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Endurable SSD-Based Read Cache for Improving the Performance of Selective Restore from Deduplication Systems

Journal of computer science & technology 2018年第1期33卷 58-78页

作者： Jian Liu Yun-Peng Chai Xiao Qin Yao-Hong Liu Division of Computer Science and Engineering Louisiana State University Baton Rouge LA 70803 U.S.A. Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Shelby Center for Engineering Technology Department of Computer Science and Software Engineering Samuel Ginn College of Engineering Auburn University Auburn AL 368~9-53~7 U.S.A.

Deduplication has been commonly used in both enterprise storage systems and cloud storage. To overcome the performance challenge for the selective restore operations of deduplication systems, solid-state-drive-based （i.e., SSD-based） re^d cache cm, be deployed for speeding up by caching popular restore contents dynamically. Unfortunately, frequent data updates induced by classical cache schemes （e.g., LRU and LFU） significantly shorten SSDs＇ lifetime while slowing down I/O processes in SSDs. To address this problem, we propose a new solution -- LOP-Cache to greatly improve tile write durability of SSDs as well as I/O performance by enlarging the proportion of long-term popular （LOP） data among data written into SSD-based cache. LOP-Cache keeps LOP data in the SSD cache for a long time period to decrease the number of cache replacements. Furthermore, it prevents unpopular or unnecessary data in deduplication containers from being written into the SSD cache. We implemented LOP-Cache in a prototype deduplication system to evaluate its pertbrmance. Our experimental results indicate that LOP-Cache shortens the latency of selective restore by an average of 37.3% at the cost of a small SSD-based cache with only 5.56% capacity of the deduplicated data. Importantly, LOP-Cache improves SSDs＇ lifetime by a factor of 9.77. The evidence shows that LOP-Cache offers a cost-efficient SSD-based read cache solution to boost performance of selective restore for deduplication systems.

关键词： data deduplication solid state drive （SSD） flash cache endurance

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Deep-RL: Deep Reinforcement Learning for Marking-Aware via per-Port in Data Centers

Deep-RL: Deep Reinforcement Learning for Marking-Aware via p...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Akbar Majidi Xiaofeng Gao Nazila Jahanbakhsh Shahram Jamali Jiaqi Zheng Guihai Chen Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering University of Mohaghegh Ardabili Ardabil Iran State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9781728125848

In this paper, we propose Deep-RL-a marking decision with Deep Reinforcement Learning (DRL) via per-port for solving the erroneously marking problems in multi-queue multi service scenarios of Data Center Networks (DCNs). We formulate the statement as a DRL problem and use Deep Neural Network (DNN) to achieve the best possible policy for the agent. In this way we can model the complex DCNs in order to obtain the optimal threshold in the output port when the marked packets from queue buffers are not a concern. Unlike prior research that focused on mathematical models or used machine learning in DCN, Deep-RL is a novel DRL based method, which optimizes the real value of threshold with continues action space. Thus, this fact makes our work incomparable with previous research. To the best of our knowledge, we are the first to discuss the problem with DRL and DNN. Simulation results demonstrate that Deep-RL utilizes the buffer capacity at exactly 30% and achieves near optimal flow completion time.

关键词： Congestion control Buffer management Multi queue Shared output port Explicit congestion notification data center output ports learning (artificial intelligence) Learning buffering capacity Activity spaces statement

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Conformal mesh parameterization using discrete Calabi flow

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computer AIDED GEOMETRIC DESIGN 2018年 63卷 96-108页

作者： Zhao, Hui Li, Xuan Ge, Huabin Lei, Na Zhang, Min Wang, Xiaoling Gu, Xianfeng Tsinghua University China Beijing Advanced Innovation Center for Imaging Technology Capital Normal University China Computer Science Department State University of New York at Stony Brook USA Department of Mathematics Beijing Jiaotong University China DUT-RU International School of Information and Software Engineering Dalian University of Technology China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province China Brigham and Women's Hospital Harvard Medical School USA Center of Mathematical Sciences and Applications Harvard University USA School of Mechanical Engineering University of Science and Technology Beijing China

