DDRM: An SLO-aware Deep Dynamic Resource Management Framework for Microservices
Loosely coupled microservice architectures have been widely adopted in cloud-native applications due to their inherent advantages in modularity, development agility, and scalability. However, the resulting complex and dynamic service topologies introduce intricate inter-service dependencies, which o...
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| Vydané v: | Proceedings / IEEE International Conference on Cluster Computing s. 1 - 12 |
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IEEE
02.09.2025
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| Abstract | Loosely coupled microservice architectures have been widely adopted in cloud-native applications due to their inherent advantages in modularity, development agility, and scalability. However, the resulting complex and dynamic service topologies introduce intricate inter-service dependencies, which often lead to backpressure effects and queuing delays. These phenomena significantly challenge traditional monolithic and rule-based resource management approaches, which struggle to capture the non-linear performance characteristics and long-term effects of resource allocation decisions in such environments. To address these challenges, we propose DDRM, a two-stage predictor-decider collaborative framework for dynamic resource management in microservice systems. DDRM integrates deep learning to model inter-service interactions and predict the probability of Service Level Objective (SLO) violations, and employs reinforcement learning to optimize resource allocation decisions by maximizing long-term cumulative rewards while meeting SLO targets. Extensive evaluations demonstrate that DDRM outperforms state-of-the-art baselines by up to 29.8 %, while exhibiting strong stability and adaptability under highly varying workloads. |
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| AbstractList | Loosely coupled microservice architectures have been widely adopted in cloud-native applications due to their inherent advantages in modularity, development agility, and scalability. However, the resulting complex and dynamic service topologies introduce intricate inter-service dependencies, which often lead to backpressure effects and queuing delays. These phenomena significantly challenge traditional monolithic and rule-based resource management approaches, which struggle to capture the non-linear performance characteristics and long-term effects of resource allocation decisions in such environments. To address these challenges, we propose DDRM, a two-stage predictor-decider collaborative framework for dynamic resource management in microservice systems. DDRM integrates deep learning to model inter-service interactions and predict the probability of Service Level Objective (SLO) violations, and employs reinforcement learning to optimize resource allocation decisions by maximizing long-term cumulative rewards while meeting SLO targets. Extensive evaluations demonstrate that DDRM outperforms state-of-the-art baselines by up to 29.8 %, while exhibiting strong stability and adaptability under highly varying workloads. |
| Author | Li, Zhijun Wang, Jin Wang, Wanyou Tang, Liangping Shi, Gaotao |
| Author_xml | – sequence: 1 givenname: Liangping surname: Tang fullname: Tang, Liangping email: lptang@stu.suda.edu.cn organization: School of Future Science and Engineering,NEIC Laboratory, Soochow University,Soochow,China – sequence: 2 givenname: Jin surname: Wang fullname: Wang, Jin email: wjin1985@suda.edu.cn organization: School of Future Science and Engineering,NEIC Laboratory, Soochow University,Soochow,China – sequence: 3 givenname: Wanyou surname: Wang fullname: Wang, Wanyou email: wangwanyou@stu.hit.edu.cn organization: Harbin Institute of Technology,Faculty of Computing,Harbin,China – sequence: 4 givenname: Gaotao surname: Shi fullname: Shi, Gaotao email: shgt@tju.edu.cn organization: College of Intelligence and Computing, Tianjin University,Tianjin,China – sequence: 5 givenname: Zhijun surname: Li fullname: Li, Zhijun email: lizhijun_os@hit.edu.cn organization: Harbin Institute of Technology,Faculty of Computing,Harbin,China |
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| Snippet | Loosely coupled microservice architectures have been widely adopted in cloud-native applications due to their inherent advantages in modularity, development... |
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| SubjectTerms | Adaptation models Cloud computing Collaboration Deep learning deep learning for systems Dynamic scheduling Microservice architectures microservices resource efficiency resource man-agement Resource management Scalability Stability analysis Thermal stability |
| Title | DDRM: An SLO-aware Deep Dynamic Resource Management Framework for Microservices |
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