TRACE: A Targeted Recommender for VM Assignment in Cloud Environment
Multi-tenancy in modern cloud service colocates multiple virtual machines (VMs) into physical machines (PMs) to improve resource efficiency. However, co-location introduces interference among VMs, potentially degrading the quality-ofservice (QoS) for users. Previous methods predict QoS degradation a...
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| Vydané v: | Proceedings / IEEE International Conference on Cluster Computing s. 01 - 11 |
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| Hlavní autori: | , , , , |
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| Jazyk: | English |
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IEEE
02.09.2025
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| ISSN: | 2168-9253 |
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| Abstract | Multi-tenancy in modern cloud service colocates multiple virtual machines (VMs) into physical machines (PMs) to improve resource efficiency. However, co-location introduces interference among VMs, potentially degrading the quality-ofservice (QoS) for users. Previous methods predict QoS degradation and schedule VMs accordingly, but they often overlook important information provided by VM metrics and are hard to integrate with real-world cloud schedulers. Considering the above factors, we present TRACE, a novel QoS-aware, lightweight, and decoupled recommender for VM scheduling. Firstly, TRACE employs a dual-tower feature extraction mechanism that independently extracts metrics from VMs and PMs, thereby reducing the time complexity of the model. Secondly, the dual-tower is enhanced by Deep & Cross Networks to explicitly model cross-feature interactions, and we further incorporate a Set Transformer to process overlooked multi-VM metrics from the PM. Thirdly, TRACE designs a trainable similarity gate and an adaptive mask to filter suboptimal migrations, decoupling it from the scheduler for easy integration. Experimental results on data collected from real-world clusters show that TRACE outperforms state-of-the-art methods in QoS prediction accuracy and ranking quality, achieving at least 6.3 % QoS improvements. |
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| AbstractList | Multi-tenancy in modern cloud service colocates multiple virtual machines (VMs) into physical machines (PMs) to improve resource efficiency. However, co-location introduces interference among VMs, potentially degrading the quality-ofservice (QoS) for users. Previous methods predict QoS degradation and schedule VMs accordingly, but they often overlook important information provided by VM metrics and are hard to integrate with real-world cloud schedulers. Considering the above factors, we present TRACE, a novel QoS-aware, lightweight, and decoupled recommender for VM scheduling. Firstly, TRACE employs a dual-tower feature extraction mechanism that independently extracts metrics from VMs and PMs, thereby reducing the time complexity of the model. Secondly, the dual-tower is enhanced by Deep & Cross Networks to explicitly model cross-feature interactions, and we further incorporate a Set Transformer to process overlooked multi-VM metrics from the PM. Thirdly, TRACE designs a trainable similarity gate and an adaptive mask to filter suboptimal migrations, decoupling it from the scheduler for easy integration. Experimental results on data collected from real-world clusters show that TRACE outperforms state-of-the-art methods in QoS prediction accuracy and ranking quality, achieving at least 6.3 % QoS improvements. |
| Author | Cheng, Yunlong Chan, Tin Ping Chen, Guihai Gao, Xiaofeng Dong, Hongji |
| Author_xml | – sequence: 1 givenname: Hongji surname: Dong fullname: Dong, Hongji email: harlin671@sjtu.edu.cn organization: School of Computer Science, Shanghai Jiao Tong University,Shanghai Key Laboratory of Scalable Computing and Systems,Shanghai,China – sequence: 2 givenname: Yunlong surname: Cheng fullname: Cheng, Yunlong email: aweftr@sjtu.edu.cn organization: School of Computer Science, Shanghai Jiao Tong University,Shanghai Key Laboratory of Scalable Computing and Systems,Shanghai,China – sequence: 3 givenname: Tin Ping surname: Chan fullname: Chan, Tin Ping email: chantp@sjtu.edu.cn organization: School of Computer Science, Shanghai Jiao Tong University,Shanghai Key Laboratory of Scalable Computing and Systems,Shanghai,China – sequence: 4 givenname: Xiaofeng surname: Gao fullname: Gao, Xiaofeng email: gaoxiaofeng@sjtu.edu.cn organization: School of Computer Science, Shanghai Jiao Tong University,Shanghai Key Laboratory of Scalable Computing and Systems,Shanghai,China – sequence: 5 givenname: Guihai surname: Chen fullname: Chen, Guihai email: chen-gh@sjtu.edu.cn organization: School of Computer Science, Shanghai Jiao Tong University,Shanghai Key Laboratory of Scalable Computing and Systems,Shanghai,China |
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| SubjectTerms | Accuracy Adaptation models Cloud computing Cloud Service Feature extraction Logic gates Measurement QoS Quality of service Recommender Time complexity Transformers Virtual machines |
| Title | TRACE: A Targeted Recommender for VM Assignment in Cloud Environment |
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