2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)最新文献

{"title":"Demo: BubbleNet: Towards developing an IoT-based Physically Distant Classroom For Personal Bubbles","authors":"Brandon Purvis, Dheryta Jaisinghani, S. Diesburg, H. Lone","doi":"10.1109/ICDCS51616.2021.00112","DOIUrl":"https://doi.org/10.1109/ICDCS51616.2021.00112","url":null,"abstract":"The COVID-19 pandemic has presented unprecedented challenges across the world and universities are not saved either. Standard classroom activities in COVID era are even more challenging, with the primary challenge being ensuring physical distancing. We present a smart classroom system, BubbleNet, that attempts to relax these challenges. BubbleNet leverages cost-effective (~ $30) IoT nodes with motion sensors. The IoT nodes collaborate with each other via OpenThread - the latest open-source mesh networking protocol released by Google. In this paper, we present the development and demonstration of Bub-bieN et for monitoring physical distancing rules in a classroom.","PeriodicalId":222376,"journal":{"name":"2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128071334","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}

{"title":"Complexity-aware Adaptive Training and Inference for Edge-Cloud Distributed AI Systems","authors":"Yinghan Long, I. Chakraborty, G. Srinivasan, Kaushik Roy","doi":"10.1109/ICDCS51616.2021.00061","DOIUrl":"https://doi.org/10.1109/ICDCS51616.2021.00061","url":null,"abstract":"The ubiquitous use of IoT and machine learning applications is creating large amounts of data that require accurate and real-time processing. Although edge-based smart data processing can be enabled by deploying pretrained models, the energy and memory constraints of edge devices necessitate distributed deep learning between the edge and the cloud for complex data. In this paper, we propose a distributed system to exploit both the edge and the cloud for training and inference. We propose a new architecture, MEANet, with a main block, an extension block, and an adaptive block for the edge. The inference process can terminate at either the main block, the extension block, or the cloud. MEANet is trained to categorize inputs into easy/hard/complex classes. The main block identifies instances of easy/hard classes and classifies easy classes with high confidence. Only data with high probabilities of belonging to hard classes would be sent to the extension block for prediction. Further, only if the neural network at the edge shows low confidence in the prediction, the instance is considered complex and sent to the cloud for further processing. The training technique lends to the majority of inference on edge devices while going to the cloud only for a small set of complex jobs. The performance of the proposed system is evaluated via extensive experiments using modified models of ResNets and MobileNetV2 on CIFAR-100 and ImageNet datasets. The results show that the proposed distributed model has improved accuracy and lower energy consumption compared to standard models, indicating its capacity to adapt.","PeriodicalId":222376,"journal":{"name":"2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126722955","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}

{"title":"CAD3: Edge-facilitated Real-time Collaborative Abnormal Driving Distributed Detection","authors":"A. Alhilal, Tristan Braud, Xiang Sut, Luay Al Asadi, P. Hui","doi":"10.1109/ICDCS51616.2021.00074","DOIUrl":"https://doi.org/10.1109/ICDCS51616.2021.00074","url":null,"abstract":"Speeding, slowing down, and sudden acceleration are the leading causes of fatal accidents on highways. Anomalous driving behavior detection can improve road safety by informing drivers who are in the vicinity of dangerous vehicles. However, detecting abnormal driving behavior at the city-scale in a centralized fashion results in considerable network and computation load, that would significantly restrict the scalability of the system. In this paper, we propose CAD3, a distributed collaborative system for road-aware and driver-aware anomaly driving detection. CAD3 considers a decentralized deployment of edge computation nodes on the roadside and combines collaborative and context-aware computation with low-latency communication to detect and inform nearby drivers of unsafe behaviors of other vehicles in real-time. Adjacent edge nodes collaborate to improve the detection of abnormal driving behavior at the city-scale. We evaluate CAD3 with a physical testbed implementation. We emulate realistic driving scenarios from a real driving data set of 3,000 vehicles, 214,000 trips, and 18 million trajectories of private cars in Shenzhen, China. At the microscopic (road) level, CAD3 significantly improves the accuracy of detection and lowers the number of potential accidents caused by false negatives up to four times and 24 times as compared to distributed standalone and centralized models, respectively. CAD3 can scale up to 256 vehicles connected to a single node while keeping the end-to-end latency under 50 ms and a required bandwidth below 5 mbps. At the mesoscopic (driver-trip) level, CAD3 performs stable and accurate detection over time, owing to local RSU interaction. With a dense deployment of edge nodes, CAD3 can scale up to the size of Shenzhen, a megalopolis of 12 million inhabitant with over 2 million concurrent vehicles at peak hours.","PeriodicalId":222376,"journal":{"name":"2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127680942","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}

