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检索条件"任意字段=Proceedings of the 22nd International Conference on Distributed Computing and Networking"

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DNNEmu: A Lightweight Performance Emulator for distributed DNN Training 22nd

DNNEmu: A Lightweight Performance Emulator for Distributed ...

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22nd international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Wang, Jian Yu, Enda Dong, Dezun Pang, Zhengbin College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783031226762

distributed deep learning system usually leverages large-scale GPU clusters to speed up training. Therefore, for organizations lacking GPU resources temporarily, it is difficult to evaluate distributed communication optimization. Specifically, due to the complex interaction between computation and communication, accurately predicting the performance of distributed training is an almost impossible task. In this paper, we propose DNNEmu, a lightweight performance emulator for distributed DNN training to help ML developers design and optimize the communication strategies before target machines are available. Based on our key observation that the computation overhead could be predicted by input features and replaced by simulation programs, we use CPUs to emulate the communication and computation of distributed training. DNNEmu makes layer-wise predictions based on profiling traces collected by the layer-wise profiler, and emulates DNN training by simulating computation. DNNEmu contains three integrated modules: (i) the layer-wise profiler profiling operators with layer names;(ii) the performance predictor predicting the layer-wise operator runtime;and (iii) the computation simulator simulating the actual computation with the predicted runtime. DNNEmu is implemented over MXNet and requires no changes to the developers’ models or inputs. We show that DNNEmu can emulate distributed training with an average estimation error of 7.2 %. © 2023, Springer Nature Switzerland AG.

关键词： Forecasting

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Scalable Federated Unlearning via Isolated and Coded Sharding 33

Scalable Federated Unlearning via Isolated and Coded Shardin...

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33rd international Joint conference on Artificial Intelligence (IJCAI)

作者： Lin, Yijing Gao, Zhipeng Duo, Hongyang Niyato, Dusit Gui, Gui Cui, Shuguang Ren, Jinke Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Chinese Univ Hong Kong Shenzhen Future Network Intelligence Inst Shenzhen Guangdong Peoples R China Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Shenzhen Guangdong Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore Singapore Cent South Univ Sch Automat Changsha Hunan Peoples R China

ISBN: (纸本)9781956792041

Federated unlearning has emerged as a promising paradigm to erase the client-level data effect without affecting the performance of collaborative learning models. However, the federated unlearning process often introduces extensive storage overhead and consumes substantial computational resources, thus hindering its implementation in practice. To address this issue, this paper proposes a scalable federated unlearning framework based on isolated sharding and coded computing. We first divide distributed clients into multiple isolated shards across stages to reduce the number of clients being affected. Then, to reduce the storage overhead of the central server, we develop a coded computing mechanism by compressing the model parameters across different shards. In addition, we provide the theoretical analysis of time efficiency and storage effectiveness for the isolated and coded sharding. Finally, extensive experiments on two typical learning tasks, i.e., classification and generation, demonstrate that our proposed framework can achieve better performance than three state-of-the-art frameworks in terms of accuracy, retraining time, storage overhead, and F1 scores for resisting membership inference attacks.

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Statistical analysis of execution time profile for temporal validation of a distributed hard real-time system 22

Statistical analysis of execution time profile for temporal ...

引用

22nd IEEE international conference on Industrial Technology (ICIT)

作者： Prabhakara, Arpitha Steinwender, Benjamin Elmenreich, Wilfried Kompetenzzentrum Automobil & Ind Elekt GmbH Villach Austria Alpen Adria Univ Networked & Embedded Syst Klagenfurt Austria

ISBN: (纸本)9781728157306

This paper addresses modelling and assessing of worst-case execution times of tasks in a distributed real-time system. The presented case study is based on a stress test system for power semiconductors. Typical distributed systems involve inherent inter-dependencies that have to be handled with minimized delays. Studying the computing task's timing behavior becomes necessary to understand the timing violations. Modelling the temporal behaviour of the system using probabilistic Worst-Case Execution Time (WCET) analysis aids to overcome the timing faults. This paper focuses on (1) the study of the temporal behaviour through the execution time profile of the distributed hard realtime system;(2) the statistical analysis by performing probability distribution modelling on the measured data. Measurement-based probabilistic timing analysis is an emerging and reliable method used to arrive at industry quality estimates. This method is used here in the paper to carry out temporal validation of the real-time computing tasks on our case study.

