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Статті в журналах з теми "Memory disaggregation"

Автор: Grafiati

Опубліковано: 27 січня 2024

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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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11

Wang, Qing, Youyou Lu, and Jiwu Shu. "Building Write-Optimized Tree Indexes on Disaggregated Memory." ACM SIGMOD Record 52, no. 1 (2023): 45–52. http://dx.doi.org/10.1145/3604437.3604448.

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Memory disaggregation architecture physically separates CPU and memory into independent components, which are connected via high-speed RDMA networks, greatly improving resource utilization of database systems. However, such an architecture poses unique challenges to data indexing due to limited RDMA semantics and near-zero computation power at memory side. Existing indexes supporting disaggregated memory either suffer from low write performance, or require hardware modification.

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12

Lee, Myeung-Hun, and Hyeun-Jun Moon. "Nonintrusive Load Monitoring Using Recurrent Neural Networks with Occupants Location Information in Residential Buildings." Energies 16, no. 9 (2023): 3688. http://dx.doi.org/10.3390/en16093688.

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Nonintrusive load monitoring (NILM) is a process that disaggregates individual energy consumption based on the total energy consumption. In this study, an energy disaggregation model was developed and verified using an algorithm based on a recurrent neural network (RNN). It also aimed to evaluate the utility of the occupant location information, which is nonelectrical information. This study developed energy disaggregation models with RNN-based long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the suggested models was evaluated with a conventional method that use

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13

Dacunha-Castelle, Didier, and Lisandro Fermin. "Disaggregation of Long Memory Processes on $\mathcal{C}^{\infty}$ Class." Electronic Communications in Probability 11 (2006): 35–44. http://dx.doi.org/10.1214/ecp.v11-1133.

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14

Michelogiannakis, George, Benjamin Klenk, Brandon Cook, et al. "A Case For Intra-rack Resource Disaggregation in HPC." ACM Transactions on Architecture and Code Optimization 19, no. 2 (2022): 1–26. http://dx.doi.org/10.1145/3514245.

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The expected halt of traditional technology scaling is motivating increased heterogeneity in high-performance computing (HPC) systems with the emergence of numerous specialized accelerators. As heterogeneity increases, so does the risk of underutilizing expensive hardware resources if we preserve today’s rigid node configuration and reservation strategies. This has sparked interest in resource disaggregation to enable finer-grain allocation of hardware resources to applications. However, there is currently no data-driven study of what range of disaggregation is appropriate in HPC. To that end,

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15

Zou, Mingzhe, Shuyang Zhu, Jiacheng Gu, Lidija M. Korunovic, and Sasa Z. Djokic. "Heating and Lighting Load Disaggregation Using Frequency Components and Convolutional Bidirectional Long Short-Term Memory Method." Energies 14, no. 16 (2021): 4831. http://dx.doi.org/10.3390/en14164831.

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Load disaggregation for the identification of specific load types in the total demands (e.g., demand-manageable loads, such as heating or cooling loads) is becoming increasingly important for the operation of existing and future power supply systems. This paper introduces an approach in which periodical changes in the total demands (e.g., daily, weekly, and seasonal variations) are disaggregated into corresponding frequency components and correlated with the same frequency components in the meteorological variables (e.g., temperature and solar irradiance), allowing to select combinations of fr

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16

ÇAVDAR, İsmail, and Vahid FARYAD. "New Design of a Supervised Energy Disaggregation Model Based on the Deep Neural Network for a Smart Grid." Energies 12, no. 7 (2019): 1217. http://dx.doi.org/10.3390/en12071217.

