2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)最新文献_第3页

Persival: Simulating Complex 3D Meshes on Resource-Constrained Mobile AR Devices Using Interpolation 持久性:在资源受限的移动AR设备上使用插值模拟复杂的3D网格

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00097

Johannes Kässinger, D. Rosin, Frank Dürr, Niklas Hornischer, O. Röhrle, K. Rothermel

{"title":"Persival: Simulating Complex 3D Meshes on Resource-Constrained Mobile AR Devices Using Interpolation","authors":"Johannes Kässinger, D. Rosin, Frank Dürr, Niklas Hornischer, O. Röhrle, K. Rothermel","doi":"10.1109/ICDCS54860.2022.00097","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00097","url":null,"abstract":"Simulations are an important part of analyzing and understanding systems, including not only technical but also bio-mechanical subjects such as the musculoskeletal apparatus of the human body. Detailed, biophysical simulations are complex and require a substantial amount of computational resources. With the advent of mobile AR devices such as the Microsoft HoloLens, new challenges arise to run or represent the results of such complex simulations on resource-constrained devices. In this paper we propose a deep-learning-based mobile simulation approach for the contraction of a human muscle model on an AR device (MS HoloLens 2). To elaborate, we present a two-step workflow consisting of simulating the deformation of the 3D geometry of the biceps, of which a subset of points can be interpolated back to full resolution. This allows to either offload the full simulation, just communicating the subset of nodal points, or to use a lower-quality local simulation restricted to the subset. Interpolation is done locally in both cases. The interpolation model consists of a dense, single hidden layer neural network. A mesh simplification method is combined with a genetic algorithm to determine the optimal subset of mesh nodes to interpolate from. In purely local execution, our simulation and interpolation model is able to accurately predict the position of 2809 nodal points based on as few as 30, while using 97.78 % less energy and evaluating up to 1.23 times faster compared to the local reference model. In an ideal distributed scenario energy consumption decreases by 99 % and evaluation time is up to 32.42 times faster. For the latter, it also reduces communication-data to 1.2 % of the full resolution mesh.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129165398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Lightweight and Secure Vehicular Edge Computing Framework for V2X Services 面向V2X服务的轻型安全车辆边缘计算框架

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00146

Ramneek, Sangheon Pack

引用次数: 1

Joint Optimization of Energy Consumption and Completion Time in Federated Learning 联邦学习中能量消耗与完成时间的联合优化

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00101

Xinyu Zhou, Jun Zhao, Huimei Han, C. Guet

{"title":"Joint Optimization of Energy Consumption and Completion Time in Federated Learning","authors":"Xinyu Zhou, Jun Zhao, Huimei Han, C. Guet","doi":"10.1109/ICDCS54860.2022.00101","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00101","url":null,"abstract":"Federated Learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics. To balance the trade-off between energy and execution latency, and thus accommodate different demands and application scenarios, we formulate an optimization problem to minimize a weighted sum of total energy consumption and completion time through two weight parameters. The optimization variables include bandwidth, transmission power and CPU frequency of each device in the FL system, where all devices are linked to a base station and train a global model collaboratively. Through decomposing the non-convex optimization problem into two subproblems, we devise a resource allocation algorithm to determine the bandwidth allocation, transmission power, and CPU frequency for each participating device. We further present the convergence analysis and computational complexity of the proposed algorithm. Numerical results show that our proposed algorithm not only has better performance at different weight parameters (i.e., different demands) but also outperforms the state of the art.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132617936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Vehicle-Key: A Secret Key Establishment Scheme for LoRa-enabled IoV Communications 车辆密钥:用于支持lora的车联网通信的秘密密钥建立方案

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00081

Huanqi Yang, Hongzhi Liu, Chengwen Luo, Yuezhong Wu, Wei Li, Albert Y. Zomaya, Linqi Song, Weitao Xu

