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A distributed Business-Aware Storage Execution Environment Towards Large-Scale Applications 20th

A Distributed Business-Aware Storage Execution Environment T...

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20th international conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Jiang, Feng Cheng, Yongyang Dong, Changkun Hui, Zhao Yan, Ruibo China Telecom Cloud Comp Corp Beijing Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9783030602390;9783030602383

The rapid development of distributed storage technology has attracted growing attention from both industry and academia. Although distributed system has been studied for many years, however, it is widely applied to engineering practice since the rise of cloud computing recently. Cloud storage constructs a storage resource pool with massive common storage devices through networks, which could be allocated to authorized users on demand. In this paper, we present a distributed business-aware storage execution environment towards large-scale applications. Initially, we present the overall of our proposed storage execution environment and different data delivery models according to corresponding business needs. Furthermore, we design a globally shared and cross-regional deployed metadata service to minimize the access delay of metadata. Finally, the approach presented in this paper has been validated to be effective through a series of evaluation experiments and actual use cases.

关键词： Cloud computing Object-oriented storage distributed system Large scale applications High-performance architecture

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Semi-Supervised Self Training to Assess the Credibility of Tweets

Semi-Supervised Self Training to Assess the Credibility of T...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Leyu Gao Sandeep Shah Nasser Assery Xiaohong Yuan Xiuli Qu Kaushik Roy North Carolina Agricultural and Technical State University Greensboro North Carolina USA

In our daily lives, social media generates a large amount of data, and the value of this information is becoming increasingly obvious today. However, identifying the credibility of information is a complex issue, and manual assessment to evaluate the credibility of information is very time consuming. Therefore, semi-supervised learning using a small amount of labeled data, combined with a large amount of unlabeled data can be a practical approach for evaluating the credibility of information in the data generated in social media. In this experiment, a typical self-training algorithm in semi-supervised learning is used for evaluating the credibility of Tweeter data. The algorithm first trains the classifier with the labeled data using a supervised learning algorithm, and then predicts the category of the unlabeled data with the classifier obtained from the training. We implemented an improved self-training algorithm by using the repeated labeling strategy. We conducted experiments on disaster related Twitter data and predicted the credibility of disaster related Twitter data. Logistic regression and random forest classifiers were used in our experiments. Our experiments show the improved self-training algorithm produced better accuracy in the classification.

关键词： Training Support vector machines Social networking (online) Blogs Semisupervised learning Prediction algorithms Classification algorithms

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Signature Scheme Based on The SM2 Algorithm in Fabric

Signature Scheme Based on The SM2 Algorithm in Fabric

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international conference on networking and Network Applications (NaNA)

作者： Yaling Zhang Hongyun Wang Yichuan Wang Wenjiang Ji Lei Zhu School of computer science and engineering Xi’an University of technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of technology Xi’an China

Based on the analysis and research of the widely used Hyperledger Fabric alliance chain structure, this paper proposes a digital signature scheme based on the national cryptographic algorithm SM2 which can be applied to Hyperledger Fabric. Firstly, two elliptic curve public-key cryptography algorithms are analyzed, and the feasibility of the SM2 in Fabric system is studied. Secondly, the Fabric system using the SM2 algorithm is designed and implemented, and the generation process of the Fabric chain address using the SM2 algorithm is given. Finally, experimental verification of the availability and performance of the Fabric system after the replacement of national cryptography algorithms is carried out. The experimental results show that the SM2 interface of the new Fabric alliance chain has good usability, and the average latency is reduced by 51.8%, and the transaction throughput is increased by 33.1%.

关键词： distributed ledger Elliptic curves Public key cryptography Throughput Fabrics Blockchains Usability

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Robust Social Event Detection via Deep Clustering

Robust Social Event Detection via Deep Clustering

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Jiaofu Zhang Lianzhong Liu Zihang Huang Lihua Han Shuhai Wang Tongge Xu Jingyi Zhang Yangyang Li Yifeng Liu Md Zakirul Alam Bhuiyan School of Cyber Science and Technology Beihang University Beijing China School of Continuing Education Shijiazhuang Railway University Shijiazhuang China National Engineering Laboratory for Risk Perception and Prevention (NEL-RPP) China Academy of Electronics and Information Technology Beijing China Fordham University NY USA

Social networks are quickly becoming the primary medium for discussing what is happening around real-world events. However, it is still a challenge to detect events on social media due to its real-time nature, scale and amount of unstructured data generated. In this paper, we present a novel real-time system for detecting surrounding real-world events. Our proposed framework consists of four main components, including text filtering, text representation, deep clustering, and event merging. After filtering non-event messages, we use entities and words to represent messages. Based on text representation, we propose a novel density clustering algorithm for online event detection. The resulted sub-events are further merged based on time information and keyword similarity. Experiments on standard and real-world datasets demonstrated the effectiveness of our proposed method.

