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IEEE International Conference on Joint Cloud computing (JCC)

作者： Yaojie Li Peichang Shi Jianfei Liu Rui Li Fei Gao Penghui Ma Dong Xie Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Serverless computing, comprised of Function as a Service (FaaS) and Backend as a Service (BaaS), has garnered widespread attention owning to its features such as maintenance-free operations, pay-per-use pricing, and automatic scalability. However, practical usage encounters several challenges: 1) The diversity of user applications makes comprehensive performance evaluation difficult, as benchmark and application tests only reflect performance under specific conditions and cannot fully capture users’ actual experiences across different serverless platforms. 2) Disparities in performance and costs across different serverless platforms make it challenging to achieve optimal performance and cost efficiency through single-cloud deployment, thereby underutilizing the advantages of each platform. 3) Vendor lock-in issues restrict the migration of user applications and exacerbate dependence on a single cloud *** address these challenges, this paper proposes a collaborative mechanism, referred to as DCSA, which integrates FaaS and storage services to achieve automatic cross-cloud deployment of user applications while considering both performance and cost comprehensively. Firstly, we adapt the interfaces of different serverless platforms, effectively reducing the complexity of cross-cloud deployment. Secondly, we develop cost and latency models for the cross-cloud deployment of chained serverless applications and propose a deployment scheduling algorithm that simultaneously considers both latency and cost. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. Results demonstrate that our method can effectively reduce latency (up to 2.3%) and lower costs (up to 9.9%).

关键词： Performance evaluation Adaptation models Costs Scheduling algorithms Scalability Serverless computing Collaboration

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A consistency algorithm for causal and totally ordered event delivery in DVE

A consistency algorithm for causal and totally ordered event...

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International Conference on Computer Science and Information Technology (CSIT)

作者： Hangjun Zhou Wei Zhang Yuxing Peng Sikun Li Department of Information Management Hunan College of Finance and Economics Changsha Hunan China Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha Hunan China

How to preserve causal and totally ordered event delivery is an important issue in real-time serverless DVE(distributed Virtual Environment). However, most of the related works are designed to maintain causal order merely or time stamped order with intensive computation and bandwidth overhead. In this paper, we proposed a novel distributed algorithm to maintain the before-and-after relationship between events, both causal and concurrent, of DVE at each individual node. Several simulation experiments are carried out to evaluate the performance of our algorithm and the results demonstrate that the algorithm is effective in preserving causal and totally ordered event delivery and more efficient than the previous algorithms.

关键词： Artificial neural networks Silicon Educational institutions

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A causality based dynamic message ordering for DVEs on Wide Area Network

A causality based dynamic message ordering for DVEs on Wide ...

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International Conference on Communication Software and Networks, ICCSN

作者： Hangjun Zhou Sha Fu Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha Hunan China Department of Information Management Hunan University of Finance and Economics Changsha Hunan China

Due to the large message transmission latency in distributed Virtual Environments(DVEs) on Wide Area Net-work(WAN), the effectiveness of causality consistency control of message ordering is determined by not only causal order of messages but also the real-timeness. If merely causal order is considered, the real-time property of DVEs may not be ensured because of the unlimited waiting time for the delayed messages. While if only real-timeness is emphasized, there may be too many delayed messages, which have to be discarded, to maintain the quality of causal message ordering. Therefore, a trade-off between the quality of causal order delivery and real-timeness is necessary for DVEs. In this article, a novel causality based message ordering approach is presented. In general, this new approach dynamically balances the demands of causal order delivery and real-timeness. Experiment results demonstrate the approach can enhance the quality of causality, while simultaneously keep the real-time property of DVEs.

关键词： Educational institutions

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Conservative Q-Learning for Mechanical Ventilation Treatment Using Diagnose Transformer-Encoder

Conservative Q-Learning for Mechanical Ventilation Treatment...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuyu Yuan Jinsheng Shi Jincui Yang Chenlong Li Yuang Cai Baoyu Tang Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

The application of artificial intelligence to mechanical ventilation has garnered significant attention, especially with the advancement of deep reinforcement learning. Mechanical ventilation is a medical procedure used in critical care to provide life support for patients with lung injuries. Physicians must continuously diagnose the patient’s condition and adjust ventilator parameters. Existing reinforcement learning treatment models provide decision support but solely focus on treatment while neglecting diagnosis. This paper proposes the DTE-CQL(Diagnose Transformer-Encoder Conservative Q-Learning) model to address this limitation. The DTE model predicts the next time-step observation and generates informative representations for auxiliary treatment. The DTE-CQL model can provide a treatment strategy and performs 1.127 times better than physicians. We trained and validated our model using the MIMIC-III dataset, demonstrating its ability to accurately predict the next time-step observation for diagnosis and provide physicians with a safe, effective, and reasonable treatment strategy.

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CRAWL: A Trace Routing Algorithm Based on Hybrid Two-Layer Topology

CRAWL: A Trace Routing Algorithm Based on Hybrid Two-Layer T...

