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Evaluation of Topology-Aware All-Reduce Algorithm for Dragonfly networks 18th

Evaluation of Topology-Aware All-Reduce Algorithm for Dragon...

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18th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2021

作者： Ma, Junchao Dong, Dezun Li, Cunlu Wu, Ke Xiao, Liquan National University of Defense Technology Changsha China

ISBN: (纸本)9783030935702

Dragonfly is a popular topology for current and future high-speed interconnection networks. The concept of gathering topology information to accelerate collective operations is a very hot research field. All-reduce operations are often used in the research fields of distributed machine learning (DML) and high-performance computing (HPC), because All-reduce is the key collective communication algorithm. The hierarchical characteristics of the dragonfly topology can be used to take advantage of the low communication delay of adjacent nodes to reduce the completion time of All-reduce operations. In this paper, we propose g-PAARD, a general proximity-aware All-reduce communication on the Dragonfly network. We study the impact of different routing mechanisms on the All-reduce algorithm, and their sensitivity to topology size and message size. Our results show that the proposed topology-aware algorithm can significantly reduce the communication delay, while having little impact on the network topology. © 2022, IFIP international Federation for Information Processing.

关键词： Interconnection networks (circuit switching)

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A Cross Data Center Access Control Model by Constructing GAS on Blockchain

A Cross Data Center Access Control Model by Constructing GAS...

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international conference on parallel and distributed Systems (ICPADS)

作者： Shujiang Xu Chaoyang Zhang Lianhai Wang Shuhui Zhang Wei Shao Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

With the rapid development of cloud computing, big data, and mobile Internet technology, data sharing across different data centers is increasingly urgent. Meanwhile, data security risks such as unauthorized access and data abuse bring serious challenges to data security sharing. Due to its poor scalability, traditional access control technology cannot play its role in the distributed computing paradigms with massive data and dynamic network environment. Attribute-based access control (ABAC) model is a potential candidate for data sharing cross data centers. But it still faces some challenges, such as different attribute semantics among data centers, and slow query speed of attributes and policies. This paper proposes an architecture for access control across data centers by constructing a global attribute set (GAS) on the blockchain to manage attributes and policies. To achieve a high efficiency, the scheme utilizes world state rather than block for query of data attributes and policies, and employs trust degree to pre authorization. Security analysis and experimental results show that the model is secure and practicable.

关键词： Access control Data centers Analytical models Computational modeling Data security Semantics Smart contracts

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Sync-Switch: Hybrid Parameter Synchronization for distributed Deep Learning 41

<i>Sync</i>-<i>Switch</i>: Hybrid Parameter Synchronization ...

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41st IEEE international conference on distributed computing Systems (ICDCS)

作者： Li, Shijian Mangoubi, Oren Xu, Lijie Guo, Tian Worcester Polytech Inst Worcester MA 01609 USA Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China

ISBN: (纸本)9781665445139

Stochastic Gradient Descent (SGD) has become the de facto way to train deep neural networks in distributed clusters. A critical factor in determining the training throughput and model accuracy is the choice of the parameter synchronization protocol. For example, while Bulk Synchronous parallel (BSP) often achieves better converged accuracy, the corresponding training throughput can be negatively impacted by stragglers. In contrast, Asynchronous parallel (ASP) can have higher throughput, but its convergence and accuracy can be impacted by stale gradients. To improve the performance of synchronization protocol, recent work often focuses on designing new protocols with a heavy reliance on hard-to-tune hyper-parameters. In this paper, we design a hybrid synchronization approach that exploits the benefits of both BSP and ASP, i.e., reducing training time while simultaneously maintaining the converged accuracy. Based on extensive empirical profiling, we devise a collection of adaptive policies that determine how and when to switch between synchronization protocols. Our policies include both offline ones that target recurring jobs and online ones for handling transient stragglers. We implement the proposed policies in a prototype system, called Sync-Switch, on top of TensorFlow, and evaluate the training performance with popular deep learning models and datasets. Our experiments show that Sync-Switch can achieve ASP level training speedup while maintaining similar converged accuracy when comparing to BSP. Moreover, Sync-Switch's elastic-based policy can adequately mitigate the impact from transient stragglers.

关键词： distributed deep learning synchronization policy design empirical performance optimization

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Ideological and Political Theories Teaching in All Courses Learning System using user-to-user -based Collaborative Filtering Algorithm

Ideological and Political Theories Teaching in All Courses L...