关键词： Event detection Social networking (online) Filtering Semantics Merging Clustering algorithms Media

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Enhancing Matrix Factorization-based Recommender Systems via Graph Neural Networks

Enhancing Matrix Factorization-based Recommender Systems via...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Zhiwei Guo Dian Meng Huiyan Zhang Heng Wang Keping Yu School of Computer Science and Engineering Chongqing Technology and Business University Chongqing China National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing China College of Mechanical and Electrical Engineering Henan Agricultural University Zhengzhou Global Information and Telecommunication Institute Waseda University Tokyo Japan

Due to the serious information overload problem caused by the rapid development of the Internet, recommender system (RS) has been one of the most concerned technologies in the past decade. Accompanied with the prevalence of social networks, social information is usually introduced into RS to pursue higher recommendation efficiency, yielding the research of social recommendations (SoR). Almost all of existing researches of SoR just consider the influence of social relationships, yet ignoring the fact that correlations exist among item attributes and will certainly influence social choices. Therefore, this work introduces the graph neural networks to enhance matrix factorization-based recommender systems. And the proposal in this work is named GNN-MF for short. The user subspace and item subspace in matrix factorization are represented with the use of deep neural networks, in which parameters are learned by back propagation. The experiments well prove efficiency of the GNN-MF.

关键词： Deep learning Backpropagation Correlation Social networking (online) Graph neural networks Internet Proposals

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A global distributed trust management framework 4th

A global distributed trust management framework

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4th international conference on Research in Intelligent and computing in Engineering, RICE 2019

作者： Doan Trung, Son Faculty of Information Technology and Security People’s Security University Hanoi Viet Nam

ISBN: (纸本)9789811527791

There are many ways to interact humanly in today’s society, in complex environments and with mutual dependencies. It is hard to understand and to analyse by formal methods. It is said that safe and secure online trading is obtained by providing recommendations on how users feel of trust. However, existing solutions cannot conquer the complexity of the problem or convey the human trust feeling in a reasonable as well as persuading way. The article suggests a global distributed trust framework solution making trust feeling information from several sources data and use them to generate trustworthiness (i.e. global trust) of each online user, which can be accepted by online social network users for security problem. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Social networking (online)

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Machine Learning-Based, networking and computing Infrastructure Resource Management 17th

Machine Learning-Based, Networking and Computing Infrastruct...

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17th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations (AIAI) / 6th 5G-PINE Workshop / AI-BIO Workshop / DAAI Workshop / DARE Workshop / EEAI Workshop / 10th MHDW Workshop

作者： Chochliouros, Ioannis P. Kostopoulos, Alexandros Payaro, Miquel Verikoukis, Christos di Vimercati, Sabrina De Capitani Vinogradov, Evgenii Ranjbar, Vida Vardakas, John Rahman, Md Arifur Soumplis, Polyzois Varvarigos, Emmanuel Hellen Telecommun Org OTE SA 99 Kifissias Ave Athens 15124 Greece Ctr Tecnol Telecomunicac Catalunya CTTC CERCA Barcelona Spain Univ Milan Milan Italy Katholieke Univ Leuven Leuven Belgium Iquadrat Informat SL Barcelona Spain IS Wireless Warsaw Poland Inst Commun & Comp Syst ICCS Athens Greece

ISBN: (纸本)9783030791599;9783030791575;9783030791568

5G mobile networks will be soon available to handle all types of applications and to provide service to massive numbers of users. In this complex and dynamic network ecosystem, end-to-end performance analysis and optimization will be key features in order to effectively manage the diverse requirements imposed by multiple vertical industries over the same shared infrastructure. To enable such a vision, the MARSAL project [ 1] targets the development and evaluation of a complete framework for the management and orchestration of network resources in 5G and beyond by utilizing a converged optical-wireless network infrastructure in the access and fronthaul/midhaul segments. At the network design domain, MARSAL targets the development of novel cell-free-based solutions. Namely, scalable and cost-efficient wireless access points deployment will be achieved by exploiting the distributed cell-free concept combined with wireless and wired serial fronthaul approaches. We will target the inclusion of these innovative functionalities in the O-RAN project. In parallel, in the fronthaul/midhaul segments MARSAL aims to radically increase the flexibility of optical access architectures for Beyond-5G cell site connectivity via different levels of fixed-mobile convergence. In the network and service management domain, the design philosophy of MARSAL is to provide a comprehensive framework for the management of the entire set of communication and computational network resources by exploiting novel ML-based algorithms of both edge and midhaul data centers, by incorporating the Virtual Elastic Data Centers/Infrastructures paradigm. Finally, at the network security domain, MARSAL aims to introduce mechanisms that provide privacy and security to application workload and data, targeting to allow applications and users to maintain control over their data when relying on the deployed shared infrastructures, while AI and Blockchain technologies will be developed in order to guarantee a secured

