检索结果-内蒙古大学图书馆

Fault-tolerance approaches for distributed and cloud computing environments: A systematic review, taxonomy and future directions

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第13期36卷 e8081-e8081页

作者： Kirti, Medha Maurya, Ashish Kumar Yadav, Rama Shankar Motilal Nehru Natl Inst Technol Allahabad Dept Comp Sci & Engn Prayagraj India

Fault tolerance is crucial in ensuring smooth working of distributed and cloud computing. It is challenging to implement because of the constantly changing infrastructure and complex configurations in distributed and cloud computing. Implementation of various fault tolerance methods require domain-specific knowledge as well as in-depth understanding of the existing techniques and approaches. Recent surveys on fault tolerance in cloud and distributed environments exist, but they have limitations. This article systematically reviews fault tolerance approaches in distributed and cloud computing and discusses their taxonomy. Based on the taxonomy provided, fault-tolerance approaches are divided into four types, that is, reactive approaches, proactive approaches, adaptive approaches, and hybrid approaches. Reactive approaches provide a preventive measure after the occurrence of faults in the system. Proactive approaches prevent the system or minimize failure effects by predicting in advance. The adaptive approaches predict, learn, and adapt the changes to deal with new faults in the system. The hybrid approaches combine reactive, proactive, and adaptive approaches. The objective of this article is to give a better understanding of handling faults using suitable approaches and further compare them on various parameters. The paper also presents a promising research direction based on the challenges and issues in multiple approaches.

关键词： adaptive cloud distributed computing fault tolerance proactive reactive

来源：评论

学校读者我要写书评

暂无评论

Wireless MapReduce Arrays for Coded distributed computing

Wireless MapReduce Arrays for Coded Distributed Computing

引用

IEEE (first ITW conference of the year) Information Theory Workshop (ITW)

作者： Elizabath Peter K. K. Krishnan Namboodiri B. Sundar Rajan Department of Electrical Communication Engineering IISc Bangalore India

ISBN: (数字)9798350348934

ISBN: (纸本)9798350348941

We study the wireless MapReduce distributed computing system in [5], which operates in three phases: Map, Shuffle, and Reduce. The system consists of a set of distributed nodes assigned to compute arbitrary output functions depending on a file library. The computation of the output functions is decomposed into Map and Reduce functions, and the Shuffle phase, which involves the data exchange, links the two. In our model, the Shuffle phase communication happens over a full-duplex wireless interference channel. For this setting, a coded wireless MapReduce distributed computing scheme exists in the literature, achieving optimal performance under one-shot linear schemes. However, the scheme requires the number of input files to be very large, growing exponentially with the number of nodes. We present schemes that require the number of files to be in the order of the number of nodes and achieve the same performance as the existing scheme. The schemes are obtained by designing a structure called wireless MapReduce array that succinctly represents all three phases in a single array. The wireless MapReduce arrays can also be obtained from the extended placement delivery arrays known for multi-antenna coded caching schemes.

关键词： Wireless communication Phased arrays Conferences Computational modeling Full-duplex system Libraries Interference channels distributed computing Periodic structures Information theory

来源：评论

学校读者我要写书评

暂无评论

Group-Wise Coding for Coded distributed computing Systems with Group Heterogeneity and Communication Delay

Group-Wise Coding for Coded Distributed Computing Systems wi...

引用

International Conference on Ubiquitous and Future Networks, ICUFN

作者： Maryam Haghighi Ardakani Masoud Ardakani Chintha Tellambura Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9798350385298

ISBN: (纸本)9798350385304

In distributed computing, coding techniques are shown to be an effective solution for mitigating the impact of stragglers. However, previous research on coding has predominantly focused on homogeneous worker environments, overlooking the fact that real-world systems often consist of heterogeneous workers with varying computing and communication capabilities. Specifically, uniform load allocation, without considering worker heterogeneity, can result in substantial latency losses. In this paper, we propose a load allocation strategy designed for distributed systems with group heterogeneity, where workers in the same group have similar computing and communication capabilities, but workers of different groups do not. By exploring group-wise MDS codes, we determine the optimal code dimension and optimal computation loads for individual groups. Our proposed approach demonstrates comparable computation times to existing methods, while exhibiting the advantage of much shorter encoding and decoding times.

