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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

共 149579 条记录，以下是181-190 订阅

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Edge-assisted Adaptive Configuration for Serverless-based Video Analytics 43

Edge-assisted Adaptive Configuration for Serverless-based Vi...

引用

43rd ieee international conference on distributed computing systems (ICDCS)

作者： Wang, Ziyi Zhang, Songyu Cheng, Jing Wu, Zhixiong Cao, Zhen Cui, Yong Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Huawei Technol Comp Network & Protocol Lab Shenyang Peoples R China

ISBN: (纸本)9798350339864

The growth of video volumes and increased DNN capabilities have led to a growing desire for video analytics, which demands intensive computation resources. Traditional resource provisioning strategies, such as configuring a cluster per peak utilization, lead to low resource efficiency. Serverless computing is a promising way to avoid wasteful resource provisioning since video analytics regularly encounters bursty input workloads and fine-grained video content dynamics. For serverless-based video analytics, the application configuration (frame rate, detection model, and computation resources) will impact several metrics, such as computation cost and analytics accuracy. In this paper, we investigate the joint configuration adjustment problem for video knobs and computation resources provided by the serverless platform. We propose an algorithm that can efficiently adapt configurations for video streams to address two key challenges in serverless-based video analytics systems, including the complex relationships between the configurations and the key performance metrics, and the dynamically best configuration. Our algorithm is developed based on Markov approximation to minimize the computation cost within an accuracy constraint. We have developed a prototype over AWS Lambda and conducted extensive experiments with real-world video streams. The results show that our algorithm can greatly reduce the computation cost under the constraint of target accuracy.

关键词： Video analytics Edge computing Serverless computing Deep neural network

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distributed Quantum computing for Chemical Applications 5

Distributed Quantum Computing for Chemical Applications

引用

2024 international conference on Quantum computing and Engineering

作者： Jones, Grier M. Jacobsen, Hans-Arno Univ Toronto Dept Chem & Phys Sci Toronto ON Canada Univ Toronto Edward S Rogers Sr Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9798331541378

In recent years, interest in quantum computing has increased due to technological advances in quantum hardware and algorithms. Despite the promises of quantum advantage, the applicability of quantum devices has been limited to few qubits on hardware that experiences decoherence due to noise. One proposed method to get around this challenge is distributed quantum computing (DQC). Like classical distributed computing, DQC aims at increasing compute power by spreading the compute processes across many devices, with the goal to minimize the noise and circuit depth required by quantum devices. In this paper, we cover the fundamental concepts of DQC and provide insight into where the field of DQC stands with respect to the field of chemistry a field which can potentially be used to demonstrate quantum advantage on noisy-intermediate scale quantum devices.

关键词： distributed systems quantum distributed systems quantum computing chemistry

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Communication-Efficient Topology Orchestration for distributed Learning in UAV Networks 20

Communication-Efficient Topology Orchestration for Distribut...

引用

20th ieee international Wireless Communications and Mobile computing (ieee IWCMC) - Green and Intelligent Communications

作者： Liang, Zixuan Lyu, Xinchen Ren, Chenshan Li, Na Li, Kai Beijing Univ Posts & Telecommun Beijing Peoples R China Minzu Univ China Beijing Peoples R China Univ Cambridge Cambridge England CISTER Portugal Alcobaca Portugal

ISBN: (纸本)9798350361261;9798350361278

distributed learning is a promising paradigm for future UAV (unmanned aerial vehicle) networks networks and other emerging autonomous unmanned systems. Such distributed learning framework can suit the intrisic decentralized topology of UAV networks, where the UAVs can collaborate to train a global AI model by only exchanging the model parmeters via its peer-to-peer (i.e, inter-UAV) links in a distributed manner. However, with the ever-increasing AI model sizes, the challenges arise from the significant communication overhead for exchanging massive model weights via inter-UAV links in an ad-hoc manner. Previous communication-efficient techniques are mainly designed for conventional federated learning and not easily extendable to the decentralized counterpart. We propose selective link orchestration to minimize communication overhead while ensuring convergence of distributed learning, and prove that the convergence constraint is equivalent to the connectivity of the selected sub-graph. As such, we can reformulate the problem as a link selection problem in graph theory and develop a distributed optimization algorithm based on the modification of the Gallager, Humblet, and Spira's algorithm. Experimental results on MNIST, Fashion-MNIST, and CIFAR-10 datasets demonstrate up to a 90% reduction in communication overhead without compromising model accuracy.

