[2509.03394] CloudFormer: An Attention-based Performance Prediction for Public Clouds with Unknown Workload

Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2509.03394 (cs) [Submitted on 3 Sep 2025 (v1), last revised 25 Mar 2026 (this version, v2)] Title:CloudFormer: An Attention-based Performance Prediction for Public Clouds with Unknown Workload Authors:Amirhossein Shahbazinia, Darong Huang, Luis Costero, David Atienza View a PDF of the paper titled CloudFormer: An Attention-based Performance Prediction for Public Clouds with Unknown Workload, by Amirhossein Shahbazinia and 3 other authors View PDF HTML (experimental) Abstract:Cloud platforms are increasingly relied upon to host diverse, resource-intensive workloads due to their scalability, flexibility, and cost-efficiency. In multi-tenant cloud environments, virtual machines are consolidated on shared physical servers to improve resource utilization. While virtualization guarantees resource partitioning for CPU, memory, and storage, it cannot ensure performance isolation. Competition for shared resources such as last-level cache, memory bandwidth, and network interfaces often leads to severe performance degradation. Existing management techniques, including VM scheduling and resource provisioning, require accurate performance prediction to mitigate interference. However, this remains challenging in public clouds due to the black-box nature of VMs and the highly dynamic nature of workloads. To address these limitations, we propose CloudFormer, a dual-branch Transformer-based model designed to predict VM p...