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Relevant bibliographies by topics / Memory disaggregation

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Journal articles

Academic literature on the topic 'Memory disaggregation'

Author: Grafiati

Published: 27 January 2024

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Journal articles on the topic "Memory disaggregation"

1

Aguilera, Marcos K., Emmanuel Amaro, Nadav Amit, Erika Hunhoff, Anil Yelam, and Gerd Zellweger. "Memory disaggregation: why now and what are the challenges." ACM SIGOPS Operating Systems Review 57, no. 1 (2023): 38–46. http://dx.doi.org/10.1145/3606557.3606563.

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Hardware disaggregation has emerged as one of the most fundamental shifts in how we build computer systems over the past decades. While disaggregation has been successful for several types of resources (storage, power, and others), memory disaggregation has yet to happen. We make the case that the time for memory disaggregation has arrived. We look at past successful disaggregation stories and learn that their success depended on two requirements: addressing a burning issue and being technically feasible. We examine memory disaggregation through this lens and find that both requirements are fi

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2

Mehra, Pankaj, and Tom Coughlin. "Taming Memory With Disaggregation." Computer 55, no. 9 (2022): 94–98. http://dx.doi.org/10.1109/mc.2022.3187847.

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3

Wu, Chenyuan, Mohammad Javad Amiri, Jared Asch, Heena Nagda, Qizhen Zhang, and Boon Thau Loo. "FlexChain." Proceedings of the VLDB Endowment 16, no. 1 (2022): 23–36. http://dx.doi.org/10.14778/3561261.3561264.

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While permissioned blockchains enable a family of data center applications, existing systems suffer from imbalanced loads across compute and memory, exacerbating the underutilization of cloud resources. This paper presents FlexChain , a novel permissioned blockchain system that addresses this challenge by physically disaggregating CPUs, DRAM, and storage devices to process different blockchain workloads efficiently. Disaggregation allows blockchain service providers to upgrade and expand hardware resources independently to support a wide range of smart contracts with diverse CPU and memory dem

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4

Al Maruf, Hasan, and Mosharaf Chowdhury. "Memory Disaggregation: Advances and Open Challenges." ACM SIGOPS Operating Systems Review 57, no. 1 (2023): 29–37. http://dx.doi.org/10.1145/3606557.3606562.

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Compute and memory are tightly coupled within each server in traditional datacenters. Large-scale datacenter operators have identified this coupling as a root cause behind fleetwide resource underutilization and increasing Total Cost of Ownership (TCO). With the advent of ultra-fast networks and cache-coherent interfaces, memory disaggregation has emerged as a potential solution, whereby applications can leverage available memory even outside server boundaries. This paper summarizes the growing research landscape of memory disaggregation from a software perspective and introduces the challenge

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5

Nam, Jaeyoun, Hokeun Cha, ByeongKeon Lee, and Beomseok Nam. "Xpass: NUMA-aware Persistent Memory Disaggregation." Journal of KIISE 48, no. 7 (2021): 735–41. http://dx.doi.org/10.5626/jok.2021.48.7.735.

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6

Celov, Dmitrij, and Remigijus Leipus. "Time series aggregation, disaggregation and long memory." Lietuvos matematikos rinkinys 46 (September 21, 2023): 255–62. http://dx.doi.org/10.15388/lmr.2006.30723.

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Large-scale aggregation and its inverse, disaggregation, problems are important in many fields of studies like macroeconomics, astronomy, hydrology and sociology. It was shown in Granger (1980) that a certain aggregation of random coefficient AR(1) models can lead to long memory output. Dacunha-Castelle and Oppenheim (2001) explored the topic further, answering when and if a predefined long memory process could be obtained as the result of aggregation of a specific class of individual processes. In this paper, the disaggregation scheme of Leipus et al. (2006) is briefly discussed. Then disaggr

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7

Wang, Zhonghua, Yixing Guo, Kai Lu, et al. "Rcmp: Reconstructing RDMA-Based Memory Disaggregation via CXL." ACM Transactions on Architecture and Code Optimization 21, no. 1 (2024): 1–26. http://dx.doi.org/10.1145/3634916.

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Memory disaggregation is a promising architecture for modern datacenters that separates compute and memory resources into independent pools connected by ultra-fast networks, which can improve memory utilization, reduce cost, and enable elastic scaling of compute and memory resources. However, existing memory disaggregation solutions based on remote direct memory access (RDMA) suffer from high latency and additional overheads including page faults and code refactoring. Emerging cache-coherent interconnects such as CXL offer opportunities to reconstruct high-performance memory disaggregation. Ho

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8

Celov, D., R. Leipus, and A. Philippe. "Time series aggregation, disaggregation, and long memory." Lithuanian Mathematical Journal 47, no. 4 (2007): 379–93. http://dx.doi.org/10.1007/s10986-007-0026-6.

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9

Zhang, Yingqiang, Chaoyi Ruan, Cheng Li, et al. "Towards cost-effective and elastic cloud database deployment via memory disaggregation." Proceedings of the VLDB Endowment 14, no. 10 (2021): 1900–1912. http://dx.doi.org/10.14778/3467861.3467877.

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It is challenging for cloud-native relational databases to meet the ever-increasing needs of scaling compute and memory resources independently and elastically. The recent emergence of memory disaggregation architecture, relying on high-speed RDMA network, offers opportunities to build cost-effective and elastic cloud-native databases. There exist proposals to let unmodified applications run transparently on disaggregated systems. However, running relational database kernel atop such proposals experiences notable performance degradation and time-consuming failure recovery, offsetting the benef

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10

Wang, Ruihong, Jianguo Wang, Stratos Idreos, M. Tamer Özsu, and Walid G. Aref. "The case for distributed shared-memory databases with RDMA-enabled memory disaggregation." Proceedings of the VLDB Endowment 16, no. 1 (2022): 15–22. http://dx.doi.org/10.14778/3561261.3561263.

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Memory disaggregation (MD) allows for scalable and elastic data center design by separating compute (CPU) from memory. With MD, compute and memory are no longer coupled into the same server box. Instead, they are connected to each other via ultra-fast networking such as RDMA. MD can bring many advantages, e.g., higher memory utilization, better independent scaling (of compute and memory), and lower cost of ownership. This paper makes the case that MD can fuel the next wave of innovation on database systems. We observe that MD revives the great debate of "shared what" in the database community.

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