In this paper, we introduce discrete Calabi flow to the graphics research community and present a novel conformal mesh parameterization algorithm. Calabi energy has a succinct and explicit format. Its corresponding flow is conformal and convergent under certain conditions. Our method is based on the Calabi energy and Calabi flow with solid theoretical and mathematical base. We demonstrate our approach on dozens of models and compare it with other related flow based methods, such as the well-known Ricci flow and conformal equivalence of triangle meshes (CETM). Our experiments show that the performance of our algorithm is comparably the same with other methods. The discrete Calabi flow in our method provides another perspective on conformal flow and conformal parameterization. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Calabi flow Mesh parameterization Conformal mapping

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A Family of P Systems for Solving Linear Equations

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Chinese Journal of Electronics 2017年第4期26卷 667-674页

作者： GUO Ping WEI Lijiao CHEN Haizhu LIU Ran College of Computer Science Chongqing University Chongqing Key Laboratory of Software Theory and Technology Department of Software Engineering Chongqing College of Electronic Engineering

Solving linear equations is one of the fundamental problems in numerical calculation and has a wide application in many science and engineering fields. We present a family of P systems to solve linear equations in which the coefficients are real numbers. An instance is given to illustrate the feasibility and effectiveness of our designed P systems. Our work contributes to make P systems to solve more complicated computation problems effectively.

关键词： Membrane computing Cell-like P systems Fraction arithmetic operation Numerical calculation P systems

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Optimal verification of two-qubit pure states

arXiv

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arXiv 2019年

作者： Wang, Kun Hayashi, Masahito Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Graduate School of Mathematics Nagoya University Nagoya464-8602 Japan Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore117542 Singapore

In a recent work [Phys. Rev. Lett. 120, 170502 (2018)], Pallister et al. proposed an optimal strategy to verify non-maximally entangled two-qubit pure states under the constraint that the accessible measurements being locally projective and non-adaptive. Their nice result leads naturally to the question: What is the optimal strategy among general LOCC measurements? In this Letter, we answer this problem completely for two-qubit pure states. To be specific, we give the optimal strategy for each of the following available classes of measurements: (i) local operations and one-way classical communication (one-way LOCC) measurements;(ii) local operations and two-way classical communication (two-way LOCC) measurements;and (iii) separable measurements. Surprisingly, our results reveal that for the two-qubit pure state verification problem, two-way LOCC measurements remarkably outperforms one-way LOCC measurements and has the same power as the separable measurements. Copyright © 2019, The Authors. All rights reserved.

关键词： Optimal systems

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Implementing the Matrix Multiplication with DFC on Kunlun Small Scale computer

Implementing the Matrix Multiplication with DFC on Kunlun Sm...

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IEEE International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

作者： Zheng Du Jing Zhang Shihao Sha Qiuming Luo Department of Computer Science and Technology Tsinghua University NHPCC/Guangdong Key Laboratory of popular UPC & Guangdong Province Engineering Center of China-made High Performance Data Computing System in College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

In this paper, we demonstrate a new dataflow platform of DFC, which can handle the successive dataflow computing passes with tagged data. By implementing the matrix multiplication in DFC, we show that DFC can exploit the parallelism automatically with a much simple dataflow graph constructed by DF functions of DFC. Different from the other dataflow execution platform, DFC support multiple worker threads for one dataflow node of DF functions. By running the matrix multiplication program of DFC on Kunlun system, it was verified that DFC get a reasonable speedup for large scale computing for thread number up to 512.

关键词： Distributed computing Handheld computers

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Implicit multi-feature learning for dynamic time series prediction of the impact of institutions

arXiv

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arXiv 2020年

作者： Bai, Xiaomei Zhang, Fuli Hou, Jie Xia, Feng Tolba, Amr Elashkar, Elsayed Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China Computing Center Anshan Normal University Anshan114007 China Library Anshan Normal University Anshan114007 China Computer Science Department Community College King Saud University Riyadh11437 Saudi Arabia Mathematics Department Faculty of Science Menoufia University Shebin El-Kom32511 Egypt Administrative Sciences Department Community College King Saud University Riyadh11437 Saudi Arabia Applied Statistics Department Faculty of Commerce Mansoura University Egypt

Predicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions evolves in time. Previous researches have focused on using the historical relevance scores of different institutions to predict potential future impact for these institutions. In this paper, we explore the factors that can drive the changes of the impact of institutions, finding that the impact of an institution, as measured by the number of the accepted papers of the institution, more is determined by the authors’ influence of the institution. Geographic location of institution feature and state GDP can drive the changes of the impact of institutions. Identifying these features allows us to formulate a predictive model that integrates the effects of individual ability, location of institution, and state GDP. The model unveils the underlying factors driving the future impact of institutions, which can be used to accurately predict the future impact of institutions. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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On local distributed sampling and counting

arXiv

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arXiv 2018年

作者： Feng, Weiming Yin, Yitong Department of Computer Science and Technology Nanjing University State Key Laboratory for Novel Software Technology Nanjing University

