于用户需求，可以有效平衡集群的性能。经过对比实验，我们发现该算法在负载均衡、网络传输开销、优化速度等方面都能取得较好的效果。我们就相关参数讨论我们的改进。其次，我们创建了多目标优化模型，并使用多目标优化并行粒子群优化算法进行基于容器的微服务调度（MOPPSO-CMS）。该算法基于用户需求，可以有效平衡集群的性能。经过对比实验，我们发现该算法在负载均衡、网络传输开销、优化速度等方面都能取得较好的效果。我们就相关参数讨论我们的改进。其次，我们创建了多目标优化模型，并使用多目标优化并行粒子群优化算法进行基于容器的微服务调度（MOPPSO-CMS）。该算法基于用户需求，可以有效平衡集群的性能。经过对比实验，我们发现该算法在负载均衡、网络传输开销、优化速度等方面都能取得较好的效果。该算法基于用户需求，可以有效平衡集群的性能。经过对比实验，我们发现该算法在负载均衡、网络传输开销、优化速度等方面都能取得较好的效果。该算法基于用户需求，可以有效平衡集群的性能。经过对比实验，我们发现该算法在负载均衡、网络传输开销、优化速度等方面都能取得较好的效果。