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Energy management technology of demand-side is a key process of the smart grid that helps achieve a more efficient use of generation assets by reducing the energy demand of users during peak loads. In the context of a smart grid and smart metering, this paper proposes a hybrid model of energy disaggregation through deep feature learning for non-intrusive load monitoring to classify home appliances based on the information of main meters. In addition, a deep neural model of supervised energy disaggregation with a high accuracy for giving awareness to end users and generating detailed feedback f

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17

Mishra, Vaibhawa, Joshua L. Benjamin, and Georgios Zervas. "MONet: heterogeneous Memory over Optical Network for large-scale data center resource disaggregation." Journal of Optical Communications and Networking 13, no. 5 (2021): 126. http://dx.doi.org/10.1364/jocn.419145.

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18

Xia, Min, Wan’an Liu, Ke Wang, Wenzhu Song, Chunling Chen, and Yaping Li. "Non-intrusive load disaggregation based on composite deep long short-term memory network." Expert Systems with Applications 160 (December 2020): 113669. http://dx.doi.org/10.1016/j.eswa.2020.113669.

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19

Kianpoor, Nasrin, Bjarte Hoff, and Trond Østrem. "Deep Adaptive Ensemble Filter for Non-Intrusive Residential Load Monitoring." Sensors 23, no. 4 (2023): 1992. http://dx.doi.org/10.3390/s23041992.

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Identifying flexible loads, such as a heat pump, has an essential role in a home energy management system. In this study, an adaptive ensemble filtering framework integrated with long short-term memory (LSTM) is proposed for identifying flexible loads. The proposed framework, called AEFLSTM, takes advantage of filtering techniques and the representational power of LSTM for load disaggregation by filtering noise from the total power and learning the long-term dependencies of flexible loads. Furthermore, the proposed framework is adaptive and searches ensemble filtering techniques, including dis

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20

Maruf, Hasan Al, Yuhong Zhong, Hongyi Wang, Mosharaf Chowdhury, Asaf Cidon, and Carl Waldspurger. "Memtrade: Marketplace for Disaggregated Memory Clouds." ACM SIGMETRICS Performance Evaluation Review 51, no. 1 (2023): 1–2. http://dx.doi.org/10.1145/3606376.3593553.

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We present Memtrade, the first practical marketplace for disaggregated memory clouds. Clouds introduce a set of unique challenges for resource disaggregation across different tenants, including resource harvesting, isolation, and matching. Memtrade allows producer virtual machines (VMs) to lease both their unallocated memory and allocated-but-idle application memory to remote consumer VMs for a limited period of time. Memtrade does not require any modifications to host-level system software or support from the cloud provider. It harvests producer memory using an application-aware control loop

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21

Maruf, Hasan Al, Yuhong Zhong, Hongyi Wang, Mosharaf Chowdhury, Asaf Cidon, and Carl Waldspurger. "Memtrade: Marketplace for Disaggregated Memory Clouds." Proceedings of the ACM on Measurement and Analysis of Computing Systems 7, no. 2 (2023): 1–27. http://dx.doi.org/10.1145/3589985.

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We present Memtrade, the first practical marketplace for disaggregated memory clouds. Clouds introduce a set of unique challenges for resource disaggregation across different tenants, including resource harvesting, isolation, and matching. Memtrade allows producer virtual machines (VMs) to lease both their unallocated memory and allocated-but-idle application memory to remote consumer VMs for a limited period of time. Memtrade does not require any modifications to host-level system software or support from the cloud provider. It harvests producer memory using an application-aware control loop

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22

Rafiq, Hasan, Xiaohan Shi, Hengxu Zhang, Huimin Li, and Manesh Kumar Ochani. "A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing." Energies 13, no. 9 (2020): 2195. http://dx.doi.org/10.3390/en13092195.

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Non-intrusive load monitoring (NILM) is a process of estimating operational states and power consumption of individual appliances, which if implemented in real-time, can provide actionable feedback in terms of energy usage and personalized recommendations to consumers. Intelligent disaggregation algorithms such as deep neural networks can fulfill this objective if they possess high estimation accuracy and lowest generalization error. In order to achieve these two goals, this paper presents a disaggregation algorithm based on a deep recurrent neural network using multi-feature input space and p

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23

Quek, Yang Thee, Wai Lok Woo, and Thillainathan Logenthiran. "Load Disaggregation Using One-Directional Convolutional Stacked Long Short-Term Memory Recurrent Neural Network." IEEE Systems Journal 14, no. 1 (2020): 1395–404. http://dx.doi.org/10.1109/jsyst.2019.2919668.

