Optimizing Task Scheduling in Cloud VMs with Accurate vCPU Abstraction
Document Type
Conference Proceeding
Date of Original Version
3-30-2025
Abstract
The paper shows that task scheduling in Cloud VMs hasn’t evolved quickly to handle the dynamic vCPU resources. The existing vCPU abstraction cannot accurately depict the vCPU dynamics in capacity, activity, and topology, and these mismatches can mislead the scheduler, causing performance degradation and system anomalies. The paper proposes a novel solution, vSched, which probes accurate vCPU abstraction through a set of lightweight microbenchmarks (vProbers) without modifying the hypervisor, and leverages the probed information to optimize task scheduling in cloud VMs with three new techniques: biased vCPU selection, intra-VM harvesting, and relaxed work conservation. Our evaluation of vSched’s implementation in x86 Linux Kernel demonstrates that it can effectively improve both system throughput and workload latency across various VM types in the dynamic multi-cloud environment.
Publication Title, e.g., Journal
Eurosys 2025 Proceedings of the 2025 20th European Conference on Computer Systems
Citation/Publisher Attribution
Guo, Edward, Weiwei Jia, Xiaoning Ding, and Jianchen Shan. "Optimizing Task Scheduling in Cloud VMs with Accurate vCPU Abstraction." Eurosys 2025 Proceedings of the 2025 20th European Conference on Computer Systems (2025). doi: 10.1145/3689031.3696092.