2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)最新文献_第9页

Enhancing Quality of Experience for Collaborative Virtual Reality with Commodity Mobile Devices 基于商品移动设备的协同虚拟现实体验质量提升研究

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00102

Jiangong Chen, Feng Qian, Bin Li

{"title":"Enhancing Quality of Experience for Collaborative Virtual Reality with Commodity Mobile Devices","authors":"Jiangong Chen, Feng Qian, Bin Li","doi":"10.1109/ICDCS54860.2022.00102","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00102","url":null,"abstract":"Virtual Reality (VR), together with the network infrastructure, can provide an interactive and immersive experience for multiple users simultaneously and thus enables collaborative VR applications (e.g., VR-based classroom). However, the satisfactory user experience requires not only high-resolution panoramic image rendering but also extremely low latency and seamless user experience. Besides, the competition for limited network resources (e.g., multiple users share the total limited bandwidth) poses a significant challenge to collaborative user experience, in particular under the wireless network with time-varying capacities. While existing works have tackled some of these challenges, a principled design considering all those factors is still missing. In this paper, we formulate a combinatorial optimization problem to maximize the Quality of Experience (QoE), defined as the linear combination of the quality, the average VR content delivery delay, and variance of the quality over a finite time horizon. In particular, we incorporate the influence of imperfect motion prediction when considering the quality of the perceived contents. However, the optimal solution to this problem can not be implemented in real-time since it relies on future decisions. Then, we decompose the optimization problem into a series of combinatorial optimization in each time slot and develop a low-complexity algorithm that can achieve at least 1/2 of the optimal value. Despite this, the trace-based simulation results reveal that our algorithm performs very close to the decomposed optimal offline solution. Furthermore, we implement our proposed algorithm in a practical system with commercial mobile devices and demonstrate its superior performance over state-of-the-art algorithms. We open-source our implementations on https://github.com/SNeC-Lab-PSU/ICDCS-CollaborativeVR.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":" 45","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132123462","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

Energy-Efficient and QoE-Aware 360-Degree Video Streaming on Mobile Devices 移动设备上的节能和qos感知360度视频流

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00100

Xianda Chen, Guohong Cao

{"title":"Energy-Efficient and QoE-Aware 360-Degree Video Streaming on Mobile Devices","authors":"Xianda Chen, Guohong Cao","doi":"10.1109/ICDCS54860.2022.00100","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00100","url":null,"abstract":"Tile-based streaming has been widely used in 360° video streaming to adapt to varying network conditions. However, downloading and processing many small tiles consumes a large amount of energy on mobile devices. To address this issue, we propose techniques to encode video by considering the viewing popularity, where the tiles requested by users of similar interests are encoded as a large tile (called Ptile). When encoding Ptiles, we propose to further save energy by reducing the insignificant frames in each video segment, i.e., reducing the frame rate to save energy while satisfying some QoE constraint. Based on real video traces, we model the impact of video features (i.e., video bitrate, frame rate) and user behavior (i.e., view switching) on QoE, and model the impact of video features on power consumption. Based on the QoE model and the power model, we formulate the energy-efficient and QoE-aware 360° video streaming problem as an optimization problem, and propose a control theory based algorithm to solve it. Through extensive evaluations based on real traces, we demonstrate that the proposed algorithm can significantly reduce the energy consumption (49.7%) and improve the QoE (7.4%).","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"42 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":"133809990","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: 4

