Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling
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 app...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/18/6212 |
| _version_ | 1797517207922540544 |
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| author | Xinying Chen Siyi Xiao |
| author_facet | Xinying Chen Siyi Xiao |
| author_sort | Xinying Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T07:13:32Z |
| format | Article |
| id | doaj.art-72ae276e348f44eb8caccb74538754d2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T07:13:32Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-72ae276e348f44eb8caccb74538754d22023-11-22T15:13:21ZengMDPI AGSensors1424-82202021-09-012118621210.3390/s21186212Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice SchedulingXinying Chen0Siyi Xiao1School of Software, Dalian Jiaotong University, Dalian 116000, ChinaSchool of Software, Dalian Jiaotong University, Dalian 116000, ChinaAn 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.https://www.mdpi.com/1424-8220/21/18/6212multi-objective optimizationcontainer-based microservice schedulingparticle swarm optimization algorithmcloud computing |
| spellingShingle | Xinying Chen Siyi Xiao Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling Sensors multi-objective optimization container-based microservice scheduling particle swarm optimization algorithm cloud computing |
| title | Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling |
| title_full | Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling |
| title_fullStr | Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling |
| title_full_unstemmed | Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling |
| title_short | Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling |
| title_sort | multi objective and parallel particle swarm optimization algorithm for container based microservice scheduling |
| topic | multi-objective optimization container-based microservice scheduling particle swarm optimization algorithm cloud computing |
| url | https://www.mdpi.com/1424-8220/21/18/6212 |
| work_keys_str_mv | AT xinyingchen multiobjectiveandparallelparticleswarmoptimizationalgorithmforcontainerbasedmicroservicescheduling AT siyixiao multiobjectiveandparallelparticleswarmoptimizationalgorithmforcontainerbasedmicroservicescheduling |