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24

Wheater, H. S., T. J. Jolley, C. Onof, N. Mackay, and R. E. Chandler. "Analysis of aggregation and disaggregation effects for grid-based hydrological models and the development of improved precipitation disaggregation procedures for GCMs." Hydrology and Earth System Sciences 3, no. 1 (1999): 95–108. http://dx.doi.org/10.5194/hess-3-95-1999.

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Abstract. Appropriate representation of hydrological processes within atmospheric General Circulation Models (GCMs) is important with respect to internal model dynamics (e.g. surface feedback effects on atmospheric fluxes, continental runoff production) and to simulation of terrestrial impacts of climate change. However, at the scale of a GCM grid-square, several methodological problems arise. Spatial disaggregation of grid-square average climatological parameters is required in particular to produce appropriate point intensities from average precipitation. Conversely, aggregation of land surf

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25

Koasidis, Konstantinos, Vangelis Marinakis, Haris Doukas, Nikolaos Doumouras, Anastasios Karamaneas, and Alexandros Nikas. "Equipment- and Time-Constrained Data Acquisition Protocol for Non-Intrusive Appliance Load Monitoring." Energies 16, no. 21 (2023): 7315. http://dx.doi.org/10.3390/en16217315.

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Energy behaviours will play a key role in decarbonising the building sector but require the provision of tailored insights to assist occupants to reduce their energy use. Energy disaggregation has been proposed to provide such information on the appliance level without needing a smart meter plugged in to each load. However, the use of public datasets with pre-collected data employed for energy disaggregation is associated with limitations regarding its compatibility with random households, while gathering data on the ground still requires extensive, and hitherto under-deployed, equipment and t

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26

Markussen, Jonas, Lars Bjørlykke Kristiansen, Pål Halvorsen, Halvor Kielland-Gyrud, Håkon Kvale Stensland, and Carsten Griwodz. "SmartIO." ACM Transactions on Computer Systems 38, no. 1-2 (2021): 1–78. http://dx.doi.org/10.1145/3462545.

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The large variety of compute-heavy and data-driven applications accelerate the need for a distributed I/O solution that enables cost-effective scaling of resources between networked hosts. For example, in a cluster system, different machines may have various devices available at different times, but moving workloads to remote units over the network is often costly and introduces large overheads compared to accessing local resources. To facilitate I/O disaggregation and device sharing among hosts connected using Peripheral Component Interconnect Express (PCIe) non-transparent bridges, we presen

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27

Fang, Yifan, Shanshan Jiang, Shengxuan Fang, Zhenxi Gong, Min Xia, and Xiaodong Zhang. "Non-Intrusive Load Disaggregation Based on a Feature Reused Long Short-Term Memory Multiple Output Network." Buildings 12, no. 7 (2022): 1048. http://dx.doi.org/10.3390/buildings12071048.

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Load decomposition technology is an important aspect of power intelligence. At present, there are mainly machine learning methods based on artificial features and deep learning methods for load decomposition. The method based on artificial features has a difficult time obtaining effective load features, leading to low accuracy. The method based on deep learning can automatically extract load characteristics, which improves the accuracy of load decomposition. However, with the deepening of the model structure, the number of parameters becomes too large, the training speed is slow, and the compu

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28

Dinamarca, M. C., W. Cerpa, J. Garrido, J. L. Hancke та N. C. Inestrosa. "Hyperforin prevents β-amyloid neurotoxicity and spatial memory impairments by disaggregation of Alzheimer's amyloid-β-deposits". Molecular Psychiatry 11, № 11 (2006): 1032–48. http://dx.doi.org/10.1038/sj.mp.4001866.