{"title":"Pluto: High-Performance IoT-Aware Stream Processing","authors":"Taegeon Um, Gyewon Lee, Byung-Gon Chun","doi":"10.1109/ICDCS51616.2021.00017","DOIUrl":"https://doi.org/10.1109/ICDCS51616.2021.00017","url":null,"abstract":"Nowadays, large numbers of small IoT stream queries are created from diverse IoT applications and executed on cloud backend servers. However, existing distributed stream processing systems such as Storm and Flink do not efficiently handle the large numbers of IoT stream queries because of their tightly-coupled query/code submission layer and inefficient query execution layer. In this paper, we propose Pluto, a new IoT-aware stream processing system. As a first step for IoT stream processing, this paper focuses on optimizing the execution of many IoT stream queries on a node. Pluto optimizes the end-to-end query processing with a three-phase execution, harnessing IoT-query characteristics. First, Pluto minimizes bottlenecks in the IoT query submission by decoupling the code registration from the query submission process with new APIs, which eliminates duplicate code registration and enables code sharing across queries. Second, in the execution phase, Pluto shares system resources as much as possible and minimizes resource bottlenecks in a machine by exploiting commonalities among IoT stream queries and information exposed in the API. Our evaluations show that Pluto improves the throughput by an order of magnitude compared to other stream processing systems on a 24-core machine, keeping P99 latency less than one second.","PeriodicalId":222376,"journal":{"name":"2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133681309","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}

{"title":"SHARE: Shaping Data Distribution at Edge for Communication-Efficient Hierarchical Federated Learning","authors":"Yongheng Deng, Feng Lyu, Ju Ren, Yongmin Zhang, Yuezhi Zhou, Yaoxue Zhang, Yuanyuan Yang","doi":"10.1109/ICDCS51616.2021.00012","DOIUrl":"https://doi.org/10.1109/ICDCS51616.2021.00012","url":null,"abstract":"Federated learning (FL) can enable distributed model training over mobile nodes without sharing privacy-sensitive raw data. However, to achieve efficient FL, one significant challenge is the prohibitive communication overhead to commit model updates since frequent cloud model aggregations are usually required to reach a target accuracy, especially when the data distributions at mobile nodes are imbalanced. With pilot experiments, it is verified that frequent cloud model aggregations can be avoided without performance degradation if model aggregations can be conducted at edge. To this end, we shed light on the hierarchical federated learning (HFL) framework, where a subset of distributed nodes are selected as edge aggregators to conduct edge aggregations. Particularly, under the HFL framework, we formulate a communication cost minimization (CCM) problem to minimize the communication cost raised by edge/cloud aggregations with making decisions on edge aggregator selection and distributed node association. Inspired by the insight that the potential of HFL lies in the data distribution at edge aggregators, we propose SHARE, i.e., SHaping dAta distRibution at Edge, to transform and solve the CCM problem. In SHARE, we divide the original problem into two sub-problems to minimize the per-round communication cost and mean Kullback-Leibler divergence of edge aggregator data, and devise two light-weight algorithms to solve them, respectively. Extensive experiments under various settings are carried out to corroborate the efficacy of SHARE.","PeriodicalId":222376,"journal":{"name":"2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134040840","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}

