TRACE: A Targeted Recommender for VM Assignment in Cloud Environment

Hongji Dong, Yunlong Cheng, Tin Ping Chan, Xiaofeng Gao, Guihai Chen

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.

External IDs:dblp:conf/cluster/DongCCGC25