Toward Cleansing Backdoored Neural Networks in Federated Learning 清除联邦学习中的后门神经网络

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00084

Chen Wu, Xian Yang, Sencun Zhu, P. Mitra

{"title":"Toward Cleansing Backdoored Neural Networks in Federated Learning","authors":"Chen Wu, Xian Yang, Sencun Zhu, P. Mitra","doi":"10.1109/ICDCS54860.2022.00084","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00084","url":null,"abstract":"Malicious clients can attack federated learning systems using compromised data during the training phase, including backdoor samples. The compromised global model will perform well on the validation dataset designed for the task, but a small subset of data with backdoor patterns may trigger the model to make a wrong prediction. In this work, we propose a new and effective method to mitigate backdoor attacks in federated learning after the training phase. Through federated pruning method, we remove redundant neurons and \"backdoor neurons\", which trigger misbehavior upon recognizing backdoor patterns while keeping silent when the input data is clean. The second optional fine-tuning process is designed to recover the pruning damage to the test accuracy on benign datasets. In the last step, we eliminate backdoor attacks by limiting the extreme values of inputs and neural network neurons’ weights. Experiments using our defenses mechanism against the state-of-the-art Distributed Backdoor Attacks on CIFAR-10 show promising results; the averaged attack success rate drops more than 70% with less than 2% loss of test accuracy on the validation dataset. Our defense method has also outperformed the state-of-the-art pruning defense against backdoor attacks in the federated learning scenario.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"45 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":"114074776","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 Digital-Twin Based Architecture for Software Longevity in Smart Homes 基于数字孪生的智能家居软件寿命架构

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00070

Peter Zdankin, Marco Picone, M. Mamei, Torben Weis

{"title":"A Digital-Twin Based Architecture for Software Longevity in Smart Homes","authors":"Peter Zdankin, Marco Picone, M. Mamei, Torben Weis","doi":"10.1109/ICDCS54860.2022.00070","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00070","url":null,"abstract":"Smart homes usually consist of smart objects (SOs) with limited resources and capabilities, and therefore constrain the complexity of applications that can be performed on them. In particular, updating smart objects within a smart home is a challenging undertaking, as seemingly insignificant updates affect the longevity of the deployment if they cause previously established dependencies to break. In this paper, we propose an architecture that we call Longevity Digital Twins (LDTs) as a strategic counterpart of SOs, aimed at running at the edge, as local to the smart home as possible. With this architecture, the capabilities of a SO can be virtually enhanced to support the software update process in the smart home. In this context, foresighted software management requires both a local capability to describe involved functionalities together with awareness about existing dependencies in this distributed system. Using a simulated smart home environment, we first measure the impact of conventional update strategies and then present the noticeable improvement that LDTs offer to this problem. Going further, we present the analysis of a real-world use case that showcases the potential of LDTs on how it could not only prevent the installation of breaking updates but also extend a SOs capabilities and its overall longevity.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"25 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":"124899825","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

Optimizing Near-Data Processing for Spark 优化Spark的近数据处理

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00067

Sri Pramodh Rachuri, Arun Gantasala, Prajeeth Emanuel, Anshul Gandhi, Robert Foley, Peter Puhov, Theo Gkountouvas, H. Lei

{"title":"Optimizing Near-Data Processing for Spark","authors":"Sri Pramodh Rachuri, Arun Gantasala, Prajeeth Emanuel, Anshul Gandhi, Robert Foley, Peter Puhov, Theo Gkountouvas, H. Lei","doi":"10.1109/ICDCS54860.2022.00067","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00067","url":null,"abstract":"Resource disaggregation (RD) is an emerging paradigm for data center computing whereby resource-optimized servers are employed to minimize resource fragmentation and improve resource utilization. Apache Spark deployed under the RD paradigm employs a cluster of compute-optimized servers to run executors and a cluster of storage-optimized servers to host the data on HDFS. However, the network transfer from storage to compute cluster becomes a severe bottleneck for big data processing. Near-data processing (NDP) is a concept that aims to alleviate network load in such cases by offloading (or \"pushing down\") some of the compute tasks to the storage cluster. Employing NDP for Spark under the RD paradigm is challenging because storage-optimized servers have limited computational resources and cannot host the entire Spark processing stack. Further, even if such a lightweight stack could be developed and deployed on the storage cluster, it is not entirely obvious which Spark queries would benefit from pushdown, and which tasks of a given query should be pushed down to storage.This paper presents the design and implementation of a near-data processing system for Spark, SparkNDP, that aims to address the aforementioned challenges. SparkNDP works by implementing novel NDP Spark capabilities on the storage cluster using a lightweight library of SQL operators and then developing an analytical model to help determine which Spark tasks should be pushed down to storage based on the current network and system state. Simulation and prototype implementation results show that SparkNDP can help reduce Spark query execution times when compared to both the default approach of not pushing down any tasks to storage and the outright NDP approach of pushing all tasks to storage.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"121 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":"127440710","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

GRAFICS: Graph Embedding-based Floor Identification Using Crowdsourced RF Signals GRAFICS:使用众包射频信号的基于图形嵌入的地板识别

