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Research on cloud architecture of enterprise distributed business information system based on SOA

Research on cloud architecture of enterprise distributed bus...

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2020 international Seminar on Artificial Intelligence, networking and Information Technology, AINIT 2020

作者： Hu, Hai-Rong Fang, Lu-Lu Yang, Chao-Hong Zhang, Yang Department of Information and Communication Army Academy of Armored Forces Beijing China Center of Exercise and Training Army Academy of Armored Forces Beijing China

The current SOA-based Enterprise distributed Business Information System has poor scalability and low utilization, in order to solve this problem, this article analyzes the possibility of integration between Cloud computing and SOA after telling the similarities between the two. We proposed a SOA-based Cloud architecture model, and applied it to Enterprise distributed Information Systems, and proposed SOA-based Enterprise distributed Business Information System Cloud Architecture Conceptual Model and Operating Model. This scheme can well solve the problems of the current system, and can greatly improve the quality and efficiency of system development, and has guiding significance for the development direction of enterprise information systems. © Published under licence by IOP Publishing Ltd.

关键词： Cloud computing architecture

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Blockchain-based Secure Coordination for distributed SDN Control Plane

Blockchain-based Secure Coordination for Distributed SDN Con...

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IEEE conference on Network Softwarization (NetSoft)

作者： Wenjun Fan Sang-Yoon Chang Shubham Kumar Xiaobo Zhou Younghee Park College of Engineering and Applied Science University of Colorado Colorado Springs Colorado Springs CO United States San Jose State University San Jose CA United States

Software-defined wide-area network (SD-WAN) is an emerging and advanced networking platform extending software-defined networking (SDN) across multiple networking domains. Because SD-WAN manages the data plane in the networking domains separated by the public Internet, SDWAN provides a distinct environment and challenges from SDN, including greater risks for the security threats injecting control plane communications from attackers residing outside of the SDN domain. We design and build blockchain-coordinating controllers (BCC) to secure control communications of the SD-WAN controller network formed by the distributed controllers spread across multiple domains. BCC provides resiliency against the security threats in the control plane where an attacker compromises controller communications to manipulate the coordination and the operations of the other controllers. More specifically, BCC provides secure control communications even when up to n controllers’ networking credentials are compromised. BCC is also designed for modularity so that it applies generally across the controller implementations. We prototype BCC using Ethereum and smart contract on CloudLab to validate its effectiveness and efficiency. We experiment on geographically separate nodes on CloudLab and show that BCC achieves the distributed consensus at sub-second level for certificate/key distribution and for network-wide control communication synchronization.

关键词： Cloud computing Analytical models conferences Smart contracts Prototypes Performance gain Blockchains

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Impact of Hardware Impairments on the Performance of Millimeter-Wave Massive MU-MIMO Systems with distributed Antennas 8

Impact of Hardware Impairments on the Performance of Millime...

引用

8th international conference on Communications and networking, ComNet2020

作者： Belaoura, Widad Ghanem, Khalida Shakir, Mohummed Zeeshan Qaraqe, Khalid Department of Electronics Algiers Algeria Algiers Algeria School of Computing Engineering and Physical Sciences University of the West of Scotland Paisley Scotland United Kingdom Texas AM University at Qatar Department of Electrical and Computer Engineering Doha Qatar

ISBN: (纸本)9781728153209

The impact of hardware impairments play an crucial role in any practical communication systems, yet they are generally omitted when investigating the performance of distributed massive multiple input multiple output (MIMO) systems. In particularly, for millimeter-Wave (mm-Wave) bands, no study has been addressed yet. To overcome this limitation, in this paper a mm-Wave distributed massive multi-user (MU) MIMO system based-hybrid beamforming structure, in which the residual hardware impairments have been incorporated in the transmitter processing, is presented. At each base station (BS), a zero-forcing (ZF) baseband processing is designed over the effective channel to efficiently remove the multi-user (MU) interference. The results show that, relative to the co-located mm-Wave massive MIMO with the same antenna configurations, mm-Wave distributed MIMO achieves a significantly higher performance while reducing the impingement of hardware practical defects. © 2020 IEEE.

关键词： Millimeter waves

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PANDA: Population Automatic Neural distributed Algorithm for Deep Leaning

PANDA: Population Automatic Neural Distributed Algorithm for...