关键词： Real-Time distributed System Timing Failure WCET Analysis Measurement-based Probabilistic Timing Analysis Statistical Analysis Probability Distribution Fault-Tolerant System

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Efficient Federated Learning with Adaptive Channel Pruning for Edge Devices 19

Efficient Federated Learning with Adaptive Channel Pruning f...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Jia, Yongzhe Zhang, Xuyun Liu, Bowen Dou, Wanchun Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing China Shenzhen China Macquarie University Department of Computing SydneyNSW Australia Shiga University Faculty of Data Science Hikone Japan

ISBN: (纸本)9798350346558

Federated learning (FL) is an emerging machine learning paradigm that allows distributed participants to train a global model collaboratively. In edge networks, FL involves numerous edge devices updating the local models with the assistance of a central server while keeping the local data on the devices. However, edge devices are commonly resource-limited while neural network models used in FL require significant resource consumption to reach a satisfactory accuracy. Moreover, different devices are various in terms of system capabilities, and employing a uniform model on all devices leads to the derogation of model accuracy. Several pioneer work integrate model pruning techniques into the FL process and focus on reducing the resource consumption on edge devices, whereas neglecting the impact of model generality on accuracy. In our work, we propose FedACP, an efficient FL approach with adaptive channel pruning for edge devices to achieve better resource-accuracy trade-offs. Specifically, we design a two-phase channel pruning approach to adaptively prune local models, in which both the resource constraints and model generalities are taken into consideration. In addition, we also design an aggregation algorithm for aggregating heterogeneous local models produced by the two-phase channel pruning approach. To evaluate the performance of the FedACP, we implement it with a real FL framework FedML and compare it with several state-of-the-art methods. The experimental results show that FedACP achieves better model accuracy while simultaneously reducing 22.41% to 27.34% model parameters. © 2022 IEEE.

关键词： Economic and social effects

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distributed Fair k-Center Clustering Problems with Outliers 22nd

Distributed Fair k-Center Clustering Problems with Outliers

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22nd international conference on Parallel and distributed computing, Applications and Technologies (PDCAT 2021)

作者： Yuan, Fan Diao, Luhong Du, Donglei Liu, Lei Beijing Univ Technol Dept Operat Res & Informat Engn Beijing 100124 Peoples R China Univ New Brunswick Fac Management Fredericton NB E3B 5A3 Canada

ISBN: (纸本)9783030967727;9783030967710

Big data clustering is a fundamental problem with a vast number of applications. Due to the increasing size of data, interests in clustering problems in distributed computation models have increased. On the other hand, because important decision making is being automated with the help of algorithms, therefore, fairness in algorithms has become an especially important research topic. In this work, we design new distributed algorithms for the fair k-center problem with outliers. Our main contributions are: (1) In the fair k-center problem with outliers setting we give a 4-approximation ratio algorithm. (2) In the distributed fair k-center problem with outliers setting we give a 18-approximation ratio algorithm.

关键词： Clustering problem Approximate algorithm Fair k-center problem with outliers distributed fair k-center problem with outliers

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P4Fuzz: Compiler Fuzzer for Dependable Programmable Dataplanes 21

P4Fuzz: Compiler Fuzzer for Dependable Programmable Dataplan...

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22nd international conference on distributed computing and networking (ICDCN)

作者： Agape, Andrei Alexandru Danceanu, Madalin Claudiu Hansen, Rene Rydhof Schmid, Stefan Aalborg Univ Aalborg Denmark Univ Vienna Vienna Austria

ISBN: (纸本)9781450389334

Emerging software-defined networks and programmable dataplanes promise to render communication networks more dependable, overcoming today's manual and error-prone approach to operate networks. Indeed, programmable dataplanes such as P4 provide great opportunities for improving network performance and developing innovative security features, by allowing programmers to reconfigure and tailor switches towards their needs. However, extending programmability to the dataplane also introduces new threat models. In this paper, using a systematic security analysis, we identify a particularly worrisome vulnerability: the automated program compilers which lie at the core of programmable dataplanes. The dataplane compilers introduce a risk of persistent threats which are covert and hard to detect, and may be exploited for large-scale attacks, affecting many devices. Our main contribution is P4Fuzz, a compiler fuzzer to find bugs and vulnerabilities in P4 compilers, in an efficient and automated manner. We discuss the challenges involved in designing such a compiler fuzzer for P4, present our fuzzing and taming algorithms, and report on experiments with our prototype implementation, considering the standard compilers of BMv2, eBPF, and NetFPGA. Our experiments confirm that P4Fuzz is able to generate and test the validity of dozens of P4 programs per minute. Using P4Fuzz, we also successfully found several bugs which have been acknowledged and fixed by the community.

关键词： software defined networking p4 compiler fuzzing

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Counting Mobile Devices With Physical Layer Measurements in the 2.4GHz ISM Band 19

Counting Mobile Devices With Physical Layer Measurements in ...