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00105

Weipeng Zhuo, Ziqi Zhao, Ka Ho Chiu, Shiju Li, Sangtae Ha, Chul-Ho Lee, S. G. Gary Chan

{"title":"GRAFICS: Graph Embedding-based Floor Identification Using Crowdsourced RF Signals","authors":"Weipeng Zhuo, Ziqi Zhao, Ka Ho Chiu, Shiju Li, Sangtae Ha, Chul-Ho Lee, S. G. Gary Chan","doi":"10.1109/ICDCS54860.2022.00105","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00105","url":null,"abstract":"We study the problem of floor identification for radiofrequency (RF) signal samples obtained in a crowdsourced manner, where the signal samples are highly heterogeneous and most samples lack their floor labels. We propose GRAFICS, a graph embedding-based floor identification system. GRAFICS first builds a highly versatile bipartite graph model, having APs on one side and signal samples on the other. GRAFICS then learns the low-dimensional embeddings of signal samples via a novel graph embedding algorithm named E-LINE. GRAFICS finally clusters the node embeddings along with the embeddings of a few labeled samples through a proximity-based hierarchical clustering, which eases the floor identification of every new sample. We validate the effectiveness of GRAFICS based on two large-scale datasets that contain RF signal records from 204 buildings in Hangzhou, China, and five buildings in Hong Kong. Our experiment results show that GRAFICS achieves highly accurate prediction performance with only a few labeled samples (96% in both micro- and macro-F scores) and significantly outperforms several state-of-the-art algorithms (by about 45% improvement in micro-F score and 53% in macro-F score).","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"15 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":"127970144","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

Prism: Streamlined Packet Processing for Containers with Flow Prioritization Prism:具有流优先级的容器的流线型包处理

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00040

Manish Munikar, Jiaxin Lei, Hui Lu, J. Rao

{"title":"Prism: Streamlined Packet Processing for Containers with Flow Prioritization","authors":"Manish Munikar, Jiaxin Lei, Hui Lu, J. Rao","doi":"10.1109/ICDCS54860.2022.00040","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00040","url":null,"abstract":"Advanced high-speed network cards have made packet processing in host operating systems a major performance bottleneck. The kernel network stack gives rise to various sources of overheads that limit the throughput and lengthen the per-packet processing latency. The problem is further exacerbated for short-lived, latency-sensitive network flows such as control packets, online gaming, database requests, etc. — in a highly utilized system, especially in virtualized (containerized) cloud environments, short flows can experience excessively long in-kernel queuing delays. As a consequence, recent research works propose to bypass the kernel network stack to enable lightweight, custom userspace network stacks for improved performance, but at a heavy cost of compatibility and security. In this paper, we take a different approach: We first analyze various sources of inefficiencies in the kernel network stack and propose ways to mitigate them without compromising systems compatibility, security, or flexibility. Further, we propose Prism, a novel mechanism in the kernel network stack to differentiate incoming packets based on their performance requirements and streamline the processing stages of multi-stage packet processing pipelines (e.g., in container overlay networks). Our evaluation demonstrates that Prism can significantly improve the latency of high-priority flows in container overly networks in the presence of heavy low-priority background traffic.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"13 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":"115493502","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

An Automated Framework for Distributed Deep Learning–A Tool Demo 分布式深度学习的自动化框架——一个工具演示

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00142

Gharib Gharibi, Ravi Patel, A.N. Khan, Babak Poorebrahim Gilkalaye, Praneeth Vepakomma, Ramesh Raskar, Steve Penrod, Greg Storm, Riddhiman Das

{"title":"An Automated Framework for Distributed Deep Learning–A Tool Demo","authors":"Gharib Gharibi, Ravi Patel, A.N. Khan, Babak Poorebrahim Gilkalaye, Praneeth Vepakomma, Ramesh Raskar, Steve Penrod, Greg Storm, Riddhiman Das","doi":"10.1109/ICDCS54860.2022.00142","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00142","url":null,"abstract":"Split learning (SL) is a distributed deep-learning approach that enables individual data owners to train a shared model over their joint data without exchanging it with one another. SL has been the subject of much research in recent years, leading to the development of several versions for facilitating distributed learning. However, the majority of this work mainly focuses on optimizing the training process while largely ignoring the design and implementation of practical tool support. To fill this gap, we present our automated software framework for training deep neural networks from decentralized data based on our extended version of SL, termed Blind Learning. Specifically, we shed light on the underlying optimization algorithm, explain the design and implementation details of our framework, and present our preliminary evaluation results. We demonstrate that Blind Learning is 65% more computationally efficient than SL and can produce better performing models. Moreover, we show that running the same job in our framework is at least 4.5× faster than PySyft. Our goal is to spur the development of proper tool support for distributed deep learning.","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":"116446312","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