关键词： Codes Encoding Delays Decoding Resource management distributed computing

来源：评论

学校读者我要写书评

暂无评论

EMDRIVE Architecture: Embedded distributed computing and Diagnostics from Sensor to Edge

EMDRIVE Architecture: Embedded Distributed Computing and Dia...

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Patrick Schmidt Iuliia Topko Matthias Stammler Tanja Harbaum Juergen Becker Rico Berner Omar Ahmed Jakub Jagielski Thomas Seidler Markus Abel Marius Kreutzer Maximilian Kirschner Victor Pazmino Betancourt Robin Sehm Lukas Groth Andrija Neskovic Rolf Meyer Saleh Mulhem Mladen Berekovic Matthias Probst Manuel Brosch Georg Sigl Thomas Wild Matthias Ernst Andreas Herkersdorf Florian Aigner Stefan Hommes Sebastian Lauer Maximilian Seidler Thomas Raste Gasper Skvarc Bozic Ibai Irigoyen Ceberio Muhammad Hassan Albrecht Mayer Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Ambrosys GmbH Potsdam Germany FZI Research Center for Information Technology Karlsruhe Germany Universitat zu Lubeck Lubeck Germany Technical University of Munich Munich Germany Elektrobit Automotive GmbH Erlangen Germany ZF Friedrichshafen AG Friedrichshafen Germany Siemens AG Technology Erlangen Germany Continental Automotive Technologies GmbH Frankfurt am Main Germany Infineon Technologies AG Munich Germany

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Future automotive architectures are expected to transition from a network-centric to a domain-centered architecture featuring central compute units. Powerful domain controllers or smart sensors alleviate the load on these central units and communication systems. These controllers execute tasks with varying criticalities on heterogeneous multicore processors, and are ideally capable of dynamically balancing the computing load between the central unit and sensors. Here, Artificial Intelligence (AI) capabilities playa crucial role, as it is in high demand for such an automotive architecture. However, AI still requires specialized accelerators to improve their computation performance. Task-oriented distributed computing with criticalities up to ASIL-D necessitates the development and utilization of specialized methodologies, such as safety, through the isolation and abstraction of low-level hardware concepts. Meanwhile, online monitoring and diagnostics become vital features to detect errors during operation. The EMDRIVE architecture includes methods, components, and strategies to enhance the performance, safety, and security of such distributed computing platforms. The nationally funded EMDRIVE project connects its twelve partners from academia and industry and is currently in its intermediate stage.

关键词： Computer architecture Sensor fusion Feature extraction Safety Security distributed computing Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Joint Task Allocation and Scheduling for Multi - Hop distributed computing

Joint Task Allocation and Scheduling for Multi - Hop Distrib...

引用

IEEE International Conference on Communications (ICC)

作者： Ke Ma Junfei Xie Department of Electrical and Computer Engineering University of California San Diego and San Diego State University La Jolla USA Department of Electrical and Computer Engineering San Diego State University San Diego USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The rise of edge computing has shifted computing resources closer to end-users, benefiting numerous delay-sensitive, computation-intensive applications. To speed up computation, distributed computing is a promising technique that allows parallel execution of computation tasks across multiple compute nodes. However, current research predominantly revolves around the master-worker paradigm, limiting resource sharing within one-hop neighborhoods. This limitation can render distributed computing ineffective in scenarios with limited nearby resources or constrained/dynamic connectivity. In this paper, we address this limitation by introducing a new distributed computing strategy that extends resource sharing beyond one-hop neighborhoods through exploring layered network structures and multi-hop routing. Our approach involves transforming the network graph into a sink tree and solving a joint optimization problem formulated based on the layered tree structure for task allocation and scheduling. Simulation results demonstrate a significant improvement over the traditional distributed computing and computation offloading strategies.