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distributed Systems, Computer networks and Cybersecurity (ICDSCNC), international conference on

作者： Yuyin Xu Weimin Zhong Department of Organization and Propaganda United Front Work Guangdong Mechanical and Electrical Polytechnic Guangzhou China Asset and Equipment Management Department Guangdong Mechanical and Electrical Polytechnic Guangzhou China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Recently, process in education and classroom reform has emerged lately as a result of the growth, integration, and instructional resources of information technology. However, addressing the issues in ideological academic programs of educational system of colleges and universities, which was inconsistent and ineffectual. Therefore, this research suggested using the User-to-User -Based Collaborative Filtering Algorithm (UUBCFA) to evaluate the effectiveness of the career planning teaching model in diverse academic contexts and addressing these existing issues. Initially, the precise path guidance and real-time recommendation are obtained by using the sparsity of the data. An ideological and political course suggestion design serves the role of ensuring that the system operates in accordance with the established protocol. Consequently, the proposed UUBCFA discovers users with similar likes, recommends courses, and uses peer similarity to improve recommendation accuracy. According to the results, it clearly illustrated that the suggested UUBCFA methodology have achieved better performance in accuracy of 99.89% and precision of 99.95% which is comparatively higher than existing models such as Intellectual Cognitive-Inspired computing (ICIC), Deep Attention-based parallel Dual-channel Model (DAPDM) and Massive Open Online Courses (MOOCs) respectively.

关键词： Accuracy Protocols Engineering profession Reviews Computational modeling Collaborative filtering Education Real-time systems Planning Peer-to-peer computing

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Digital Twin Enhanced Data Protection Based on Cloud-Edge Collaboration in Healthcare System

Digital Twin Enhanced Data Protection Based on Cloud-Edge Co...

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IEEE international conference on Smart Cloud (SmartCloud)

作者： Xinzheng Feng Jun Wu Qianqian Pan Jianhua Li School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Engineering The University of Tokyo Japan

ISBN: (数字)9798350389500

ISBN: (纸本)9798350389517

Empowered with sensing networks and edge computing, the Healthcare system represented by the health monitoring system (HMS) gradually becomes an important part of future medical technology. HMS has been gradually applied to practice scenarios, occurring that the resource-restricted Internet of Things (IoT) nodes and heterogeneous networks’ structure make data protection more difficult. Since anomalies of HMS commonly cause extensive loss even human safety, timely anomaly prediction schemes are regarded as valuable to ensure the data protection of HMS. Most of the existing solutions are based on historical logs, which makes it difficult to identify new faults and easily causes extensive security threats. To solve this problem, we proposed a distributed digital twins (DT) enhanced abnormal prediction scheme based on cloud-edge collaboration to improve the availability and data protection of the HMS. This work proposed an HMS framework based on distributed DT to take full advantage of the parallel simulating capabilities of DT. In addition, considering the limitation of user demand and marginal server resources, we further proposed customized DT simulation operating mechanisms for medical devices in the HMS environment. The feasibility and efficiency of this work were discussed and proved in the analysis and performance evaluation.

关键词： Cloud computing Medical devices Data protection Collaboration Predictive models Digital twins Servers

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Research on Large-scale Data Anomaly Detection based on Deep Learning

Research on Large-scale Data Anomaly Detection based on Deep...

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Information Science, parallel and distributed Systems (ISPDS), international conference on

作者： Gancheng Zhuang School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

With the continuous progress of big data, Internet of Things, artificial intelligence and other technologies, we are living in an era of information explosion. A large amount of data is constantly recorded, and this data contains important information at the critical moment. With the rapid increase in data volume, traditional statistical methods are gradually unable to meet the needs due to high computational complexity and poor adaptability. In this work, we utilize autoencoder and generative adversarial network deep learning methods to detect anomalies in large-scale data. Autoencoders learn low-dimensional representations of data through the encoding and decoding process, and are mainly used to reconstruct normal data and identify anomalies by reconstructing errors. The generative adversarial network grasps the distribution characteristics of the data through the adversarial learning of its generative model and discriminant model, and uses this ability to detect abnormal patterns in the data. The effectiveness of the proposed model was verified by conducting experiments on multiple real-world datasets. Experimental results show that compared with the traditional method, the proposed deep learning model has significant improvement in detection accuracy and computing efficiency.

关键词： Deep learning Adaptation models Information science Statistical analysis Computational modeling Generative adversarial networks Data models

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A Brief Study on Cloud Security

A Brief Study on Cloud Security

引用

international conference on computing and Networking Technology (ICCNT)

作者： Abhay Ajith Adharsh S Mathew Remya S Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

distributed, parallel, and grid computing are all used in a kind of computing known as cloud computing. It serves as a flexible, affordable, and tried-and-true online delivery platform for IT services marketed to businesses or consumers. The outsourcing of critical services to external entities has become a common practice, and this trend has led to various challenges in ensuring the security and privacy of data, maintaining data availability and recovery, and ensuring compliance. Cloud computing, in particular, has added a new layer of complexity to these issues and has introduced additional risks. Cloud computing makes use of many different technologies. Additionally, it carries along its inherent security flaws. Customer access to networks, storage, servers, services, and applications is made possible by cloud technology thanks to how well the pay-per-use model’s services function. Our main goal is to locate and connect these systems’ weaknesses, threats, and remedies.

