Citation Export
DC Field | Value | Language |
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dc.contributor.author | Kim, Taehoon | - |
dc.contributor.author | Koh, Kwangwon | - |
dc.contributor.author | Kim, Changdae | - |
dc.contributor.author | Pak, Eunji | - |
dc.contributor.author | Jeong, Yeonjeong | - |
dc.contributor.author | Kim, Sang Hoon | - |
dc.date.issued | 2023-01-01 | - |
dc.identifier.issn | 1552-5244 | - |
dc.identifier.uri | https://aurora.ajou.ac.kr/handle/2018.oak/36940 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85179507468&origin=inward | - |
dc.description.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. | - |
dc.description.sponsorship | 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). | - |
dc.description.sponsorship | 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). | - |
dc.language.iso | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.subject.mesh | Address space | - |
dc.subject.mesh | Address space management | - |
dc.subject.mesh | Datacenter | - |
dc.subject.mesh | Dis-aggregated memory | - |
dc.subject.mesh | Hypervisors | - |
dc.subject.mesh | Memory systems | - |
dc.subject.mesh | Performance | - |
dc.subject.mesh | Prefetching | - |
dc.subject.mesh | Space management | - |
dc.subject.mesh | Virtualization technologies | - |
dc.title | DEHype: Retrofitting Hypervisors for a Resource-Disaggregated Environment | - |
dc.type | Conference | - |
dc.citation.conferenceDate | 2023.10.31. ~ 2023.11.3. | - |
dc.citation.conferenceName | 25th IEEE International Conference on Cluster Computing, CLUSTER 2023 | - |
dc.citation.edition | Proceedings - 2023 IEEE International Conference on Cluster Computing, CLUSTER 2023 | - |
dc.citation.endPage | 48 | - |
dc.citation.startPage | 37 | - |
dc.citation.title | Proceedings - IEEE International Conference on Cluster Computing, ICCC | - |
dc.identifier.bibliographicCitation | Proceedings - IEEE International Conference on Cluster Computing, ICCC, pp.37-48 | - |
dc.identifier.doi | 10.1109/cluster52292.2023.00011 | - |
dc.identifier.scopusid | 2-s2.0-85179507468 | - |
dc.subject.keyword | address space management | - |
dc.subject.keyword | disaggregated memory | - |
dc.subject.keyword | hypervisor | - |
dc.subject.keyword | prefetching | - |
dc.subject.keyword | virtual machine | - |
dc.type.other | Conference Paper | - |
dc.description.isoa | false | - |
dc.subject.subarea | Software | - |
dc.subject.subarea | Hardware and Architecture | - |
dc.subject.subarea | Signal Processing | - |
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