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학술지 Disaggregated Cloud Memory with Elastic Block Management
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저자
고광원, 김강호, 전승협, 허재혁
발행일
201901
출처
IEEE Transactions on Computers, v.68 no.1, pp.39-52
ISSN
0018-9340
출판사
IEEE
DOI
https://dx.doi.org/10.1109/TC.2018.2851565
협약과제
18ZS1200, IDX 플랫폼 원천기술 연구, 김영균
초록
With the growing importance of in-memory data processing, cloud service providers have launched large memory virtual machine services to accommodate memory intensive workloads. Such large memory services using low volume scaled-up machines are far less cost-efficient than scaled-out services consisting of high volume commodity servers. By exploiting memory usage imbalance across cloud nodes, disaggregated memory can scale up the memory capacity for a virtual machine in a cost-effective way. Disaggregated memory allows available memory in remote nodes to be used for the virtual machine requiring more memory than its locally available memory. It supports high performance with the faster direct memory while satisfying the memory capacity demand with the slower remote memory. This paper proposes a new hypervisor-integrated disaggregated memory system for cloud computing. The hypervisor-integrated design has several new contributions in its disaggregated memory design and implementation. First, with the tight hypervisor integration, it investigates a new page management mechanism and policy tuned for disaggregated memory in virtualized systems. Second, it restructures the memory management procedures and relieves the scalability concern for supporting large virtual machines. Third, exploiting page access records available to the hypervisor, it supports application-aware elastic block sizes for fetching remote memory pages with different granularities. Depending on the degrees of spatial locality for different regions of memory in a virtual machine, the optimal block size for each memory region is dynamically selected. The experimental results with the implementation integrated to the KVM hypervisor, show that the disaggregated memory can provide on average 6 percent performance degradation compared to the ideal local-memory only machine, even though the direct memory capacity is only 50 percent of the total memory footprint.
KSP 제안 키워드
Application-Aware, Block size, Capacity demand, Cloud Computing, Cloud Nodes, Cloud Service Providers(CSP), Cost-efficient, Data processing, Different regions, High performance, High volume