关键词： Limiting Processor scheduling Simulation Spread spectrum communication Routing Resource management distributed computing

来源：评论

学校读者我要写书评

暂无评论

Cloud-Driven AI: Enhancing Machine Learning Performance Through distributed computing

Cloud-Driven AI: Enhancing Machine Learning Performance Thro...

引用

International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Kunal Dixit Himani Parul Bhardwaj Ritu Sachdeva School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

The exponential data volume and complexity of Machine Learning (ML) algorithms has resulted in an increase in computational limitations, which affects artificial intelligence [AI]. This research seeks to establish whether cloud-based distributed computing can effectively address these limitations and improve the performance of ML. Our approach is a new architecture that deploys the computing load over a cloud framework in order to distribute and process in parallel some of the computational majorities of learning algorithms. To further enhance the application’s efficiency of processing the circuit data, a combination of adaptive resource allocation techniques, data partitioning strategies, and a new synchronization protocol has been proposed and implemented. This framework was assessed with deep neural networks, random forests, gradient-boosting machines to different datasets, and complex tasks. Focusing on the obtained results, it becomes possible to state that the proposed methods yield up to 87% improvement in the training speed and 5–12% increase in the accuracy of the models compared to the traditional implementations in single nodes. Moreover, our approach proved its capability of scalability did not degrade the performance and achieved near-linear speedup throughout the distributed nodes of up to 1000. These results propose that cloud-based AI can significantly reduce time to on-ramp for training numerous models on larger datasets. In light of this, distributed computing in the cloud offers a way to meet the computational demands of today’s AI applications while opening the door to new advancements in ML throughout different domains.

关键词： Training Machine learning algorithms Protocols Scalability Government Partitioning algorithms Synchronization Resource management distributed computing Random forests

来源：评论

学校读者我要写书评

暂无评论

Hands-On Learning: Teaching Parallel and distributed computing through Unplugged Activities in Undergraduate CS Courses

Hands-On Learning: Teaching Parallel and Distributed Computi...

引用

High Performance computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

作者： Anurag Dasgupta Venkat Margapuri Simon Shamoun Shubbhi Taneja Matthew Toups Computer Science and Engineering Technology Valdosta State University Valdosta GA Department of Computing Sciences Villanova University Villanova PA Department of Computer Science Hofstra University Hempstead NY Department of Computer Science Worcester Polytechnic Institute Worcester Massachusetts Computer Science Department Tulane University New Orleans LA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

The authors present and evaluate an unplugged activity to introduce parallel computing concepts to undergraduate students. Students in five CS classrooms used a deck of playing cards in small groups to consider how parallelization can improve performance and how improvement decreases with increased parallelization. Before and after the activity, students took a short survey about their solution and their ideas about parallelism. The authors carried out this activity in seven courses at five institutions in the 2023-2024 academic year. The results showed that students had an increased appreciation for parallelization and this type of activity.

关键词： Surveys High performance computing Conferences Education Parallel processing distributed computing

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient distributed Edge computing to Assist Dense Internet of Things

引用

FUTURE INTERNET 2025年第1期17卷 37-37页

作者： Algarni, Sumaiah Abd El-Samie, Fathi E. Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Univ Liverpool Dept Elect Engn & Elect Liverpool L69 7ZX England Menoufia Univ Fac Elect Engn Dept Elect & Elect Commun Menoufia 32952 Egypt

The Internet of Things (IoT) represents a rapidly growing field, where billions of intelligent devices are interconnected through the Internet, enabling the seamless sharing of data and resources. These smart devices are typically employed to sense various environmental characteristics, including temperature, motion of objects, and occupancy, and transfer their values to the nearest access points for further analysis. The exponential growth in sensor availability and deployment, powered by recent advances in sensor fabrication, has greatly increased the complexity of IoT network architecture. As the market for these sensors grows, so does the problem of ensuring that IoT networks meet high requirements for network availability, dependability, flexibility, and scalability. Unlike traditional networks, IoT systems must be able to handle massive amounts of data generated by various and frequently-used resource-constrained devices, while ensuring efficient and dependable communication. This puts high constraints on the design of IoT, mainly in terms of the required network availability, reliability, flexibility, and scalability. To this end, this work considers deploying a recent technology of distributed edge computing to enable IoT applications over dense networks with the announced requirements. The proposed network depends on distributed edge computing at two levels: multiple access edge computing and fog computing. The proposed structure increases network scalability, availability, reliability, and scalability. The network model and the energy model of the distributed nodes are introduced. An energy-offloading method is considered to manage IoT data over the network energy, efficiently. The developed network was evaluated using a developed IoT testbed. Heterogeneous evaluation scenarios and metrics were considered. The proposed model achieved a higher energy efficiency by 19%, resource utilization by 54%, latency efficiency by 86%, and reduced network congestion by