Multi-View Scheduling of Onboard Live Video Analytics to Minimize Frame Processing Latency 机载实时视频分析的多视图调度以最小化帧处理延迟

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00055

Shengzhong Liu, Tianshi Wang, Hongpeng Guo, Xinzhe Fu, P. David, Maggie B. Wigness, Archan Misra, T. Abdelzaher

{"title":"Multi-View Scheduling of Onboard Live Video Analytics to Minimize Frame Processing Latency","authors":"Shengzhong Liu, Tianshi Wang, Hongpeng Guo, Xinzhe Fu, P. David, Maggie B. Wigness, Archan Misra, T. Abdelzaher","doi":"10.1109/ICDCS54860.2022.00055","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00055","url":null,"abstract":"This paper presents a real-time multi-view scheduling framework for DNN-based live video analytics at the edge to minimize frame processing latency. The work is motivated by applications where a higher frame rate is important, not to miss actions of interest. Examples include defense, border security, and intruder detection applications where sensors (in this paper, cameras) are deployed to monitor key roads, chokepoints, or passageways to identify events of interest (and intervene in real-time). Supporting a higher frame rate entails lowering frame processing latency. We assume that multiple cameras are deployed with partially overlapping views. Each camera has access to limited onboard computing capacity. Many targets cross the field of view of these cameras (but the great majority do not require action). We take advantage of the spatial-temporal correlations among multi-camera video streams to perform target-to-camera assignment such that the maximum frame processing time across cameras is minimized. Specifically, we use a data-driven approach to identify objects seen by multiple cameras, and propose a batch-aware latency-balanced (BALB) scheduling algorithm to drive the object-to-camera assignment. We empirically evaluate the proposed system with a real-world surveillance dataset on a testbed consisting of multiple NVIDIA Jetson boards. The results show that our system substantially improves the video processing speed, attaining multiplicative speedups of 2.45× to 6.85×, and consistently outperforms the competitive static region partitioning strategy.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"28 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":"114878071","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

D3: Lightweight Secure Fault Localization in Edge Cloud 边缘云中的轻量级安全故障定位

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2022-07-01 DOI: 10.1109/ICDCS54860.2022.00056

Songtao Fu, Qi Li, Xiaoliang Wang, Su Yao, Xuewei Feng, Ziqiang Wang, Xinle Du, Kao Wan, Ke Xu

{"title":"D3: Lightweight Secure Fault Localization in Edge Cloud","authors":"Songtao Fu, Qi Li, Xiaoliang Wang, Su Yao, Xuewei Feng, Ziqiang Wang, Xinle Du, Kao Wan, Ke Xu","doi":"10.1109/ICDCS54860.2022.00056","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00056","url":null,"abstract":"In pursuit of high-performance applications, the cloud is moving out of the data center and towards the edge. Secure data forwarding is critical for the users between the edge and the remote cloud. In this paper, we propose D3 (Demon Detector in Data Plane), a lightweight, secure fault localization mechanism, which can enable the users in the edge cloud to localize faulty links and thus avoid the faulty links to guarantee secure data forwarding along the path to the remote cloud. D3 utilizes the user to instruct the transit routers, thus empowering the user to detect whether the transit routers forward the packet as expected. Compared with existing schemes that are difficult to be deployed in practice due to the incurred heavy storage, computation, and communication overhead, D3 offloads most of the transit router’s storage and computation overhead, thus dramatically improving the deployment efficiency. Particularly, the length of the additional packet header in D3 is 2-5 times less than the state-of-the-art mechanisms, and the extra control packet overhead is ten times less while keeping a little constant storage overhead in the data plane. The evaluations in BMv2 and Barefoot Tofino hardware show that D3 could achieve high fault localization accuracy and efficiency.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"38 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":"117204098","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