Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00050
Yuanhe Shu, Jingwei Wang, L. Kong, Jiadi Yu, Guisong Yang, Yueping Cai, Zhen Wang, M. K. Khan
The booming of mobile technologies and Internet of Things (IoTs) have facilitated the explosion of wireless devices and brought convenience to people's daily lives. Coming with the explosive growth of wireless devices, incompatibility of heterogeneous wireless technologies hindered the growing demands for everything connected. And spectrum sharing among heterogeneous wireless technologies has led to severe Cross-Technology Interference (CTI), which is a vital obstacle for network reliability and spectrum utilization. Researches in recent years have shown that Cross-Technology Communication (CTC) turns out to be a promising solution with broad perspective for the coexistence of heterogeneous wireless technologies. However, due to the physical layer incompatibility of WiFi and Bluetooth Low Energy (BLE), the researches about CTC between these two most wildly used wireless technologies are limited by now. In this paper, we propose WiBWi, a payload encoding-based bidirectional CTC scheme between BLE and WiFi, which can achieve near-optimal throughput and powerful robustness. For uplink, i.e., BLE to WiFi communication, WiBWi leverages a novel extended WiFi preamble detection rule and probabilistic inference based encode mapping to achieve fast and reliable communication. For downlink, i.e., WiFi to BLE communication, WiBWi introduces an encoding mapping scheme in the sight of BLE receiver with little modification to accomplish high throughput and robustness. Extensive evaluation shows that WiBWi can offer near-optimal throughput (near the maximum throughput of BLE) and extremely low bit error rate (less than 1%).
{"title":"WiBWi: Encoding-based Bidirectional Physical-Layer Cross-Technology Communication between BLE and WiFi","authors":"Yuanhe Shu, Jingwei Wang, L. Kong, Jiadi Yu, Guisong Yang, Yueping Cai, Zhen Wang, M. K. Khan","doi":"10.1109/ICPADS53394.2021.00050","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00050","url":null,"abstract":"The booming of mobile technologies and Internet of Things (IoTs) have facilitated the explosion of wireless devices and brought convenience to people's daily lives. Coming with the explosive growth of wireless devices, incompatibility of heterogeneous wireless technologies hindered the growing demands for everything connected. And spectrum sharing among heterogeneous wireless technologies has led to severe Cross-Technology Interference (CTI), which is a vital obstacle for network reliability and spectrum utilization. Researches in recent years have shown that Cross-Technology Communication (CTC) turns out to be a promising solution with broad perspective for the coexistence of heterogeneous wireless technologies. However, due to the physical layer incompatibility of WiFi and Bluetooth Low Energy (BLE), the researches about CTC between these two most wildly used wireless technologies are limited by now. In this paper, we propose WiBWi, a payload encoding-based bidirectional CTC scheme between BLE and WiFi, which can achieve near-optimal throughput and powerful robustness. For uplink, i.e., BLE to WiFi communication, WiBWi leverages a novel extended WiFi preamble detection rule and probabilistic inference based encode mapping to achieve fast and reliable communication. For downlink, i.e., WiFi to BLE communication, WiBWi introduces an encoding mapping scheme in the sight of BLE receiver with little modification to accomplish high throughput and robustness. Extensive evaluation shows that WiBWi can offer near-optimal throughput (near the maximum throughput of BLE) and extremely low bit error rate (less than 1%).","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133625889","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}
Accurate traffic prediction is of great importance in Intelligent Transportation System. This problem is very challenging due to the complex spatial and long-range temporal dependencies. Existing models generally suffer two limitations: (1) GCN-based methods usually use a fixed Laplacian matrix to model spatial dependencies, without considering their dynamics; (2) RNN and its variants are only capable of modeling a limited-range temporal dependencies, resulting in significant information loss. In this paper, we propose a novel spatial-temporal graph neural network (STNN), an end-to-end solution for traffic prediction that simultaneously captures dynamic spatial and long-range temporal dependencies. Specifically, STNN first uses a spatial attention network to model complex and dynamic spatial correlations, without any expensive matrix operations or relying on predefined road network topologies. Second, a temporal transformer network is utilized to model long-range temporal dependencies across multiple time steps, which considers not only the recent segment, but also the periodic dependencies of historical data. Making full use of historical data can alleviate the difficulty of obtaining real-time data and improve the prediction accuracy. Experiments are conducted on two real-world traffic datasets, and the results verify the effectiveness of the proposed model, especially in long-term traffic prediction.