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International Conference on Computer Sciences and Applications (CSA)

作者： Li-Ming Zheng Xiao-Dong Tan Wei-Dong Sun Xiao-Dong Li Department of Electronics Technology Armed Police Officer Academy Chengdu China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Ministry of Scientific Research Armed Police Officer Academy Chengdu China

ISBN: (纸本)9781479999620

Data distribution is a key technology for resources convergence and sharing in distributed environment. To better meet the requirement for real time data distribution in the dynamic network, a trace routing algorithm named CRAWL based on the hybrid two-layered topology is put forward. The algorithm contains an overlay topology named CBDLO, upper of which consists of multiple distributed balanced binary trees corresponding to different properties and the lower of which is an unstructured topology. CRAWL forwards the data on the lower unstructured topology in the form of random walk, so that the data can be sent to the corresponding upper topology entry, It also includes a matching algorithm named CDM for the parallel matching data properties on the upper distributed and balanced binary tree and transmitting the matched data to the nodes that are interested in the data. The experimental results show that the algorithm can effectively support large-scale data distribution in a dynamical network, reduce distribution overhead and matching delays.

关键词： Topology Peer-to-peer computing Routing distributed databases Network topology Heuristic algorithms Binary trees

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PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain

PPSS: A Privacy-Preserving Supervision Scheme Based on Block...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Ming Chen Peichang Shi Xiang Fu Feng Jiang Fei Gao Penghui Ma Jinzhu Kong National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Kylin Software Co.LTD Changsha China Zhejiang Lab Hangzhou China

In JointCloud computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud computing, and combines the “double encryptions” and “threshold encryption” technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.

关键词： Privacy Uncertainty Decision making Buildings Reliability engineering Blockchains Complexity theory

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On the parallels between Paxos and Raft, and how to port optimizations (Extended Version)

arXiv

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arXiv 2019年

作者： Wang, Zhaoguo Zhao, Changgeng Mu, Shuai Chen, Haibo Li, Jinyang Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Institute of Parallel and Distributed Systems Shanghai Jiao Tong University Department of Computer Science New York University Department of Computer Science Stony Brook University

In recent years, Raft has overtaken Paxos as the consensus algorithm of choice. [53] While many have pointed out similarities between the two protocols, no one has formally mapped out their relationships. In this paper, we show how Raft and Paxos are formally related despite their surface differences. Based on the formal mapping between the two protocols, we show how to automatically port a certain class of optimizations from Paxos to Raft with guaranteed correctness. As case studies, we port and evaluate two optimizations, Mencius and Paxos Quorum Lease to Raft. Copyright © 2019, The Authors. All rights reserved.

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TRANSDUCTIVE NONNEGATIVE MATRIX FACTORIZATION FOR SEMI-SUPERVISED HIGH-PERFORMANCE SPEECH SEPARATION

TRANSDUCTIVE NONNEGATIVE MATRIX FACTORIZATION FOR SEMI-SUPER...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Naiyang Guan Long Lan Dacheng Tao Zhigang Luo Xuejun Yang Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Changsha Hunan 410073 China Centre for Quantum Computation and Intelligent Systems FEIT University of Technology Sydney Sydney NSW 2007 Australia State Key Laboratory of High Performance Computing National University of Defense Technology Changsha Hunan 410073 China

ISBN: (纸本)9781479928941

Regarding the non-negativity property of the magnitude spectrogram of speech signals, nonnegative matrix factorization (NMF) has obtained promising performance for speech separation by independently learning a dictionary on the speech signals of each known speaker. However, traditional NM-F fails to represent the mixture signals accurately because the dictionaries for speakers are learned in the absence of mixture signals. In this paper, we propose a new transductive NMF algorithm (TNMF) to jointly learn a dictionary on both speech signals of each speaker and the mixture signals to be separated. Since TNMF learns a more descriptive dictionary by encoding the mixture signals than that learned by NMF, it significantly boosts the separation performance. Experiments results on a popular TIMIT dataset show that the proposed TNMF-based methods outperform traditional NMF-based methods for separating the monophonic mixtures of speech signals of known speakers.

关键词： Speech Dictionaries as Topic Loudspeakers NMF protocol signals mixtures factorization HIGH-PERFORMANCE

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Security Issues and Solutions in Cloud computing

Security Issues and Solutions in Cloud Computing

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International Conference on distributed computing Systems Workshop

作者： Pengfei You Yuxing Peng Weidong Liu Shoufu Xue National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China Jiangsu Automation Research Institute Jiangsu China CPLA Air Defence Forces Command Academy Zhengzhou Henan China

Recently, Cloud computing, as one of the hottest words in IT world, has drawn great attention. Many IT companies such as IBM, Google, Amazon, Microsoft, Yahoo and others vigorously develop cloud computing systems and related products to customers. However, there are still some difficulties for customers to adopt cloud computing, in which many security issues exist, because data for a customer is stored and processed in cloud, not in a local machine. This paper briefly introduces cloud computing and its key concepts. In particularly, we intend to discuss security requirements and security issues involving data, application and virtualization in cloud computing, as well as current solutions to these issues.

关键词： Cloud computing Computational modeling Virtual machine monitors Operating systems Cryptography

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for Highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yanlong Qiu Siqi Wang Xi Yang Xinyuan Qiu Chengkun Wu Yingbo Cui Canqun Yang Institute for Quantum Information & State Key Laboratory of High-Performance Computing College of Computer Science National University of Defense Technology Changsha P. R. China National Supercomputer Center in Tianjin Tianjin P. R. China National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha P. R. China Department of Biology and Chemistry College of Science National University of Defense Technology Changsha P. R. China

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

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