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

作者： Jia Wei Xingjun Zhang Zeyu Ji Jingbo Li Zheng Wei High Performance Computing Lab Xi’an Jiaotong University Xi’an China

Deep neural network models perform very brightly in the field of artificial intelligence, but their success is affected by hyperparameters, and the learning rate schedule is one of the most important hyperparameters, and the search for the learning rate schedule is often time-consuming and computationally resource-intensive. In this paper, we propose a Population Automatic Neural distributed Algorithm (PANDA) based on population joint optimization, which uses distributed data parallel deep neural network training to implement a dynamic learning rate schedule optimization strategy based on the population idea, with almost no loss of test accuracy, PANDA is able to dynamically refine the learning rate schedule during model training instead of following the usual suboptimal strategy. We conducted experiments on typical AlexNet, VGG16, and ResNet18 using the Tianhe-3 supercomputing prototype, and the results show that using PANDA to dynamically update the learning rate greatly reduces the learning rate schedule search time while ensuring close performance with the latest population hyperparameter algorithm, and in our experiments, PANDA lead to at max 123.85x speedup, and the experimental results prove the effectiveness and robustness of PANDA.

关键词： Training Deep learning Schedules Heuristic algorithms Sociology Learning (artificial intelligence) Statistics

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Social-Aware Cross-Chain Authentication Strategy in Mobile Edge computing

Social-Aware Cross-Chain Authentication Strategy in Mobile E...

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international conference on Information, Communication and Networks (ICICN)

作者： Jinghan Zhao Hui Yang State Key Laboratory of information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665411578

With the rapid development of mobile edge computing (MEC), the amount of mobile devices and users is growing rapidly, and the distributed management architecture based on the blockchain technology is gradually used for the demand of trust and privacy protection in MEC. While most of the works focus on how to use blockchain to provide a secure and trusted environment in MEC, the problem of excessive authentication cost caused by repeated authentication has not been solved. This paper proposes a social-aware cross-chain authentication strategy in MEC. The concept of social network is introduced into MEC, based on data collected from the users, we can derive the social connection among users and select notaries from users with high credibility to cross the chain. Simulation results show that the proposed strategy can effectively reduce the system cost of distributed management architecture in MEC.

关键词： Costs Multi-access edge computing Social networking (online) Simulation Authentication Computer architecture Blockchains

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Towards HPC I/O Performance Prediction Through Large-scale Log Analysis 20

Towards HPC I/O Performance Prediction Through Large-scale L...

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29th international conference on High-Performance Parallel and distributed computing (HPDC)

作者： Kim, Sunggon Sim, Alex Wu, Kesheng Byna, Suren Son, Yongseok Eom, Hyeonsang Seoul Natl Univ Seoul South Korea Lawrence Berkeley Natl Lab Berkeley CA USA Chung Ang Univ Seoul South Korea

ISBN: (纸本)9781450370523

Large-scale high performance computing (HPC) systems typically consist of many thousands of CPUs and storage units, while used by hundreds to thousands of users at the same time. Applications from these large numbers of users have diverse characteristics, such as varying compute, communication, memory, and I/O intensiveness. A good understanding of the performance characteristics of each user application is important for job scheduling and resource provisioning. Among these performance characteristics, the I/O performance is difficult to predict because the I/O system software is complex, the I/O system is shared among all users, and the I/O operations also heavily rely on networking systems. To improve the prediction of the I/O performance on HPC systems, we propose to integrate information from a number of different system logs and develop a regression-based approach that dynamically selects the most relevant features from the most recent log entries, and automatically select the best regression algorithm for the prediction task. Evaluation results show that our proposed scheme can predict the I/O performance with up to 84% prediction accuracy in the case of the I/O-intensive applications using the logs from CORI supercomputer at NERSC.

关键词： High Performance computing distributed File System I/O Performance Prediction Log Analysis

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distributed V2V computing Offloading Method Based on Delay and Fairness Awareness

Distributed V2V Computing Offloading Method Based on Delay a...

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

作者： Guoliang Yang Mengqi Xiong Guangsheng Feng Yuzheng Liu Yun Huang College of Computer Science and Technology Harbin Engineering University Harbin China The Second Research Institute China Aerospace Science and Industry Corporation Beijing China

The roadside Basic unit (RSU) can relieve the heavy computing and analysis pressure required by vehicles’computing tasks. Due to the mobility of vehicles and limited computing resources, it is difficult to deal extensive vehicle tasks in time. Therefore, we consider the computation task offloading problem between the vehicles, as well as the dynamic topology caused by the vehicle movement and the randomness of task arrival, and propose an online offloading method based on fairness awareness, which can be ensured tolerable delays and the requirement of the quality of experience (QoE) between vehicles by introducing delay-aware and price-constrained virtual queues. By Lyapunov optimization technology, the objective function is transformed into a drift-plus-penalty profit minimum bound problem, achieveing all the offloading decisions by the realtime resource requirements and topology state. Finally, extensive simulations are performed to demonstrate the efficiency of the proposed algorithm.