{"title":"Vehicle-Key: A Secret Key Establishment Scheme for LoRa-enabled IoV Communications","authors":"Huanqi Yang, Hongzhi Liu, Chengwen Luo, Yuezhong Wu, Wei Li, Albert Y. Zomaya, Linqi Song, Weitao Xu","doi":"10.1109/ICDCS54860.2022.00081","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00081","url":null,"abstract":"Recent years have witnessed the remarkable growth of the Internet of Vehicles (IoV). Due to the high dynamics and ad-hoc nature of IoV communication, the lack of effective secret key establishment in IoV remains a security bottleneck. Physical layer key generation has emerged as a promising technology to establish a pair of cryptographic keys in a lightweight and information-theoretic secure way. However, prior works mainly focus on legacy communication technologies such as Wi-Fi, ZigBee, and 5G which can only achieve short range IoV communications. The emergence of Long-range (LoRa) communication technology that features long-range, low power, and extremely low data rate, brings new challenges for key generation in long range IoV scenarios. In this paper, we present Vehicle-Key, which is a secret key generation system to secure LoRa-enabled IoV communications. In Vehicle-Key, we design a novel deep learning model that can achieve channel prediction and quantization simultaneously. Additionally, we propose an autoencoder-based reconciliation method that improves the key agreement rate significantly. Extensive real-world experiments show that Vehicle-Key improves the key agreement rate by 15.10%–49.81% and key generation rate by 9–14× compared with the state-of-the-art. Security analysis demonstrates that Vehicle-Key is secure against several common attacks. Moreover, we implement Vehicle-Key on a Raspberry Pi and show that it can be executed in 3.4 ms.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133016818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Poster: A Dynamic Task Scheduling using Multi-Platoon Architecture in Vehicular Networks 海报:车辆网络中基于多排架构的动态任务调度

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00135

Tingting Xiao, Chen Chen, Qingqi Pei, Shaohua Wan

引用次数: 0

Neurotrie: Deep Reinforcement Learning-based Fast Software IPv6 Lookup Neurotrie:基于深度强化学习的快速软件IPv6查找

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00093

Hao Chen, Yuan Yang, Mingwei Xu, Yuxuan Zhang, Chenyi Liu

{"title":"Neurotrie: Deep Reinforcement Learning-based Fast Software IPv6 Lookup","authors":"Hao Chen, Yuan Yang, Mingwei Xu, Yuxuan Zhang, Chenyi Liu","doi":"10.1109/ICDCS54860.2022.00093","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00093","url":null,"abstract":"IPv6 has shown notable growth in recent years, imposing the need for high-speed IPv6 lookup. As the forwarding rate of virtual switches continues increasing, software-based IPv6 lookup without using special hardware such as TCAM, GPU, and FPGA is of academic interest and industrial importance. Existing studies achieve fast software IPv4 lookup by reducing the operation number, as well as reducing the memory footprint so as to benefit from CPU cache. However, in the situation of 128-bit IPv6 addresses, it is challenging to keep both operation numbers and memory footprints small. To address the issue, we propose the Neurotrie data structure, which supports fast lookup and arbitrary strides. Thus, a good balance can be made between trie depth and memory footprint by computing the proper stride for each Neurotrie node. We model the optimal Neurotrie problem which minimizes the depth with limited memory footprint and develop a pseudo-polynomial time baseline algorithm to construct Neurotrie using dynamic programming. To improve the performance and reduce the computation complexity, we develop a deep reinforcement learning-based approach, which leverages a deep neural network to construct Neurotrie efficiently, based on characteristics captured from real IPv6 prefixes. We further refine the data structure and develop an efficient mechanism for routing updates. Experiments on real routing tables show that Neurotrie achieves a lookup rate 34% higher than that of state-of-the-art approaches.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131458104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Mobility-aware Seamless Virtual Function Migration in Deviceless Edge Computing Environments 无设备边缘计算环境中移动感知的无缝虚拟功能迁移

