VSMT-IO: Improving I/O performance and efficiency on SMT processors in virtualized clouds

Weiwei Jia, Jianchen Shan, Tsz On Li, Xiaowei Shang, Heming Cui, Xiaoning Ding

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The paper focuses on an under-studied yet fundamental issue on Simultaneous Multi-Threading (SMT) processors - how to schedule I/O workloads, so as to improve I/O performance and efficiency. The paper shows that existing techniques used by CPU schedulers to improve I/O performance are inefficient on SMT processors, because they incur excessive context switches and spinning when workloads are waiting for I/O events. Such inefficiency makes it difficult to achieve high CPU throughput and high I/O throughput, which are required by typical workloads in clouds with both intensive I/O operations and heavy computation. The paper proposes to use context retention as a key technique to improve I/O performance and efficiency on SMT processors. Context retention uses a hardware thread to hold the context of an I/O workload waiting for I/O events, such that overhead of context switches and spinning can be eliminated, and the workload can quickly respond to I/O events. Targeting virtualized clouds and x86 systems, the paper identifies the technical issues in implementing context retention in real systems, and explores effective techniques to address these issues, including long term context retention and retention-aware symbiotic scheduling. The paper designs VSMT-IO to implement the idea and the techniques. Extensive evaluation based on the prototype implementation in KVM and diverse real-world applications, such as DBMS, web servers, AI workload, and Hadoop jobs, shows that VSMT-IO can improve I/O throughput by up to 88.3% and CPU throughput by up to 123.1%.

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 USENIX Annual Technical Conference, ATC 2020
PublisherUSENIX Association
Pages449-463
Number of pages15
ISBN (Electronic)9781939133144
StatePublished - 2020
Event2020 USENIX Annual Technical Conference, ATC 2020 - Virtual, Online
Duration: Jul 15 2020Jul 17 2020

Publication series

NameProceedings of the 2020 USENIX Annual Technical Conference, ATC 2020

Conference

Conference2020 USENIX Annual Technical Conference, ATC 2020
CityVirtual, Online
Period7/15/207/17/20

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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