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DEHype: Retrofitting Hypervisors for a Resource-Disaggregated Environment
  • Kim, Taehoon ;
  • Koh, Kwangwon ;
  • Kim, Changdae ;
  • Pak, Eunji ;
  • Jeong, Yeonjeong ;
  • Kim, Sang Hoon
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Publication Year
2023-01-01
Journal
Proceedings - IEEE International Conference on Cluster Computing, ICCC
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings - IEEE International Conference on Cluster Computing, ICCC, pp.37-48
Keyword
address space managementdisaggregated memoryhypervisorprefetchingvirtual machine
Mesh Keyword
Address spaceAddress space managementDatacenterDis-aggregated memoryHypervisorsMemory systemsPerformancePrefetchingSpace managementVirtualization technologies
All Science Classification Codes (ASJC)
SoftwareHardware and ArchitectureSignal Processing
Abstract
Resource disaggregation has been proposed as a solution for resource under-utilization in data centers. However, host virtualization technologies, which are the basic building blocks for constructing data centers, are implemented without considering the disaggregated resources. In addition, we discover that a RDMA I/O unit plays a significant role in the performance of a disaggregated resource environment. In this study, we propose DEHype, which alleviates the inefficiency of the hypervisors utilized in a disaggregated environment by investigating host virtualization technologies that are suitable for disaggregated memory systems. Specifically, DEHype aims to identify and improve the performance issues associated with virtual machines through KVM/QEMU in a disaggregated resource environment. The results demonstrate the effectiveness of the proposed optimizations in improving the performance of disaggregated memory systems. DEHype achieves up to a 351% improvement over the state-of-the-art disaggregated memory system.
ISSN
1552-5244
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36940
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85179507468&origin=inward
DOI
https://doi.org/10.1109/cluster52292.2023.00011
Type
Conference
Funding
We thank the anonymous reviewers for their insightful reviews and comments. This work was partly supported by a grant from the Institute of Information & Communications Technology Planning & Evaluation (IITP) funded by the Korean government (MSIT) (No. 2018-0-00503, Researches on next-generation memory-centric computing system architecture, and RS-2023-00255968, Artificial Intelligence Con- vergence Innovation Human Resources Development), and by a grant from the Electronics and Telecommunications Research Institute (ETRI) funded by the Korean government (23ZS1300).We thank the anonymous reviewers for their insightful reviews and comments. This work was partly supported by a grant from the Institute of Information & Communications Technology Planning & Evaluation (IITP) funded by the Korean government (MSIT) (No. 2018-0-00503, Researches on next-generation memory-centric computing system architecture, and RS-2023-00255968, Artificial Intelligence Convergence Innovation Human Resources Development), and by a grant from the Electronics and Telecommunications Research Institute (ETRI) funded by the Korean government (23ZS1300).
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