TY - GEN
T1 - Optimizing Task Scheduling in Cloud VMs with Accurate vCPU Abstraction
AU - Guo, Edward
AU - Jia, Weiwei
AU - Ding, Xiaoning
AU - Shan, Jianchen
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/3/30
Y1 - 2025/3/30
N2 - 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.
AB - 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.
KW - Cloud Computing
KW - Operating Systems
KW - Resource Probing
KW - Task Scheduling
KW - Virtualization
UR - http://www.scopus.com/inward/record.url?scp=105002228723&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=105002228723&partnerID=8YFLogxK
U2 - 10.1145/3689031.3696092
DO - 10.1145/3689031.3696092
M3 - Conference contribution
AN - SCOPUS:105002228723
T3 - EuroSys 2025 - Proceedings of the 2025 20th European Conference on Computer Systems
SP - 753
EP - 768
BT - EuroSys 2025 - Proceedings of the 2025 20th European Conference on Computer Systems
PB - Association for Computing Machinery, Inc
T2 - 20th European Conference on Computer Systems, EuroSys 2025, co-located 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2025
Y2 - 30 March 2025 through 3 April 2025
ER -