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29

Vella, Jennifer L., Aleksey Molodtsov, Christina V. Angeles, Bruce R. Branchini, Mary Jo Turk, and Yina H. Huang. "Dendritic cells maintain anti-tumor immunity by positioning CD8 skin-resident memory T cells." Life Science Alliance 4, no. 10 (2021): e202101056. http://dx.doi.org/10.26508/lsa.202101056.

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Tissue-resident memory (TRM) T cells are emerging as critical components of the immune response to cancer; yet, requirements for their ongoing function and maintenance remain unclear. APCs promote TRM cell differentiation and re-activation but have not been implicated in sustaining TRM cell responses. Here, we identified a novel role for dendritic cells in supporting TRM to melanoma. We showed that CD8 TRM cells remain in close proximity to dendritic cells in the skin. Depletion of CD11c+ cells results in rapid disaggregation and eventual loss of melanoma-specific TRM cells. In addition, we de

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30

Alachiotis, Nikolaos, Panagiotis Skrimponis, Manolis Pissadakis, and Dionisios Pnevmatikatos. "Scalable Phylogeny Reconstruction with Disaggregated Near-memory Processing." ACM Transactions on Reconfigurable Technology and Systems 15, no. 3 (2022): 1–32. http://dx.doi.org/10.1145/3484983.

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Disaggregated computer architectures eliminate resource fragmentation in next-generation datacenters by enabling virtual machines to employ resources such as CPUs, memory, and accelerators that are physically located on different servers. While this paves the way for highly compute- and/or memory-intensive applications to potentially deploy all CPUs and/or memory resources in a datacenter, it poses a major challenge to the efficient deployment of hardware accelerators: input/output data can reside on different servers than the ones hosting accelerator resources, thereby requiring time- and ene

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31

Zhou, Dongguo, Yangjie Wu, and Hong Zhou. "A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi-LSTM Algorithm." Complexity 2021 (January 22, 2021): 1–11. http://dx.doi.org/10.1155/2021/6688889.

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Nonintrusive load monitoring in smart microgrids aims to obtain the energy consumption of individual appliances from the aggregated energy data, which is generally confronted with the error identification of the load type for energy disaggregation in microgrid energy management system (EMS). This paper proposes a classification strategy for the nonintrusive load identification scheme based on the bilateral long-term and short-term memory network (Bi-LSTM) algorithm. The sliding window algorithm is used to extract the detected load event features and obtain the load features of data samples. In

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32

Wang, Yunliang, Honglei Yin, Lin Wang, et al. "Curcumin as a Potential Treatment for Alzheimer's Disease: A Study of the Effects of Curcumin on Hippocampal Expression of Glial Fibrillary Acidic Protein." American Journal of Chinese Medicine 41, no. 01 (2013): 59–70. http://dx.doi.org/10.1142/s0192415x13500055.

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Curcumin, an agent traditionally utilized for its preventative action against tumorigenesis, oxidation, inflammation, apoptosis and hyperlipemia, has also been used in the treatment of Alzheimer's disease (AD). Recent advances in the study of AD have revealed astrocytes (AS) as being key factors in the early pathophysiological changes in AD. Glial fibrillary acidic protein (GFAP), a marker specific to AS, is markedly more manifest during morphological modifications and neural degeneration signature during the onset of AD. Several studies investigating the functionality of curcumin have shown t

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33

Chauhan, Pallavi Singh, Dhananjay Yadav, Bhupendra Koul, Yugal Kishore Mohanta, and Jun-O. Jin. "Recent Advances in Nanotechnology: A Novel Therapeutic System for the Treatment of Alzheimer’s Disease." Current Drug Metabolism 21, no. 14 (2020): 1144–51. http://dx.doi.org/10.2174/1389200221666201124140518.

