Ant Colony Algorithm for Multi-Objective Optimization of Container-Based Microservice Scheduling in Cloud
In cloud architectures, the microservice model divides an application into a set of loosely coupled and collaborative fine-grained services. As a lightweight virtualization technology, the container supports the encapsulation and deployment of microservice applications. Despite a large number of sol...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8744199/ |
| _version_ | 1818877558883614720 |
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| author | Miao Lin Jianqing Xi Weihua Bai Jiayin Wu |
| author_facet | Miao Lin Jianqing Xi Weihua Bai Jiayin Wu |
| author_sort | Miao Lin |
| collection | DOAJ |
| description | In cloud architectures, the microservice model divides an application into a set of loosely coupled and collaborative fine-grained services. As a lightweight virtualization technology, the container supports the encapsulation and deployment of microservice applications. Despite a large number of solutions and implementations, there remain open issues that have not been completely addressed in the deployment and management of the microservice containers. An effective method for container resource scheduling not only satisfies the service requirements of users but also reduces the running overhead and ensures the performance of the cluster. In this paper, a multi-objective optimization model for the container-based microservice scheduling is established, and an ant colony algorithm is proposed to solve the scheduling problem. Our algorithm considers not only the utilization of computing and storage resources of the physical nodes but also the number of microservice requests and the failure rate of the physical nodes. Our algorithm uses the quality evaluation function of the feasible solutions to ensure the validity of pheromone updating and combines multi-objective heuristic information to improve the selection probability of the optimal path. By comparing with other related algorithms, the experimental results show that the proposed optimization algorithm achieves better results in the optimization of cluster service reliability, cluster load balancing, and network transmission overhead. |
| first_indexed | 2024-12-19T14:00:12Z |
| format | Article |
| id | doaj.art-8315539e2d4240ca9f128f7d9b19c496 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T14:00:12Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8315539e2d4240ca9f128f7d9b19c4962022-12-21T20:18:28ZengIEEEIEEE Access2169-35362019-01-017830888310010.1109/ACCESS.2019.29244148744199Ant Colony Algorithm for Multi-Objective Optimization of Container-Based Microservice Scheduling in CloudMiao Lin0https://orcid.org/0000-0002-5198-1780Jianqing Xi1Weihua Bai2https://orcid.org/0000-0001-8333-7415Jiayin Wu3School of Software Engineering, South China University of Technology, Guangzhou, ChinaSchool of Software Engineering, South China University of Technology, Guangzhou, ChinaSchool of Computer Science, Zhaoqing University, Zhaoqing, ChinaSchool of Computer, Guangdong Vocational College of Post and Telecom, Guangzhou, ChinaIn cloud architectures, the microservice model divides an application into a set of loosely coupled and collaborative fine-grained services. As a lightweight virtualization technology, the container supports the encapsulation and deployment of microservice applications. Despite a large number of solutions and implementations, there remain open issues that have not been completely addressed in the deployment and management of the microservice containers. An effective method for container resource scheduling not only satisfies the service requirements of users but also reduces the running overhead and ensures the performance of the cluster. In this paper, a multi-objective optimization model for the container-based microservice scheduling is established, and an ant colony algorithm is proposed to solve the scheduling problem. Our algorithm considers not only the utilization of computing and storage resources of the physical nodes but also the number of microservice requests and the failure rate of the physical nodes. Our algorithm uses the quality evaluation function of the feasible solutions to ensure the validity of pheromone updating and combines multi-objective heuristic information to improve the selection probability of the optimal path. By comparing with other related algorithms, the experimental results show that the proposed optimization algorithm achieves better results in the optimization of cluster service reliability, cluster load balancing, and network transmission overhead.https://ieeexplore.ieee.org/document/8744199/Ant colony algorithmcloud computingcontainer schedulingmicroservicesmulti-objective optimization |
| spellingShingle | Miao Lin Jianqing Xi Weihua Bai Jiayin Wu Ant Colony Algorithm for Multi-Objective Optimization of Container-Based Microservice Scheduling in Cloud IEEE Access Ant colony algorithm cloud computing container scheduling microservices multi-objective optimization |
| title | Ant Colony Algorithm for Multi-Objective Optimization of Container-Based Microservice Scheduling in Cloud |
| title_full | Ant Colony Algorithm for Multi-Objective Optimization of Container-Based Microservice Scheduling in Cloud |
| title_fullStr | Ant Colony Algorithm for Multi-Objective Optimization of Container-Based Microservice Scheduling in Cloud |
| title_full_unstemmed | Ant Colony Algorithm for Multi-Objective Optimization of Container-Based Microservice Scheduling in Cloud |
| title_short | Ant Colony Algorithm for Multi-Objective Optimization of Container-Based Microservice Scheduling in Cloud |
| title_sort | ant colony algorithm for multi objective optimization of container based microservice scheduling in cloud |
| topic | Ant colony algorithm cloud computing container scheduling microservices multi-objective optimization |
| url | https://ieeexplore.ieee.org/document/8744199/ |
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