关键词： UAV distributed learning communication efficiency selective link orchestration

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Exploring Use of Symbolic Execution for Service Analysis 54

Exploring Use of Symbolic Execution for Service Analysis

引用

54th Annual ieee/IFIP international conference on Dependable systems and Networks (DSN)

作者： Zhang, Yang Wang, Long Wang, Zhengang Shangguan, Dongdong Tsinghua Univ Inst Network Sci & Cyberspace Reasons Lab Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China Huawei Corp Shenzhen Peoples R China

ISBN: (纸本)9798350395716;9798350395709

Service computing is growing popular thanks to the cloud computing technology. However, the behavior of cloud services is complicated as they involve a large number of components and complex interactions between them. It is highly helpful for service users and providers to well understand the service behavior if they can exhaustively explore potential component interactions and service scenarios. This paper discusses the challenges associated with this and an approach to achieving this goal. This paper also reports our progress of exploring the use of symbolic execution for service analysis, together with our research plan.

关键词： distributed service symbolic execution system analysis code representation

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Analyzing the Effect of Taproot on Bitcoin Deanonymization 43

Analyzing the Effect of Taproot on Bitcoin Deanonymization

引用

43rd ieee international conference on distributed computing systems (ICDCS)

作者： Kleinwort, Felix Posdorfer, Wolf Edinger, Janick Univ Hamburg Dept Comp Sci Distributed Operating Syst Hamburg Germany Univ Hamburg Dept Comp Sci Comp Networks Hamburg Germany

ISBN: (纸本)9798350328127

Bitcoin was released in 2008 as an electronic peer-to-peer payment system. The aim was to enable financial transactions in an anonymous manner between participating parties. However, Bitcoin is considered a pseudonymous network rather than anonymous, as the identity of the address owner is unknown, but every transaction is permanently stored on the Bitcoin blockchain and can be tracked by anyone. The structure of the blockchain, its transactions, and the Bitcoin network make it possible to deanonymize pseudonyms through various methods such as flow analysis, heuristics, and network traffic observations. Once a connection is established between pseudonyms used in the Bitcoin network and the real world, all previous transactions can be attributed to that identity. In 2021, Taproot was introduced to further increase privacy within the Bitcoin Network by introducing a new address format which will allow transactions to be more indistinguishable from one another. In this paper, we analyze current methods for deanonymizing Bitcoin transactions to understand which parts of the Bitcoin protocol they exploit. In addition, we look at the changes introduced by Taproot and determine the extent to which these changes affect the methods and what assumptions must be made for these methods to remain applicable.

关键词： Taproot Privacy Anonymity Deanonymization Blockchain Bitcoin

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Migration of Isolated Application Across Heterogeneous Edge systems 8

Migration of Isolated Application Across Heterogeneous Edge ...

引用

8th ieee international conference on Fog and Edge computing (ICFEC)

作者： Kreutzer, Marius Scidler, Maximilian Dudzik, Konstantin Betancourt, Victor Pazmino Becker, Jurgen FZI Res Ctr Informat Technol Karlsruhe Germany Siemens AG Technol Erlangen Germany

ISBN: (纸本)9798350361360;9798350361353

distributed computing capabilities at the network's edge enable new use cases, e.g., smart factories, industrial internet of things, or autonomous mobility systems. While new applications evolve, managing the resources and being capable of integrating and adjusting the execution of tasks in a distributed edge infrastructure is of great importance. For this, applications need to be migrated between different nodes. These migrations must happen without interruption, allowing the system to meet service requirements while fully utilizing all available hardware resources. Therefore, applications should also be executable on all different compute nodes in a heterogeneous edge system without interfering with each other. To this end, applications should be granted only necessary permission, especially when un-trusted applications are integrated into the system. We, therefore, propose a migration method for isolated applications across heterogeneous compute nodes in service-oriented edge architectures. A service-oriented architecture is used to decouple applications, allowing for flexible scheduling. The migration method enables the fast migration of sandboxed applications based on WebAssembly by utilizing a two-stage migration approach. The concept can utilize multiple communication protocols for management and service communication. We have implemented a proof of concept based on the Zenoh(1) communication protocol. While the time required depends on the communication protocol and the memory size, we achieved migration delays of under 51 milliseconds for smaller applications. By providing a method for fast migration for applications across heterogeneous compute nodes, it is possible for distributed edge infrastructures to run applications independently from each other and adjust the execution node based on changes in the system's environment. By integrating applications into our framework, they are executed isolated and with strict access control, allowing for easier reuse

关键词： Edge computing Migration Heterogeneous System Runtime

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A Study on the Performance of distributed Storage systems in Edge computing Environments 15

A Study on the Performance of Distributed Storage Systems in...