In classic distributed graph problems, each instance on a graph specifies a space of feasible solutions (e.g. all proper (_+ 1)-list-colorings of the graph), and the task of distributed algorithm is to construct a feasible solution using local information. We study distributed sampling and counting problems, in which each instance specifies a joint distribution of feasible solutions. The task of distributed algorithm is to sample from this joint distribution, or to locally measure the volume of the probability space via the marginal probabilities. The latter task is also known as inference, which is a local counterpart of counting. For self-reducible classes of instances, the following equivalences are established in the LOCAL model up to polylogarithmic factors: • For all joint distributions, approximate inference and approximate sampling are computationally equivalent. • For all joint distributions defined by local constraints, exact sampling is reducible to either one of the above tasks. • If further, sequentially constructing a feasible solution is trivial locally, then all above tasks are easy if and only if the joint distribution exhibits strong spatial mixing. Combining with the state of the arts of strong spatial mixing, we obtain efficient sampling algorithms in the LOCAL model for various important sampling problems, including: An O(√_log3 n)-round algorithm for exact sampling matchings in graphs with maximum degree_, and an O(log3 n)-round algorithm for sampling according to the hardcore model (weighted independent sets) in the uniqueness regime, which along with the (diam) lower bound in [2] for sampling according to the hardcore model in the non-uniqueness regime, gives the first computational phase transition for distributed sampling. Copyright © 2018, The Authors. All rights reserved.

关键词： Equivalence classes

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Fine-grained event categorization with heterogeneous graph convolutional networks

arXiv

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arXiv 2019年

作者： Peng, Hao Li, Jianxin Gong, Qiran Song, Yangqiu Ning, Yuanxing Lai, Kunfeng Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Department of Computer Science and Engineering Hong Kong University of Science and Technology Platform and Content Group Tencent Institute for Data Science Tsinghua University Department of Computer Science University of Illinois Chicago United States

Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elements in texts and explicit and implicit social network structures. In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pairwise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model. We propose a Knowledgeable meta-paths Instances based social Event Similarity (KIES) between events and build a weighted adjacent matrix as input to the PP-GCN model. Comprehensive experiments on real data collections are conducted to compare various social event detection and clustering tasks. Experimental results demonstrate that our proposed framework outperforms other alternative social event categorization techniques. Copyright © 2019, The Authors. All rights reserved.

关键词： Information services

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A novel bi-hemispheric discrepancy model for eeg emotion recognition

arXiv

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arXiv 2019年

作者： Li, Yang Zheng, Wenming Wang, Lei Zong, Yuan Qi, Lei Cui, Zhen Zhang, Tong Song, Tengfei Key Laboratory of Child Development and Learning Science Ministry of Education Department of Information Science and Engineering Southeast University Nanjing Jiangsu210096 China Key Laboratory of Child Development and Learning Science Ministry of Education School of Biological Sciences and Medical Engineering Southeast University Nanjing Jiangsu210096 China School of Computing and Information Technology University of Wollongong NSW2500 Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu210096 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu210096 China

The neuroscience study [1] has revealed the discrepancy of emotion expression between left and right hemispheres of human brain. Inspired by this study, in this paper, we propose a novel bi-hemispheric discrepancy model (BiHDM) to learn the asymmetric differences between two hemispheres for electroencephalograph (EEG) emotion recognition. Concretely, we first employ four directed recurrent neural networks (RNNs) based on two spatial orientations to traverse electrode signals on two separate brain regions, which enables the model to obtain the deep representations of all the EEG electrodes' signals while keeping the intrinsic spatial dependence. Then we design a pairwise subnetwork to capture the discrepancy information between two hemispheres and extract higher-level features for final classification. Besides, in order to reduce the domain shift between training and testing data, we use a domain discriminator that adversarially induces the overall feature learning module to generate emotion-related but domain-invariant feature, which can further promote EEG emotion recognition. We conduct experiments on three public EEG emotional datasets, and the experiments show that the new state-of-the-art results can be achieved. Copyright © 2019, The Authors. All rights reserved.

关键词： Electroencephalography

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