{"title":"Hand-Key: Leveraging Multiple Hand Biometrics for Attack-Resilient User Authentication Using COTS RFID","authors":"Jianwei Liu, Xiang Zou, Feng Lin, Jinsong Han, Xian Xu, K. Ren","doi":"10.1109/ICDCS51616.2021.00103","DOIUrl":"https://doi.org/10.1109/ICDCS51616.2021.00103","url":null,"abstract":"Biometrics have been widely used in user authentications. However, existing outer-body biometrics (e.g., fingerprint), collecting from body surface, are vulnerable to spoofing attacks. Although inner-body biometrics, such as the electrocardiogram, are hard to be forged, their complex acquisition methods and instability lead to unsatisfactory user experience. Therefore, achieving good user-friendliness and high security simultaneously in biometric-based authentication is challenging. In this paper, we propose Hand-Key, an attack-resilient and user-friendly user authentication system to address the above challenge. Hand-Key utilizes a low-cost radio frequency identification (RFID) tag array to simultaneously collect the inner-body composition and outer-body geometric features of human hand to identify users. Users are merely required to hold their hands in a ‘handshaking’ pose between a reader's antenna and a tag array during authentication. To further enhance the security, we tactfully leverage the inherent randomness of the anti-collision scheme in RFID systems to make Hand-Key immune against replay attacks. We built a prototype of Hand-Key and conducted extensive experiments with 30 volunteers. The results show that Hand-Key achieves an authentication success rate of 99%+.","PeriodicalId":222376,"journal":{"name":"2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)","volume":"25 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113994002","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}

{"title":"Demo: Resource Allocation for Wafer-Scale Deep Learning Accelerator","authors":"Huihong Peng, Longkun Guo, Long Sun, Xiaoyan Zhang","doi":"10.1109/ICDCS51616.2021.00114","DOIUrl":"https://doi.org/10.1109/ICDCS51616.2021.00114","url":null,"abstract":"Due to the rapid development of deep learning (DL) has brought, artificial intelligence (AI) chips were invented incorperating the traditional computing architecture with the simulated neural network structure for the sake of improving the energy efficiency. Recently, emerging deep learning AI chips imposed the challenge of allocating computing resources according to a deep neural networks (DNN), such that tasks using the DNN can be processed in a parallel and distributed manner. In this paper, we combine graph theory and combinatorial optimization technology to devise a fast floorplanning approach based on kernel graph structure, which is provided by Cerebras Systems Inc. for mapping the layers of DNN to the mesh of computing units called Wafer-Scale-Engine (WSE). Numerical experiments were carried out to evaluate our method using the public benchmarks and evaluation criteria, demonstrating its performance gain comparing to the state-of-art algorithms.","PeriodicalId":222376,"journal":{"name":"2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127439688","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}

{"title":"Heterogeneous Spatio-Temporal Graph Convolution Network for Traffic Forecasting with Missing Values","authors":"Weida Zhong, Qiuling Suo, Xiaowei Jia, Aidong Zhang, Lumin Su","doi":"10.1109/ICDCS51616.2021.00073","DOIUrl":"https://doi.org/10.1109/ICDCS51616.2021.00073","url":null,"abstract":"Accurate traffic prediction is indispensable for intelligent traffic management. The availability of large-scale road sensing data collected by connected wireless sensors and mobile devices have provided unrealized potential for traffic prediction. However, sensory data is often incomplete due to various factors in the process of data acquisition and transmission. The missingness of traffic data brings a key challenge to the traffic prediction task since the state-of-the-art ML-based traffic prediction models (e.g., Graph Convolutional Networks (GCN)) often rely on spatial and temporal completion of the data. Moreover, existing GCN-based methods usually build a static graph based on geographical distances and are limited in their ability to capture the time-evolving relationships amongst road segments. In this paper, we develop a heterogeneous spatio-temporal prediction framework for traffic prediction using incomplete historical data. In the framework, we build multiple graphs to explicitly model the dynamic correlations among road segments from both geographical and historical aspects, and employ recurrent neural networks to capture temporal correlations for each road segment. We impute missing values in a recurrent process, which is seamlessly embedded in the prediction framework so they can be jointly trained. The proposed framework is evaluated on a public dataset of static sensors and a private dataset collected by our roving sensor system. Experimental results show the effectiveness of the proposed framework compared to state-of-the-art methods, and indicate the potential to be deployed into real-world traffic prediction systems.","PeriodicalId":222376,"journal":{"name":"2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128149022","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}