{"title":"STNN: A Spatial-Temporal Graph Neural Network for Traffic Prediction","authors":"Xueyan Yin, Fei Li, Genze Wu, Pengfei Wang, Yanming Shen, Heng Qi, Baocai Yin","doi":"10.1109/ICPADS53394.2021.00024","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00024","url":null,"abstract":"Accurate traffic prediction is of great importance in Intelligent Transportation System. This problem is very challenging due to the complex spatial and long-range temporal dependencies. Existing models generally suffer two limitations: (1) GCN-based methods usually use a fixed Laplacian matrix to model spatial dependencies, without considering their dynamics; (2) RNN and its variants are only capable of modeling a limited-range temporal dependencies, resulting in significant information loss. In this paper, we propose a novel spatial-temporal graph neural network (STNN), an end-to-end solution for traffic prediction that simultaneously captures dynamic spatial and long-range temporal dependencies. Specifically, STNN first uses a spatial attention network to model complex and dynamic spatial correlations, without any expensive matrix operations or relying on predefined road network topologies. Second, a temporal transformer network is utilized to model long-range temporal dependencies across multiple time steps, which considers not only the recent segment, but also the periodic dependencies of historical data. Making full use of historical data can alleviate the difficulty of obtaining real-time data and improve the prediction accuracy. Experiments are conducted on two real-world traffic datasets, and the results verify the effectiveness of the proposed model, especially in long-term traffic prediction.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117234571","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}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00092
Xudong Yang, Ling Gao, Hai Wang, Yan Li, Jie Zheng, Jipeng Xu, Yuhui Ma
With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on privacy protection, they ignore the negative impact on the security of the knowledge of user-related semantic information of locations that attacker has. To solve this issue, we proposed a User-related Semantic Location Privacy Protection Mechanism (USPPM) based on k-anonymity. First, the anonymity set generation method that combines user-related mobile semantic feature of locations and semantic diversity entropy is proposed to improve the location semantic privacy safety. Second, we design an anonymity set optimization method which enhances sensitive semantic location privacy, through stackberg game model between attacker and protector. Finally, compared with other solutions, experiment on the real dataset shows that our algorithms can provide location privacy efficiently.
{"title":"A User-related Semantic Location Privacy Protection Method In Location-based Service","authors":"Xudong Yang, Ling Gao, Hai Wang, Yan Li, Jie Zheng, Jipeng Xu, Yuhui Ma","doi":"10.1109/ICPADS53394.2021.00092","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00092","url":null,"abstract":"With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on privacy protection, they ignore the negative impact on the security of the knowledge of user-related semantic information of locations that attacker has. To solve this issue, we proposed a User-related Semantic Location Privacy Protection Mechanism (USPPM) based on k-anonymity. First, the anonymity set generation method that combines user-related mobile semantic feature of locations and semantic diversity entropy is proposed to improve the location semantic privacy safety. Second, we design an anonymity set optimization method which enhances sensitive semantic location privacy, through stackberg game model between attacker and protector. Finally, compared with other solutions, experiment on the real dataset shows that our algorithms can provide location privacy efficiently.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131439065","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}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00108
Iryanto Jaya, Yusen Li, Wentong Cai
Cloud gaming abstracts the concept of traditional gaming and places the gaming activities on remote rendering servers (RSes). Although this allows heterogeneous devices to gain access to multiple game titles, latency issue is always unavoidable. Each game input must go through a complete round trip between the player's device and the cloud gaming server. Hence, cloud games are not as responsive as traditional computer games where the game logic runs locally. Moreover, in order to have an acceptable level of game playability, the latency level must be within a certain threshold. This also prevents some players who are located in remote regions from playing the game due to high latency. Therefore, in this paper, we employ edge servers in order to reach those players by activating lower capability RSes which are more geographically distributed. Furthermore, we also allow workload splitting of foreground and background rendering between edge and cloud RSes to ease the burden of each individual RS with a trade-off between cost and latency constraints. From our experiments, our architecture and allocation scheme results in reduction of play request rejections for up to 28% compared to traditional cloud gaming approach.
