A multi-task genetic programming approach for online multi-objective container placement in heterogeneous cluster
Abstract Owing to the potential for fast deployment, containerization technology has been widely used in web applications based on microservice architecture. Online container placement aims to improve resource utilization and meet other service quality requirements of cloud data centers. Most curren...
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| Format: | Article |
| Language: | English |
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Springer
2024-11-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01605-x |
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| _version_ | 1832571164104327168 |
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| author | Ruochen Liu Haoyuan Lv Ping Yang Rongfang Wang |
| author_facet | Ruochen Liu Haoyuan Lv Ping Yang Rongfang Wang |
| author_sort | Ruochen Liu |
| collection | DOAJ |
| description | Abstract Owing to the potential for fast deployment, containerization technology has been widely used in web applications based on microservice architecture. Online container placement aims to improve resource utilization and meet other service quality requirements of cloud data centers. Most current heuristic and hyper-heuristic methods for container placement rely on single allocation rules, which are inefficient in heterogeneous cluster scenarios. Moreover, some container placement tasks often have similar characteristics (e.g., resource request types and physical machine types), but traditional single-task optimization modeling cannot exploit potential common knowledge, resulting in repeated optimization during resource allocation. Therefore, a new multi-task genetic programming method is proposed to solve the online multi-objective container placement problem (MOCP-MTGP). This method considers selecting appropriate allocation rules according to the types of resource requests and cluster status. MOCP-MTGP can automatically generate multiple groups of allocation rules from historical workload patterns and different cluster states, and capture the similarities between all online tasks to guide the transfer of general knowledge during optimization. Comprehensive experiments show that the proposed algorithm can improve the resource utilization of clusters, reduce the number of physical machines, and effectively meet resource constraints and high availability requirements. |
| format | Article |
| id | doaj-art-4f46560138744e8898b95103976c7808 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-4f46560138744e8898b95103976c78082025-02-02T12:49:05ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-11-0111112010.1007/s40747-024-01605-xA multi-task genetic programming approach for online multi-objective container placement in heterogeneous clusterRuochen Liu0Haoyuan Lv1Ping Yang2Rongfang Wang3Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian UniversityKey Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian UniversityKey Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian UniversityKey Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian UniversityAbstract Owing to the potential for fast deployment, containerization technology has been widely used in web applications based on microservice architecture. Online container placement aims to improve resource utilization and meet other service quality requirements of cloud data centers. Most current heuristic and hyper-heuristic methods for container placement rely on single allocation rules, which are inefficient in heterogeneous cluster scenarios. Moreover, some container placement tasks often have similar characteristics (e.g., resource request types and physical machine types), but traditional single-task optimization modeling cannot exploit potential common knowledge, resulting in repeated optimization during resource allocation. Therefore, a new multi-task genetic programming method is proposed to solve the online multi-objective container placement problem (MOCP-MTGP). This method considers selecting appropriate allocation rules according to the types of resource requests and cluster status. MOCP-MTGP can automatically generate multiple groups of allocation rules from historical workload patterns and different cluster states, and capture the similarities between all online tasks to guide the transfer of general knowledge during optimization. Comprehensive experiments show that the proposed algorithm can improve the resource utilization of clusters, reduce the number of physical machines, and effectively meet resource constraints and high availability requirements.https://doi.org/10.1007/s40747-024-01605-xMicroservice architectureContainer placementResource utilizationEvolutionary multitasking optimization |
| spellingShingle | Ruochen Liu Haoyuan Lv Ping Yang Rongfang Wang A multi-task genetic programming approach for online multi-objective container placement in heterogeneous cluster Complex & Intelligent Systems Microservice architecture Container placement Resource utilization Evolutionary multitasking optimization |
| title | A multi-task genetic programming approach for online multi-objective container placement in heterogeneous cluster |
| title_full | A multi-task genetic programming approach for online multi-objective container placement in heterogeneous cluster |
| title_fullStr | A multi-task genetic programming approach for online multi-objective container placement in heterogeneous cluster |
| title_full_unstemmed | A multi-task genetic programming approach for online multi-objective container placement in heterogeneous cluster |
| title_short | A multi-task genetic programming approach for online multi-objective container placement in heterogeneous cluster |
| title_sort | multi task genetic programming approach for online multi objective container placement in heterogeneous cluster |
| topic | Microservice architecture Container placement Resource utilization Evolutionary multitasking optimization |
| url | https://doi.org/10.1007/s40747-024-01605-x |
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