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作者： Zhang, Zhaoyan Huang, Baoqi Zhang, Chaowei Jia, Bing Li, Wuyungerile Xu, Gang Engineering Research Center of Ecological Big Data Ministry of Education China Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Hohhot China Inner Mongolia University College of Computer Science Hohhot China Inner Mongolia Power Marketing Service and Operation Management Center China

ISBN: (纸本)9798350346558

In general, the more mobile devices are operating in space, the more wireless communications are produced, leading to significant changes in the wireless spectrum, which can be utilized to infer the number of mobile devices conversely. Inspired by this idea, we pioneered present a novel mobile device counting method of measuring the wireless signals of the 2. 4GHz ISM band in which most WiFi-and Bluetooth-enabled mobile devices operate. Precisely, the in-phase (I) and quadrature (Q) components from the wireless signals are measured through a software-defined radio (SDR) platform and are then converted into spectrograms. On this ground, we propose a dual attention network based on EfficientNet-B4 (EANet) to estimate the number of mobile devices with the aforementioned spectrograms as inputs. In addition, a cross-domain estimation method is proposed to enhance the generalizability of our model. Experiments are conducted in real environments to validate our method. It is shown that the proposed regression model EANet outperforms the models based on popular convolutional neural networks (CNNs), and the proposed cross-domain method can effectively reduce the training time without sacrificing the estimation accuracy. This study preliminarily verifies the idea's feasibility and the validity of the proposed methods, paving the way for future research. © 2022 IEEE.

关键词： Network layers

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Message from conference Chairs

Message from Conference Chairs

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ACIS international conference on Software Engineering, Artificial Intelligence, networking, and Parallel/distributed computing (SNPD)

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

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Dyncast: Use Dynamic Anycast to Facilitate Service Semantics Embedded in IP address 22

Dyncast: Use Dynamic Anycast to Facilitate Service Semantics...

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IEEE 22nd international conference on High Performance Switching and Routing (IEEE HPSR)

作者： Li, Yizhou Han, Zifa Gu, Shuheng Zhuang, Guanhua Li, Feng Huawei Technol Network Technol Lab Nanjing Peoples R China

ISBN: (纸本)9781665440059

Edge computing services are deployed at edge sites that are closer to users compare to the central cloud. There are quite a number of edge sites in a city hosting those service instances. The user's request is normally served by the geographically closest one in order to get faster response. However the shortest distance does not necessarily mean the lowest latency from the user's experience. With increasing number of edge sites, computing capacity and load, and network path status rather than geographical location, are playing key roles in determining the best edge site or service instance to handle the user's request to achieve the optimal overall load balance and user experience. In this paper, we propose a dynamic anycast (Dyncast) networking architecture to optimally route the computing request to the most appropriate service instance by considering the real time computing loads and the network status simultaneously. The field testbed experiments demonstrate the effectiveness of the proposed dyncast architecture. Dyncast shows 9.5% to 159.9% improvement in terms of job completion time (JCT) over traditional scheduling strategy under different computing load and network status scenarios.

关键词： dyncast edge computing anycast routing service metrics

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Conditional Recurrent Neural Networks for Enhancing Throughput Prediction and Slow File Transfers Detection in Large Science Workflows

Conditional Recurrent Neural Networks for Enhancing Throughp...

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Consumer Communications and networking conference, CCNC IEEE

作者： Boyu Fan Alex Sim Kesheng Wu Jinoh Kim University of California Berkeley Lawrence Berkeley National Laboratory Energy Sciences Network Texas A&M University-Commerce

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Efficient data transfer across scientific computing facilities is critical for enabling timely scientific discoveries. In this work, we explore the options of anticipating extremely slow data transfers to enable preventive actions. However, the dynamic nature of the large distributed scientific workflows driving these data transfers presents significant challenges for predicting network throughput. This study introduces a Conditional Recurrent Neural Network (CondRNN) model, specifically utilizing Conditional Long Short-Term Memory (CondLSTM), to integrate both static and dynamic features for enhanced throughput prediction. By leveraging historical transfers as proxy features, more than 60% of predictions achieved an absolute percentage error (APE) of less than 20%, and slow transfers were detected with a precision of 91.7% and recall of 100%, outperforming traditional RNN models. Implementing CondLSTM in scientific computing environments can optimize network resource utilization, ensuring efficient data transmission, thereby supporting the continuous progression of scientific research.

关键词： Recurrent neural networks Accuracy Scientific computing Computational modeling Predictive models Throughput Data transfer Data models Resource management Long short term memory

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