{"title":"Minimizing Play Request Rejection through Workload Splitting in Edge-Cloud Gaming","authors":"Iryanto Jaya, Yusen Li, Wentong Cai","doi":"10.1109/ICPADS53394.2021.00108","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00108","url":null,"abstract":"Cloud gaming abstracts the concept of traditional gaming and places the gaming activities on remote rendering servers (RSes). Although this allows heterogeneous devices to gain access to multiple game titles, latency issue is always unavoidable. Each game input must go through a complete round trip between the player's device and the cloud gaming server. Hence, cloud games are not as responsive as traditional computer games where the game logic runs locally. Moreover, in order to have an acceptable level of game playability, the latency level must be within a certain threshold. This also prevents some players who are located in remote regions from playing the game due to high latency. Therefore, in this paper, we employ edge servers in order to reach those players by activating lower capability RSes which are more geographically distributed. Furthermore, we also allow workload splitting of foreground and background rendering between edge and cloud RSes to ease the burden of each individual RS with a trade-off between cost and latency constraints. From our experiments, our architecture and allocation scheme results in reduction of play request rejections for up to 28% compared to traditional cloud gaming approach.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130301536","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}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00014
Jiale Chen, D. V. Le, R. Tan, Daren Ho
Convolutional neural networks (CNNs) are increasingly adopted on resource-constrained sensors for in-situ data analytics in Internet of Things (IoT) applications. This paper presents a model split framework, namely, splitCNN, in order to run a large CNN on a collection of concurrent IoT sensors. Specifically, we adopt CNN filter pruning techniques to split the large CNN into multiple small-size models, each of which is only sensitive to a certain number of data classes. These class-specific models are deployed onto the resource-constrained concurrent sensors which collaboratively perform distributed CNN inference on their same/similar sensing data. The outputs of multiple models are then fused to yield the global inference result. We apply splitCNN to three case studies with different sensing modalities, which include the human voice, industrial vibration signal, and visual sensing data. Extensive evaluation shows the effectiveness of the proposed splitCNN. In particular, the splitCNN achieves significant reduction in the model size and inference time while maintaining similar accuracy, compared with the original CNN model for all three case studies.
{"title":"Split Convolutional Neural Networks for Distributed Inference on Concurrent IoT Sensors","authors":"Jiale Chen, D. V. Le, R. Tan, Daren Ho","doi":"10.1109/ICPADS53394.2021.00014","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00014","url":null,"abstract":"Convolutional neural networks (CNNs) are increasingly adopted on resource-constrained sensors for in-situ data analytics in Internet of Things (IoT) applications. This paper presents a model split framework, namely, splitCNN, in order to run a large CNN on a collection of concurrent IoT sensors. Specifically, we adopt CNN filter pruning techniques to split the large CNN into multiple small-size models, each of which is only sensitive to a certain number of data classes. These class-specific models are deployed onto the resource-constrained concurrent sensors which collaboratively perform distributed CNN inference on their same/similar sensing data. The outputs of multiple models are then fused to yield the global inference result. We apply splitCNN to three case studies with different sensing modalities, which include the human voice, industrial vibration signal, and visual sensing data. Extensive evaluation shows the effectiveness of the proposed splitCNN. In particular, the splitCNN achieves significant reduction in the model size and inference time while maintaining similar accuracy, compared with the original CNN model for all three case studies.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123027539","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}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00117
Jialuo Du, Chenning Li, Zhenge Guo, Zhichao Cao
The screens of our smartphones and laptops display our private information persistently. The term “shoulder surfing” refers to the behavior of unauthorized people peeking at our screens, easily causing severe privacy leakages. Many countermeasures have been used to prevent naked eye-based peeking by reducing the possible peeking distance. However, the risk from modern smartphones with powerful cameras is underestimated. In this paper, we propose SRPeek, a long-distance shoulder surfing attack method using smartphones. Our key observation is that although a single image captured by smartphone cameras is blurred, the attacker can leverage super-resolution (SR) techniques to recover the information from multiple blurry images. We design an end-to-end system deployed on commercial smartphones, including an innovative deep neural network (DNN) architecture, StARe, for efficient multi-image SR. We implement SRPeek in Android and conduct extensive experiments to evaluate its performance. The results demonstrate we can recognize 90% of characters at a distance of 6m with telephoto lenses and 1.8m with common lenses, calling for the vigilance of the Quietly growing shoulder surfing threat.