引用

15th ieee international conference on Joint Cloud computing (JCC)

作者： Makris, Antonios Kontopoulos, Ioannis Xyalis, Stylianos Nektarios Psomakelis, Evangelos Theodoropoulos, Theodoros Varvarigos, Andreas Tserpes, Konstantinos Harokopio Univ Athens Dept Informat & Telemat Athens Greece Natl Tech Univ Athens Sch Elect & Comp Engn Athens Greece

ISBN: (纸本)9798350387339

Edge computing presents a promising paradigm for the management and processing of the vast volumes of data generated by Internet of Things (IoT) devices. By merging cloud services with decentralized processing at the edge of the network, edge computing optimizes resource utilization while mitigating communication overhead and data transfer delays. Despite advancements, there are issues regarding cloud/edge-based application requirements. A distributed edge storage solution is crucial, ensuring data proximity, minimizing network congestion, and adapting to changing demands. Nevertheless, implementing or selecting an efficient edge-enabled storage system presents numerous challenges due to the distributed and heterogeneous nature of the edge, as well as its limited resource capabilities. Hence, it is essential for the research community to actively contribute towards clarifying the objectives and delineating the strengths and weaknesses of different storage solutions. This work presents an overview and performance analysis of three storage solutions in the edge computing context, namely MinIO, IPFS, and BigchainDB. The evaluation considers a set of Quality of Service (QoS) and resource utilization metrics. The systems are deployed on a cluster of four Raspberry Pis, which function as a network of edge devices. The results demonstrate the superiority of IPFS and provide insights into the performance of the evaluated storage systems for edge deployments.

关键词： edge storage solutions performance evaluation minio ipfs bigchaindb distributed databases

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Proceedings of 2022 8th ieee international conference on Cloud computing and Intelligence systems, CCIS 2022

Proceedings of 2022 8th IEEE International Conference on Clo...

引用

8th ieee international conference on Cloud computing and Intelligence systems, CCIS 2022

ISBN: (纸本)9781665477352

The proceedings contain 124 papers. The topics discussed include: decoupling observer for contact force estimation of robot manipulators based on enhanced gaussian process model;diagonal region division-based fly neural network on omnidrectional collision detection;minimizing the average job completion time for acceleration systems in cloud computing;incremental updating multigranulation approximations with matrix representation under coarsening decision attribute;guard: multigranularity-based unsupervised anomaly detection algorithm for multivariate time series;dependency and association enhanced finding similar exercises in online education system;dialogue-enriched knowledge point recommendation for consultation task;efficient esophageal lesion detection using polarization regularized network slimming;self-regulation and supervision: a study on online privacy protection of Chinese and American children in mobile apps;and hierarchical strategy for sequence-based visual place recognition.

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distributed Parallelized Password Cracking Framework for Enhanced Cybersecurity using Authentication Codes 3

Distributed Parallelized Password Cracking Framework for Enh...

引用

international conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Balasubramaniam, N. Sweetlin, Dhalia J. Anirudh, R. Anna Univ Dept Informat Technol Chennai India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

An innovative approach to password cracking by leveraging a distributed computing model is developed. The sys- tem comprises a client-server architecture where clients receive segmented password ranges for parallelized hashing attempts. The Java-based implementation employs MD5 hashing and divides the password into smaller units for distribution among multiple clients. Each client independently processes its assigned password range, attempting to match the hash against a pre- determined target. The server orchestrates the distribution of password segments and collects results from clients, facilitating the cracking process. Security measures include secure communication protocols, and ethical considerations center around legality, user consent, and emphasizing the educational value of responsible hacking practices. The work explores the tech- nical challenges of distributed password cracking, addressing efficiency, scalability, and security implications, while fostering a deeper understanding of cybersecurity and distributed systems.

关键词： MD5 SHA256 password cracking distributed systems parallel computing

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Elastic DNN Inference with Unpredictable Exit in Edge computing 43

Elastic DNN Inference with Unpredictable Exit in Edge Comput...

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43rd ieee international conference on distributed computing systems (ICDCS)

作者： Huang, Jiaming Gao, Yi Dong, Wei Zhejiang Univ Coll Comp Sci Hangzhou Peoples R China

ISBN: (纸本)9798350339864

Multi-exit neural networks have recently boomed in edge computing to maximize the computing power of different devices. However, many real-time tasks running on edge computing applications have encountered unpredictable exiting frequently due to system power outages, high-priority preemption, etc., which have been overlooked by multi-exit models until now. To tackle this issue, it is critical to decide at which branch the multi-exit model exits so that the unpredictable exit will always come with desirable results. In this paper, we propose EINet, a sample-wise planner of real-time multi-exit deep neural networks, which achieves efficient Elastic Inference with unpredictable exit while guaranteeing best-effort accuracy on different edge platforms. Therefore, a given trained deep neural network is first partitioned into multiple blocks with one exit each by EINet. Then EINet obtains the block-wise model profiles, including the block-wise accuracy and inference time. Using the model profiles, EINet is able to dynamically determine which exits to take during the inference task for each sample. We introduce Confidence Score Predictors to dynamically adapt the uniqueness of the input samples, and the Search Engine to efficiently find the near-optimal plan during the elastic inference. EINet is evaluated extensively using multiple DNNs and datasets with unpredictable exits. Results show that EINet can achieve the highest average accuracy compared with multiple baselines.

关键词： Multi-exit unpredictable exit elastic inference real-time DNN task edge computing

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