关键词： distributed computing IoT fog computing resource utilization multiple access edge computing reliability scalability dense networks

来源：评论

学校读者我要写书评

暂无评论

Latency reduction with compression-aware training for efficient distributed computing of Convolution Neural Networks

Latency reduction with compression-aware training for effici...

引用

作者： Xiangzhou Sun The Webb Schools

To decrease workload on lightweight devices, this project accelerates the computation of Convolution Neural Networks(CNNs) and preserves accuracy through modifying the CNNs' training process. First, this research implements distributed computing to optimally divide the network workload onto both devices and the cloud. To reduce communication latency between devices and the cloud, this research introduces feature pruning by setting elements in the communicated feature to 0. However, naively pruning the feature causes a significant accuracy drop. To compensate for this limitation, this research applies pruning-aware training to preserve the CNNs' task performance. This research evaluates the proposed methods on multiple datasets and CNN models, like VGG-11 and ResNet-18 with PyTorch. Empirical results demonstrate that the methods can reduce the computational latency by 50-75% with a negligible 1% accuracy ***, this research first identifies the system bottleneck by comparing on-device, oncloud, and communication latencies(on-device: 14.8%, on-cloud: 1.7%, communication:83.5%). Then, this research compares multiple pruning strategies and observe the superiority of magnitude-based pruning. At 0.992 sparsity, magnitude-based pruning outperforms other strategies by 45% in accuracy. Finally, this research verifies the effectiveness of the proposed pruning-aware training method by comparing it with the baseline at various splitting points and networks. Pruning-aware training decreases the accuracy loss by up to 26% at 0.998 sparsity. In conclusion, even though distributed computing accelerates applications on lightweight devices,compressing the communication cost is crucial and challenging. This research proposed methods effectively reduce communication latency without sacrificing accuracy, conserving the effectiveness of CNN.

关键词： Convolution Neural Network (CNN) distributed computing Pruning Compression-aware Training

来源：评论

学校读者我要写书评

暂无评论

Secure distributed computing in Cloud Using Trusted Execution Environments

Secure Distributed Computing in Cloud Using Trusted Executio...

引用

Telecommunications Forum (TELFOR)

作者： Teodora Radaljac Danko Miladinović Žarko Stanisavljević Pavle Vuletić School of Electrical Engineering University of Belgrade Belgrade Serbia

ISBN: (数字)9798350391060

ISBN: (纸本)9798350391077

This paper investigates the impact of integrating AMD Secure Encrypted Virtualization (SEV) technology into a distributed computing environment. distributed computing frameworks are designed to handle large-scale data processing and complex computational tasks by distributing workloads across multiple nodes. However, ensuring the security and integrity of data in such environments, particularly in multi-tenant and cloud settings, poses a significant challenge. Confidential computing concepts and in particular AMD SEV address these concerns by providing virtual machine memory encryption and isolation, even protecting against threats from the hypervisor. This study evaluates the potential to set up distributed computing in the confidential computing settings. It will discuss current gaps and the performance overhead of using AMD SEV in distributed systems, focusing on task execution times and scalability. Experiments were conducted using different workloads studying performance impact. Results indicate that while SEV introduces additional data processing, the overall average performance impact is minimal, making it a viable solution for secure distributed processing without significant performance degradation.

关键词： Cloud computing Virtual machine monitors Processor scheduling Scalability Data processing Virtual machines Time measurement Encryption distributed computing Standards

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：