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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Vydáno v:Proceedings / IEEE International Conference on Cluster Computing s. 01 - 11
Hlavní autoři: Dong, Hongji, Cheng, Yunlong, Chan, Tin Ping, Gao, Xiaofeng, Chen, Guihai
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 02.09.2025
Témata:
Accuracy
Adaptation models
Cloud computing
Cloud Service
Feature extraction
Logic gates
Measurement
QoS
Quality of service
Recommender
Time complexity
Transformers
Virtual machines
ISSN:2168-9253
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Shrnutí: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.
ISSN:2168-9253
DOI:10.1109/CLUSTER59342.2025.11186461