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Joint DNN Partitioning and Resource Allocation for Multiple Machine Learning-Based Mobile Applications at the Network Edge

Joint DNN Partitioning and Resource Allocation for Multiple ...

引用

IEEE international conference on Communications (ICC)

作者： Cheng-Yu Cheng Robert Gazda Hang Liu Dept. of Electrical Engineering & Computer Science The Catholic University of America Washington DC USA InterDigital Communications Inc. Conshohocken PA USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

As a core technique of machine learning, deep neural networks (DNNs) have been extensively used in today's mobile applications. However, users' mobile devices (MDs) have limited capabilities to execute computation-intensive DNN inference operations and meet the latency constraints. Offloading part of DNN computations to edge servers (ESs) in a mobile edge computing (MEC) network can mitigate these challenges. However, prior studies on offloading either overlook the varying computation demands and output data sizes for different layers of neural networks or only consider split DNN offloading based on a single user or a single ES. Driven by the question of how to partition multiple parallel DNN inferences and offload multiple partitioned DNN processes in a multi-user multi-ES network, in this paper, we design a distributed scheme that jointly optimizes user-ES association, DNN layer-level partitioning, computing and wireless communication resource allocation, and offloading. Specifically, MDs and ESs make decisions to maximize their own utilities based on a multi-leader multi-follower Stackelberg game by leveraging DNN layer characteristics and taking account of multi- server heterogeneous network environments, computation load, and available resources. The evaluation results show that the proposed joint optimization scheme can significantly improve the performance of DNN inferences, compared to the commonly used benchmarks.

关键词： Wireless communication Multi-access edge computing Artificial neural networks Machine learning Mobile handsets Heterogeneous networks Mobile applications

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Multi-Task Scheduling Method for Information Security of Heterogeneous Big Data networks Based on Artificial Intelligence Technology

Multi-Task Scheduling Method for Information Security of Het...

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Sensors, Electronics and Computer Engineering (ICSECE), IEEE international conference on

作者： Yiping Chen Yingzi Zheng Department of Computer Science and Engineering Guangzhou Institute of Science and Technology Guangzhou China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

Multi-task scheduling of heterogeneous big data network information is proposed to improve the optimal configuration and application ability of network big data resources. A multi-task scheduling method of heterogeneous big data network information security based on artificial intelligence technology is proposed. A distributed storage and transmission structure model of heterogeneous big data is established under the task scheduling framework of cloud computing, and the heterogeneous mapping relationship is described according to the task submitted by users and the network information distribution between virtual machines. This paper establishes a scheduling channel between virtual machines and hosts, reasonably places and migrates heterogeneous big data network information to suitable hosts through artificial intelligence technology, and realizes reasonable allocation of computing resources and multi-task scheduling by using weighted polling scheduling method, so as to realize load-balanced scheduling of heterogeneous network information. The simulation results show that this method has good load balance in information task scheduling of heterogeneous big data networks, and the scheduling method has excellent global search and parallel search capabilities, which can adapt to large-scale information optimization of heterogeneous big data networks.

关键词： Processor scheduling Simulation Information security Big Data Multitasking Data models Resource management Artificial intelligence Optimization Load modeling

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Continuous Complex Relational Community Detection on Multi-layer networks

Continuous Complex Relational Community Detection on Multi-l...

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

作者： Chuanyu Zong Ziyi Cai Wenju Li Tao Qiu Jiajia Li Xiufeng Xia School of Computer Science Shenyang Aerospace University Shenyang China Shenyang Aircraft Corporation Shenyang China

Detecting communities in multi-layer heterogeneous information networks (HINs) is an important problem in graph analysis such as social network analysis and genome element analysis. Relational community detection on single-layer networks cannot reflect the actual complex requirements of users who find the desired communities from HINs, the reason is that single-layer networks can only reflect the relational communities in the current context. To overcome the limitations of relational communities on single-layer networks, we propose the problem of Continuous Complex Relational Community Detection (CCRCD) on multi-layer networks, which aims to find a maximum complex relational community that satisfies relational and temporal constraints on multi-layer networks. CCRCD allows users to specify more fine-grained connection requirements by limiting the number of meta-paths between vertices in communities and the duration time of communities. In this case, the problem of detecting continuous complex relational communities is defined over multi-layer networks in this paper. To solve this problem, we explore two exact algorithms based on the idea of sliding window and multi-branch detection, respectively. We also propose a heuristic algorithm to solve the CCRCD problem. Finally, we conduct comprehensive experimental studies on four real HINs to demonstrate that our algorithms can effectively find continuous complex relational communities on multi-layer networks.

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