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00050

Yaodong Huang, Zelin Lin, Tingting Yao, Xiaojun Shang, Laizhong Cui, J. Huang

{"title":"Mobility-aware Seamless Virtual Function Migration in Deviceless Edge Computing Environments","authors":"Yaodong Huang, Zelin Lin, Tingting Yao, Xiaojun Shang, Laizhong Cui, J. Huang","doi":"10.1109/ICDCS54860.2022.00050","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00050","url":null,"abstract":"Serverless Computing and Function-as-a-Service (FaaS) offer convenient and transparent services to developers and users. The deployment and resource allocation of services are managed by the cloud service providers. Meanwhile, the development of smart mobile devices and network technology enables the collection and transmission of a huge amount of data, which creates the mobile edge computing shifting tasks to the network edge for mobile users. In this paper, we propose a deviceless edge computing system targeting the mobility of end users. We focus on the migration of virtual functions to provide uninterrupted services to mobile users. We introduce the deviceless edge computing model and propose a seamless migration scheme of virtual functions with limited involvement of function developers. We formulate the migration decision problem into integer linear programming and use receding horizon control (RHC) for online solutions. We implement the migration system and algorithm to support delay-sensitive scenarios over real edge devices and develop a streaming game as the virtual function to test the performance. Extensive experiments in real scenarios exhibit the system has the ability to support high-mobility and delay-sensitive application scenarios. Extensive simulation results also show its applicability over large-scale networks.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133958780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Designing Robust Deep Learning Classifiers for Image-based Malware Analysis 基于图像的恶意软件分析的鲁棒深度学习分类器设计

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00126

Giacomo Iadarola, F. Mercaldo, Fabio Martinelli, A. Santone

引用次数: 1

AIACC-Training: Optimizing Distributed Deep Learning Training through Multi-streamed and Concurrent Gradient Communications AIACC-Training:通过多流和并发梯度通信优化分布式深度学习训练

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00087

Lixiang Lin, Shenghao Qiu, Ziqi Yu, Liang You, Long Xin, Xiaoyang Sun, J. Xu, Zheng Wang

{"title":"AIACC-Training: Optimizing Distributed Deep Learning Training through Multi-streamed and Concurrent Gradient Communications","authors":"Lixiang Lin, Shenghao Qiu, Ziqi Yu, Liang You, Long Xin, Xiaoyang Sun, J. Xu, Zheng Wang","doi":"10.1109/ICDCS54860.2022.00087","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00087","url":null,"abstract":"There is a growing interest in training deep neural networks (DNNs) in a GPU cloud environment. This is typically achieved by running parallel training workers on multiple GPUs across computing nodes. Under such a setup, the communication overhead is often responsible for long training time and poor scalability. This paper presents AIACC-Training, a unified communication framework designed for the distributed training of DNNs in a GPU cloud environment. AIACC-Training permits a training worker to participate in multiple gradient communication operations simultaneously to improve network bandwidth utilization and reduce communication latency. It employs auto-tuning techniques to dynamically determine the right communication parameters based on the input DNN workloads and the underlying network infrastructure. AIACC-Training has been deployed to production at Alibaba GPU Cloud with 3000+ GPUs executing AIACC-Training optimized code at any time. Experiments performed on representative DNN workloads show that AIACC-Training outperforms existing solutions, improving the training throughput and scalability by a large margin.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132928287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

FileInsurer: A Scalable and Reliable Protocol for Decentralized File Storage in Blockchain file保险人:区块链中分散文件存储的可扩展和可靠协议

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00025

Hongyin Chen, Yu‐Ju Lu, Yukun Cheng

{"title":"FileInsurer: A Scalable and Reliable Protocol for Decentralized File Storage in Blockchain","authors":"Hongyin Chen, Yu‐Ju Lu, Yukun Cheng","doi":"10.1109/ICDCS54860.2022.00025","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00025","url":null,"abstract":"With the development of blockchain applications, the requirements for file storage in blockchain are increasing rapidly. Many protocols, including Filecoin, Arweave, and Sia, have been proposed to provide scalable decentralized file storage for blockchain applications. However, the reliability is not well promised by existing protocols. Inspired by the idea of insurance, we innovatively propose a decentralized file storage protocol in blockchain, named as FileInsurer, to achieve both scalability and reliability. While ensuring scalability by distributed storage, FileInsurer guarantees reliability by enhancing robustness and fully compensating for the file loss. Specifically, under mild conditions, we prove that no more than 0.1% value of all files should be compensated even if half of the storage collapses. Therefore, only a relatively small deposit needs to be pledged by storage providers to cover the potential file loss. Because of lower burdens of deposit, storage providers have more incentives to participate in the storage network. FileInsurer can run in the top layer of the InterPlanetary File System (IPFS), and thus it can be directly applied in Web 3.0, Non-Fungible Tokens, and Metaverse.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114699411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2