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: A amyloid-β (Aβ) plaque formation in the brain is known to be the root cause of Alzheimer’s disease (AD), which affects the behavior, memory, and cognitive ability in humans. The brain starts undergoing changes several years before the actual appearance of the symptoms. Nanotechnology could prove to be an alternative strategy for treating the disease effectively. It encompasses the diagnosis as well as the therapeutic aspect using validated biomarkers and nano-based drug delivery systems, respectively. A nano-based therapy may provide an alternate strategy, wherein one targets the protofibri

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34

Giannoula, Christina, Kailong Huang, Jonathan Tang, et al. "DaeMon: Architectural Support for Efficient Data Movement in Fully Disaggregated Systems." Proceedings of the ACM on Measurement and Analysis of Computing Systems 7, no. 1 (2023): 1–36. http://dx.doi.org/10.1145/3579445.

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Resource disaggregation offers a cost effective solution to resource scaling, utilization, and failure-handling in data centers by physically separating hardware devices in a server. Servers are architected as pools of processor, memory, and storage devices, organized as independent failure-isolated components interconnected by a high-bandwidth network. A critical challenge, however, is the high performance penalty of accessing data from a remote memory module over the network. Addressing this challenge is difficult as disaggregated systems have high runtime variability in network latencies/ba

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35

Wood, Michael, Emanuele Ogliari, Alfredo Nespoli, Travis Simpkins, and Sonia Leva. "Day Ahead Electric Load Forecast: A Comprehensive LSTM-EMD Methodology and Several Diverse Case Studies." Forecasting 5, no. 1 (2023): 297–314. http://dx.doi.org/10.3390/forecast5010016.

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Optimal behind-the-meter energy management often requires a day-ahead electric load forecast capable of learning non-linear and non-stationary patterns, due to the spatial disaggregation of loads and concept drift associated with time-varying physics and behavior. There are many promising machine learning techniques in the literature, but black box models lack explainability and therefore confidence in the models’ robustness can’t be achieved without thorough testing on data sets with varying and representative statistical properties. Therefore this work adopts and builds on some of the highes

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36

Fu, Yaosheng, Evgeny Bolotin, Niladrish Chatterjee, David Nellans, and Stephen W. Keckler. "GPU Domain Specialization via Composable On-Package Architecture." ACM Transactions on Architecture and Code Optimization 19, no. 1 (2022): 1–23. http://dx.doi.org/10.1145/3484505.

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As GPUs scale their low-precision matrix math throughput to boost deep learning (DL) performance, they upset the balance between math throughput and memory system capabilities. We demonstrate that a converged GPU design trying to address diverging architectural requirements between FP32 (or larger)-based HPC and FP16 (or smaller)-based DL workloads results in sub-optimal configurations for either of the application domains. We argue that a C omposable O n- PA ckage GPU (COPA-GPU) architecture to provide domain-specialized GPU products is the most practical solution to these diverging requireme

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Çavdar, İsmail Hakkı, and Vahit Feryad. "Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid." Energies 14, no. 15 (2021): 4649. http://dx.doi.org/10.3390/en14154649.

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One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential E

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Park, Jong-Hyeok, Soyee Choi, Gihwan Oh, and Sang-Won Lee. "SaS." Proceedings of the VLDB Endowment 14, no. 9 (2021): 1481–88. http://dx.doi.org/10.14778/3461535.3461538.

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Every database engine runs on top of an operating system in the host, strictly separated with the storage. This more-than-half-century-old IHDE (In-Host-Database-Engine) architecture, however, reveals its limitations when run on fast flash memory SSDs. In particular, the IO stacks incur significant run-time overhead and also hinder vertical optimizations between database engines and SSDs. In this paper, we envisage a new database architecture, called SaS (SSD as SQL database engine), where a full-blown SQL database engine runs inside SSD, tightly integrated with SSD architecture without interv

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Athanasiadis, Christos, Dimitrios Doukas, Theofilos Papadopoulos, and Antonios Chrysopoulos. "A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption." Energies 14, no. 3 (2021): 767. http://dx.doi.org/10.3390/en14030767.