{"title":"SRPeek: Super Resolution Enabled Screen Peeking via COTS Smartphone","authors":"Jialuo Du, Chenning Li, Zhenge Guo, Zhichao Cao","doi":"10.1109/ICPADS53394.2021.00117","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00117","url":null,"abstract":"The screens of our smartphones and laptops display our private information persistently. The term “shoulder surfing” refers to the behavior of unauthorized people peeking at our screens, easily causing severe privacy leakages. Many countermeasures have been used to prevent naked eye-based peeking by reducing the possible peeking distance. However, the risk from modern smartphones with powerful cameras is underestimated. In this paper, we propose SRPeek, a long-distance shoulder surfing attack method using smartphones. Our key observation is that although a single image captured by smartphone cameras is blurred, the attacker can leverage super-resolution (SR) techniques to recover the information from multiple blurry images. We design an end-to-end system deployed on commercial smartphones, including an innovative deep neural network (DNN) architecture, StARe, for efficient multi-image SR. We implement SRPeek in Android and conduct extensive experiments to evaluate its performance. The results demonstrate we can recognize 90% of characters at a distance of 6m with telephoto lenses and 1.8m with common lenses, calling for the vigilance of the Quietly growing shoulder surfing threat.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128932344","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}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00121
Ye Li, Haitao Zhang, W. Tian, Huadong Ma
In edge computing environment where network connections are often unstable and workload intensity changes frequently, the proper scaling mechanism and service placement strategy based on microservices are needed to ensure the edge services can be provided consistently. However, the common elastic scaling mechanism nowadays is threshold-based responsive scaling and has reaction time in the order of minutes, which is not suitable for delay-sensitive applications in the edge computing environment. Moreover, auto-scaling strategy and service replica placement are considered separately. If the scaled service replicas are misplaced on the edge nodes with limited resources or significant communication latency between upstream and downstream neighbours, the Quality of Service (QoS) cannot be guaranteed even with the auto-scaling mechanism. In this paper, we study the joint optimization of dynamic auto-scaling and adaptive service placement, and define it as a task delay minimization problem while satisfying resource and bandwidth constraints. Firstly, we design a multi-stage auto-scaling model based on workload prediction and performance evaluation of edge nodes to dynamically create an appropriate number of service replicas. Secondly, we propose a Dynamic Adaptive Service Placement (DASP) approach to iteratively place each service replica by using Adaptive Discrete Binary Particle Swarm Optimization (ADBPSO) algorithm. DASP can determine the current optimal placement strategy according to dynamic service replica scaling decision in a short time. The placement results of the current round will guide the optimization of the next cycle iteratively. The experimental evaluation shows that our approach significantly outperforms the existing methods in reducing the average task response time.