{"title":"Haechi: A Token-based QoS Mechanism for One-sided I/Os in RDMA based Storage System","authors":"Qingyue Liu, P. Varman","doi":"10.1109/ICDCS51616.2021.00025","DOIUrl":"https://doi.org/10.1109/ICDCS51616.2021.00025","url":null,"abstract":"Advances in persistent memory and networking hardware are changing the architecture of storage systems and data management services in datacenters. Distributed, one-sided RDMA access to memory-resident data shows tremendous improvements in throughput, latency and server CPU utilization of storage servers. However, the silent nature of one-sided I/O simultaneously creates new challenging problems for providing QoS in such systems. In this paper, we propose Haechi, a work-conserving, token-based QoS mechanism to guarantee reservations and limits in storage systems that provide one-sided I/O services. Haechi decouples QoS enforcement into a QoS engine at the client and a QoS monitor at the data node. It leverages adaptive token dispatch, token conversion, and silent I/O reporting to guarantee the reservations of distributed clients while maintaining high server utilization. Empirical evaluations on the Chameleon cluster, with different reservation distributions and I/O access patterns, show that Haechi is successful in providing differentiated QoS with negligible overhead for token management.","PeriodicalId":222376,"journal":{"name":"2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121203850","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}

{"title":"An Efficient Message Dissemination Scheme for Cooperative Drivings via Multi-Agent Hierarchical Attention Reinforcement Learning","authors":"Bingyi Liu, Weizhen Han, Enshu Wang, Xin Ma, Shengwu Xiong, C. Qiao, Jianping Wang","doi":"10.1109/ICDCS51616.2021.00039","DOIUrl":"https://doi.org/10.1109/ICDCS51616.2021.00039","url":null,"abstract":"A group of connected and autonomous vehicles (CAVs) with common interests can drive in a cooperative manner, namely cooperative driving, which has been verified to significantly improve road safety, traffic efficiency, and environmental sustainability. A more general scenario with various types of cooperative driving applications such as truck platooning and vehicle clustering will coexist on roads in the foreseeable future. To support such multiple cooperative drivings, it is critical to design an efficient message dissemination scheduling for vehicles to broadcast their kinetic status, i.e., beacon periodically. Most ongoing researches suggest designing the communication protocols via traffic and communication modeling on top of dedicated short range communications (DSRC) or cellular-based vehicle-to-vehicle (C-V2V) communications as a potential remedy. However, most of the existing researches are designed for a simple or specific traffic scenario, e.g., ignoring the impacts of the complex communication environment and emerging hybrid traffic scenarios. Moreover, some studies design beaconing strategies based on the implication of channel and traffic conditions in the beacons of other vehicles. However, the delayed perception of these information may seriously deteriorate the beaconing performance. In this paper, we take the perspective of cooperative drivings and formulate their decision-making process as a Markov game. Furthermore, we propose a multi-agent hierarchical attention reinforcement learning (MAHA) framework to solve the Markov game. More concretely, the hierarchical structure of the proposed MAHA can lead cooperative drivings to be foresightful. Hence, even without immediate incentives, the well-trained agents can still take favorable actions that benefit their long-term rewards. Besides, we integrate each hierarchical level of MAHA separately with the graph attention network (GAT) to incorporate agents' mutual influences in the decision-making process. Besides, we set up a simulator and adopt this simulator to generate dynamic traffic scenarios, which reflect the different real-world scenarios faced by cooperative drivings. We conduct extensive experiments to evaluate the proposed MAHA framework's performance. The results show that MAHA can significantly improve the beacon reception rate and guarantee low communication delay in all of these scenarios.","PeriodicalId":222376,"journal":{"name":"2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126133918","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}