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Smart-meter technology advancements have resulted in the generation of massive volumes of information introducing new opportunities for energy services and data-driven business models. One such service is non-intrusive load monitoring (NILM). NILM is a process to break down the electricity consumption on an appliance level by analyzing the total aggregated data measurements monitored from a single point. Most prominent existing solutions use deep learning techniques resulting in models with millions of parameters and a high computational burden. Some of these solutions use the turn-on transien

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Kim, Jihyun, Thi-Thu-Huong Le, and Howon Kim. "Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature." Computational Intelligence and Neuroscience 2017 (2017): 1–22. http://dx.doi.org/10.1155/2017/4216281.

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Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption

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Lee, Chung Hyeon, Min Sung Ko, Ye Seul Kim, et al. "Neuroprotective Effects of Davallia mariesii Roots and Its Active Constituents on Scopolamine-Induced Memory Impairment in In Vivo and In Vitro Studies." Pharmaceuticals 16, no. 11 (2023): 1606. http://dx.doi.org/10.3390/ph16111606.

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Beta-amyloid (Aβ) proteins, major contributors to Alzheimer’s disease (AD), are overproduced and accumulate as oligomers and fibrils. These protein accumulations lead to significant changes in neuronal structure and function, ultimately resulting in the neuronal cell death observed in AD. Consequently, substances that can inhibit Aβ production and/or accumulation are of great interest for AD prevention and treatment. In the course of an ongoing search for natural products, the roots of Davallia mariesii T. Moore ex Baker were selected as a promising candidate with anti-amyloidogenic effects. T

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Rajan, Sanju, and Linda Joseph. "An Adaptable Optimal Network Topology Model for Efficient Data Centre Design in Storage Area Networks." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 2s (2023): 43–50. http://dx.doi.org/10.17762/ijritcc.v11i2s.6027.

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In this research, we look at how different network topologies affect the energy consumption of modular data centre (DC) setups. We use a combined-input directed approach to assess the benefits of rack-scale and pod-scale fragmentation across a variety of electrical, optoelectronic, and composite network architectures in comparison to a conventional DC. When the optical transport architecture is implemented and the appropriate resource components are distributed, the findings reveal fragmentation at the layer level is adequate, even compared to a pod-scale DC. Composable DCs can operate at peak

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Liu, Shu-Ying, Shuai Lu, Xiao-Lin Yu, et al. "Fruitless Wolfberry-Sprout Extract Rescued Cognitive Deficits and Attenuated Neuropathology in Alzheimer’s Disease Transgenic Mice." Current Alzheimer Research 15, no. 9 (2018): 856–68. http://dx.doi.org/10.2174/1567205015666180404160625.

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Background: Alzheimer’s disease (AD) is a neurodegenerative disease featured by memory loss, neuroinflammation and oxidative stress. Overproduction or insufficient clearance of Aβ leads to its pathological aggregation and deposition, which is considered the predominant neuropathological hallmark of AD. Therefore, reducing Aβ levels and inhibiting Aβ-induced neurotoxicity are feasible therapeutic strategies for AD treatment. Wolfberry has been traditionally used as a natural antioxidant and anti-aging product. However, whether wolfberry species has therapeutic potential on AD remains unknown. M

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Aalipour, Mehdi, Bohumil Šťastný, Filip Horký, and Bahman Jabbarian Amiri. "Scaling an Artificial Neural Network-Based Water Quality Index Model from Small to Large Catchments." Water 14, no. 6 (2022): 920. http://dx.doi.org/10.3390/w14060920.