{"title":"Joint Optimization of Auto-Scaling and Adaptive Service Placement in Edge Computing","authors":"Ye Li, Haitao Zhang, W. Tian, Huadong Ma","doi":"10.1109/ICPADS53394.2021.00121","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00121","url":null,"abstract":"In edge computing environment where network connections are often unstable and workload intensity changes frequently, the proper scaling mechanism and service placement strategy based on microservices are needed to ensure the edge services can be provided consistently. However, the common elastic scaling mechanism nowadays is threshold-based responsive scaling and has reaction time in the order of minutes, which is not suitable for delay-sensitive applications in the edge computing environment. Moreover, auto-scaling strategy and service replica placement are considered separately. If the scaled service replicas are misplaced on the edge nodes with limited resources or significant communication latency between upstream and downstream neighbours, the Quality of Service (QoS) cannot be guaranteed even with the auto-scaling mechanism. In this paper, we study the joint optimization of dynamic auto-scaling and adaptive service placement, and define it as a task delay minimization problem while satisfying resource and bandwidth constraints. Firstly, we design a multi-stage auto-scaling model based on workload prediction and performance evaluation of edge nodes to dynamically create an appropriate number of service replicas. Secondly, we propose a Dynamic Adaptive Service Placement (DASP) approach to iteratively place each service replica by using Adaptive Discrete Binary Particle Swarm Optimization (ADBPSO) algorithm. DASP can determine the current optimal placement strategy according to dynamic service replica scaling decision in a short time. The placement results of the current round will guide the optimization of the next cycle iteratively. The experimental evaluation shows that our approach significantly outperforms the existing methods in reducing the average task response time.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128951678","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}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00082
Minghao Zhao, Jian Chen, Zhenhua Li
Cloud storage services (e.g., Dropbox) have become pervasive in not only simple file sharing but also advanced collaborative file editing (collaboration for short). Using Dropbox for collaboration is much easier than SVN and Git, thus greatly facilitating common users. In practice, however, many Dropbox users are perplexed by unexpected collaboration conflicts, which severely impair their experiences. Through various benchmark experiments, we unveil the two root causes of collaboration conflicts: 1) Dropbox never locks an edited file during collaboration; 2) Dropbox only guarantees eventual data consistency among the collaborators, significantly aggravating the probability of conflicts. In this paper, we attempt to enable conflict-free collaborations with Dropbox-like cloud storage services. This attempt is empowered by three key findings and measures. First, although the end-to-end sync delay is unpredictable due to eventual consistency, we can always track the latest version of an edited file by actively resorting to the cloud via certain web APIs. Second, although all application-level data is encrypted in Dropbox, we can roughly deduce the sync status from traffic statistics. Third, applying a couple of useful mechanisms (e.g., distributed architecture and data lock) learned from Git, we can effectively and efficiently avoid collaboration conflicts-of course, this requires re-implementing Git mechanisms in cloud storage services with minimum overhead and user interference. Integrating above efforts, we build the ConflictReaper system capable of helping users automatically avoid almost all collaboration conflicts with affordable network and computation overhead.
{"title":"Enabling Conflict-free Collaborations with Cloud Storage Services","authors":"Minghao Zhao, Jian Chen, Zhenhua Li","doi":"10.1109/ICPADS53394.2021.00082","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00082","url":null,"abstract":"Cloud storage services (e.g., Dropbox) have become pervasive in not only simple file sharing but also advanced collaborative file editing (collaboration for short). Using Dropbox for collaboration is much easier than SVN and Git, thus greatly facilitating common users. In practice, however, many Dropbox users are perplexed by unexpected collaboration conflicts, which severely impair their experiences. Through various benchmark experiments, we unveil the two root causes of collaboration conflicts: 1) Dropbox never locks an edited file during collaboration; 2) Dropbox only guarantees eventual data consistency among the collaborators, significantly aggravating the probability of conflicts. In this paper, we attempt to enable conflict-free collaborations with Dropbox-like cloud storage services. This attempt is empowered by three key findings and measures. First, although the end-to-end sync delay is unpredictable due to eventual consistency, we can always track the latest version of an edited file by actively resorting to the cloud via certain web APIs. Second, although all application-level data is encrypted in Dropbox, we can roughly deduce the sync status from traffic statistics. Third, applying a couple of useful mechanisms (e.g., distributed architecture and data lock) learned from Git, we can effectively and efficiently avoid collaboration conflicts-of course, this requires re-implementing Git mechanisms in cloud storage services with minimum overhead and user interference. Integrating above efforts, we build the ConflictReaper system capable of helping users automatically avoid almost all collaboration conflicts with affordable network and computation overhead.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117316411","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}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00031