关键词： Cell-free distributed cloud Network automation Machine learning Secure multi-tenancy

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Co-designing the Topology/Algorithm to Accelerate distributed Training

Co-designing the Topology/Algorithm to Accelerate Distribute...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Xiang Hou Rui Xu Sheng Ma Qiong Wang Wei Jiang Hongyi Lu Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

With the development of Deep Learning (DL), Deep Neural Network (DNN) models have become more complex. At the same time, the development of the Internet makes it easy to obtain large data sets for DL training. Large-scale model parameters and training data enhance the level of AI by improving the accuracy of DNN models. But on the other hand, they also present more severe challenges to the hardware training platform because training a large model needs a lot of computing and memory resources that can easily exceed the capacity of a single processor. In this context, integrating more processors on a hierarchical system to conduct distributed training is a direction for the development of training platforms. In distributed training, collective communication operations (including all-to-all, all-reduce, and all-gather) take up a lot of training time, making the interconnection network between computing nodes one of the most critical factors affecting the system performance. The hierarchical torus topology, combined with the Ring All-Reduce collective communication algorithm, is one of the current mainstream distributed interconnection networks. However, we believe that its communication performance is not the best. In this work, we first designed a new intra-package communication topology, i.e. the switch-based fully connected topology, which shortens the time consumed by cross-node communication. Then, considering the characteristics of this topology, we carefully devised more efficient all-reduce and all-gather communication algorithms. Finally, combined with the torus topology, we implemented a novel distributed DL training platform. Compared with the hierarchical torus, our platform improves communication efficiency and provides 1.16-2.68 times speedup in distributed training of DNN models.

关键词： Training Deep learning Network topology Multiprocessor interconnection Computational modeling Training data Switches

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Multi-Path Forwarding Strategy for Named Data networking Based on Pending Interests and Available Bandwidth

Multi-Path Forwarding Strategy for Named Data Networking Bas...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Shanshan Shi Jun Li Bin Han Haibo Wu Yuxiang Ma Qian Dong Hans D. Schotten Computer Network Information Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Technische Universität Kaiserslautern Kaiserslautern Germany School of Computer and Information Engineering Henan University Kaifeng China School of electronic information engineering Foshan university Foshan China

Named Data networking (NDN) is a new Internet architecture, and it focuses on the content or name rather than the location or address. One-to-many communication has become the main feature of NDN networks. Therefore, forwarding strategy plays an important role in the content delivery performance enhancement of NDN networks. Most existing schemes tend to consider one or two metrics of different faces, such as round trip time(RTT), pending Interests (PI) and bandwidth (BW). Such single-metric or dual-metric designs usually lower accuracy in characterizing path performance. In addition, the Data packet can carry rich performance information of the path it passes through, which is more powerful than estimating the path performance on the local interface. To this end, we propose a multi-path forwarding strategy named PIBW, considering multiple metrics including PI, available BW, etc. We implement PIBW and conduct performance evaluations based on ndnSIM. Simulation evaluation shows that PIBW outperforms baseline solutions in terms of throughput, link bandwidth utilization and convergence time.

关键词： Performance evaluation Adaptive systems Simulation Estimation Bandwidth Throughput Real-time systems

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Scaling Edge computing through S-BVT and Pb/s Switching Devices in Large Dense Urban Metro Networks 22

Scaling Edge Computing through S-BVT and Pb/s Switching Devi...

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22nd international conference on Transparent Optical Networks (ICTON)

作者： Otero, G. Fernandez-Palacios, J. P. Moreolo, M. Svaluto Larrabeiti, D. Hernandez, J. A. Fabrega, J. M. Lopez, V Nadal, L. Martinez, R. Univ Carlos III Madrid Madrid Spain Telefon Espana Madrid Spain Ctr Tecnol Telecomunicac Catalunya Castelldefels Spain

ISBN: (数字)9781728184234

ISBN: (纸本)9781728184234

Dense metropolitan areas are responsible for the majority of the traffic growth in telecom operators. The focus of the EU H2020 PASSION project is to develop new photonic technologies for supporting agile metro networks and enabling capacities of Tb/ s per channel to deal with such traffic increment, with a particular focus on two components (1) the Sliceable Bandwidth Variable Transceivers (S-BVT) and (2) Pb/s optical switches. This paper analyses two strategies from the perspective of computation resource consumption: overflow over a paired same-level DC and overflow over a centralized site. We present a methodology to compare both schemes and prove that a proper distribution of computing resources in the centralized overflow together with S-BVT high-speed transmission can outperform the costs of a distributed strategy, requiring fewer processors and much smaller DCs.

关键词： S-BVT metro are network software defined networking edge computing overflow traffic PASSION project

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