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Scaling models is one of the challenges for water resource planning and management, with the aim of bringing the developed models into practice by applying them to predict water quality and quantity for catchments that lack sufficient data. For this study, we evaluated artificial neural network (ANN) training algorithms to predict the water quality index in a source catchment. Then, multiple linear regression (MLR) models were developed, using the predicted water quality index of the ANN training algorithms and water quality variables, as dependent and independent variables, respectively. The

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Ajdari, Mohammadamin, Patrick Raaf, Mostafa Kishani, Reza Salkhordeh, Hossein Asadi, and André Brinkmann. "An Enterprise-Grade Open-Source Data Reduction Architecture for All-Flash Storage Systems." Proceedings of the ACM on Measurement and Analysis of Computing Systems 6, no. 2 (2022): 1–27. http://dx.doi.org/10.1145/3530896.

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All-flash storage (AFS) systems have become an essential infrastructure component to support enterprise applications, where sub-millisecond latency and very high throughput are required. Nevertheless, the price per capacity ofsolid-state drives (SSDs) is relatively high, which has encouraged system architects to adoptdata reduction techniques, mainlydeduplication andcompression, in enterprise storage solutions. To provide higher reliability and performance, SSDs are typically grouped usingredundant array of independent disk (RAID) configurations. Data reduction on top of RAID arrays, however,

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Chai, Tian, Xiao-Bo Zhao, Wei-Feng Wang, Yin Qiang, Xiao-Yun Zhang, and Jun-Li Yang. "Design, Synthesis of N-phenethyl Cinnamide Derivatives and Their Biological Activities for the Treatment of Alzheimer’s Disease: Antioxidant, Beta-amyloid Disaggregating and Rescue Effects on Memory Loss." Molecules 23, no. 10 (2018): 2663. http://dx.doi.org/10.3390/molecules23102663.

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Gx-50 is a bioactive compound for the treatment of Alzheimer’s disease (AD) found in Sichuan pepper (Zanthoxylum bungeanum). In order to find a stronger anti-AD lead compound, 20 gx-50 (1–20) analogs have been designed and synthesized, and their molecular structures were determined based on nuclear magnetic resonance (NMR) and mass spectrometry (MS) analysis, as well as comparison with literature data. Compounds 1–20 were evaluated for their anti-AD potential by using DPPH radical scavenging assay for considering their anti-oxidant activity, thioflavin T (ThT) fluorescence assay for considerin

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Kwon, Miryeong, Junhyeok Jang, Hanjin Choi, Sangwon Lee, and Myoungsoo Jung. "Failure Tolerant Training with Persistent Memory Disaggregation over CXL." IEEE Micro, 2023, 1–11. http://dx.doi.org/10.1109/mm.2023.3237548.

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Jang, Junhyeok, Hanjin Choi, Hanyeoreum Bae, Seungjun Lee, Miryeong Kwon, and Myoungsoo Jung. "Bridging Software-Hardware for CXL Memory Disaggregation in Billion-Scale Nearest Neighbor Search." ACM Transactions on Storage, January 6, 2024. http://dx.doi.org/10.1145/3639471.

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We propose CXL-ANNS , a software-hardware collaborative approach to enable scalable approximate nearest neighbor search (ANNS) services. To this end, we first disaggregate DRAM from the host via compute express link (CXL) and place all essential datasets into its memory pool. While this CXL memory pool allows ANNS to handle billion-point graphs without an accuracy loss, we observe that the search performance significantly degrades because of CXL’s far-memory-like characteristics. To address this, CXL-ANNS considers the node-level relationship and caches the neighbors in local memory, which are

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Li, Bingbing, Tongzi Wu, Shijie Bian, and John W. Sutherland. "Predictive model for real-time energy disaggregation using long short-term memory." CIRP Annals, April 2023. http://dx.doi.org/10.1016/j.cirp.2023.04.066.

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Sun, JiaXuan, JunNian Wang, WenXin Yu, ZhenHeng Wang, and YangHua Wang. "Power Load Disaggregation of Households with Solar Panels Based on an Improved Long Short-term Memory Network." Journal of Electrical Engineering & Technology, August 18, 2020. http://dx.doi.org/10.1007/s42835-020-00513-7.

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