关键词： Vehicular ad hoc networks Transforms Linear programming Real-time systems Delays Topology Quality of experience

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ECCH: Erasure Coded Consistent Hashing for distributed Storage Systems

ECCH: Erasure Coded Consistent Hashing for Distributed Stora...

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

作者： Yinglong Xiong Jiang Zhou Lin Su Weiping Wang Yong Chen Chinese Academy of Sciences Institute of Information Engineering Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Texas Tech University Lubbock USA

In this paper, we propose ECCH, an Erasure Coded Consistent Hashing scheme to make better data placement in distributed storage systems. It combines the inherent advantages of consistent hashing together with the storage-efficiency of erasure coding technology. Specifically, ECCH divides data block stream of files into groups according to block IDs. In each group, it encoded data blocks with additional parity blocks by erasure coding. All encoded blocks in the same group are stored on different nodes with consistent hashing distribution. For node failure or data loss, ECCH locates required data through the ID of missing block in a same group for fast recovery. To deal with node changes, ECCH introduces a design of multi-version hash rings to manage data layout. It can prevent the impact of data migration on erasure coding, while achieving data balance with little data movement. We have implemented ECCH on the Sheepdog, a distributed object-based storage system. Evaluation results show that ECCH can greatly improve the space utilization of hashing-based storage systems, while achieving efficient fault tolerance.

关键词： Fault tolerance Data centers Cloud computing Scalability Layout Fault tolerant systems distributed databases

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A distributed Computation Offloading Scheduling Framework based on Deep Reinforcement Learning

A Distributed Computation Offloading Scheduling Framework ba...

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

作者： Bin Dai Tao Ren Jianwei Niu Zheyuan Hu Shucheng Hu Meikang Qiu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China Texas A&M University-Commerce TX USA

Recent years have witnessed the rapid growth of smart devices and mobile applications. However, mobile applications are typically computation-intensive and delay-sensitive, while User Devices (UDs) are usually resource-limited. Mobile Edge computing (MEC) has been proposed as a promising paradigm to mitigate the tension, where UDs’ tasks could be executed either locally on itself or remotely on the edge server via computation offloading. Lots of efficient computation offloading scheduling approaches have been proposed, whereas most of them are based on centralized scheduling which could face troubles in large-scale MEC. To address the issue, this paper proposes a distributed scheduling framework by leveraging the idea of ‘centralized training and distributed scheduling’. Furthermore, the Actor-Critic reinforcement learning is adopted to build the framework where the Actor and Critic play the roles of distributed scheduling and centralized training, respectively. Extensive simulations are conducted and the experimental results verify the effectiveness and efficiency of the proposed framework.

关键词： Training Processor scheduling Computational modeling Simulation Reinforcement learning Mobile applications Servers

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Grouping Synchronous to Eliminate Stragglers with Edge computing in distributed Deep Learning

Grouping Synchronous to Eliminate Stragglers with Edge Compu...

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

作者： Zhiyi Gui Xiang Yang Hao Yang Wei Li Lei Zhang Qi Qi Jingyu Wang Haifeng Sun Jianxin Liao The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Huawei Beijing China

With the development of artificial intelligence(AI) applications, a large number of data are generated from mobile or IoT devices at the edge of the network. Deep learning tasks are executed to obtain effective information in the user data. However, the edge nodes are heterogeneous and the network bandwidth is limited in this case, which will cause general distributed deep learning to be inefficient. In this paper, we propose Group Synchronous Parallel (GSP), which uses a density-based algorithm to group edge nodes with similar training speeds together. In order to eliminate stragglers, group parameter servers are responsible for coordinating communication of nodes in the group with Stale Synchronous Parallel and aggregating the gradients of these nodes. And a global parameter server is responsible for aggregating the gradients from the group parameter servers to update the global model. To save network bandwidth, we further propose Grouping Dynamic Sparsification (GDS). It adjusts the gradient sparsification rate of nodes dynamically based on GSP so as to differentiates the communication volume and makes the training speed of all nodes tend to be the same. We evaluate GSP and GDS’s performance on LeNet-5, ResNet, VGG, and Seq2Seq with Attention. The experimental results show that GSP speedups the training by 45% ~ 120% with 16 nodes. GDS on top of GSP can make up for some test accuracy loss, up to 0.82% for LeNet-5.

关键词： Training Deep learning Bridges Scalability Clustering methods Bandwidth Life estimation

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