Ziyang Zhang, Feng Li, Changyao Lin, Shihui Wen, Xiangyu Liu, Jie Liu
Advances in Edge AI make it possible to achieve inference deep learning for emerging applications, e.g., smart transportation and smart city on the edge in real-time. Nowadays, different industry companies have developed several edge AI devices with various architectures. However, it is hard for application users to justify how to choose the appropriate edge-AI, due to the lack of benchmark testing results and testbeds specifically used to evaluate the system performance for those edge-AI systems. In this paper, we attempt to design a benchmark test platform for the edge-AI devices and evaluate six mainstream edge devices that are equipped with different computing powers and AI chip architectures. Throughput, power consumption ratio, and cost-effectiveness are chosen as the performance metrics for the evaluation process. Three classic deep learning workloads: object detection, image classification, and natural language processing are adopted with different batch sizes. The results show that under different batch sizes, compared with traditional edge devices, edge devices equipped with AI chips have out-performance in throughput, power consumption ratio, and cost-effectiveness by 134×, 57×, and 32×, respectively. From system perspective, our work not only demonstrates the effective AI capabilities of those edge AI devices, but also provide suggestions for AI optimization at edge in details.
{"title":"Choosing Appropriate AI-enabled Edge Devices, Not the Costly Ones","authors":"Ziyang Zhang, Feng Li, Changyao Lin, Shihui Wen, Xiangyu Liu, Jie Liu","doi":"10.1109/ICPADS53394.2021.00031","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00031","url":null,"abstract":"Advances in Edge AI make it possible to achieve inference deep learning for emerging applications, e.g., smart transportation and smart city on the edge in real-time. Nowadays, different industry companies have developed several edge AI devices with various architectures. However, it is hard for application users to justify how to choose the appropriate edge-AI, due to the lack of benchmark testing results and testbeds specifically used to evaluate the system performance for those edge-AI systems. In this paper, we attempt to design a benchmark test platform for the edge-AI devices and evaluate six mainstream edge devices that are equipped with different computing powers and AI chip architectures. Throughput, power consumption ratio, and cost-effectiveness are chosen as the performance metrics for the evaluation process. Three classic deep learning workloads: object detection, image classification, and natural language processing are adopted with different batch sizes. The results show that under different batch sizes, compared with traditional edge devices, edge devices equipped with AI chips have out-performance in throughput, power consumption ratio, and cost-effectiveness by 134×, 57×, and 32×, respectively. From system perspective, our work not only demonstrates the effective AI capabilities of those edge AI devices, but also provide suggestions for AI optimization at edge in details.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114236238","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}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00085
Fan Dang, Lingkun Li, Jiajie Chen
As the most widely applied public-key cryptographic algorithm, RSA is now integrated into many low-cost devices such as IoT devices. Due to the limited resource, most low-cost devices only ship a 2048-bit multiplier, making the longest supported private key length as 2048 bits. Unfortunately, 2048-bit RSA keys are gradually considered insecure. Utilizing the existing 2048-bit multiplier is challenging because a 4096-bit message cannot be stored in the multiplier. In this paper, we perform a thorough study of RSA and propose a new method that achieves the 4096-bit RSA cryptography with the existing hardware. We use the Montgomery modular multiplication and the Chinese Remainder Theorem to reduce the computational cost and construct the necessary components to compute the RSA private key operation. To further validate the correctness of the method and evaluate its performance, we implement this method on a micro-controller and build a testbed named CanoKey with three commonly used cryptography protocols. The result shows that our method is over 200x faster than the naive method, a.k.a., software-based big number multiplications.
{"title":"xRSA: Construct Larger Bits RSA on Low-Cost Devices","authors":"Fan Dang, Lingkun Li, Jiajie Chen","doi":"10.1109/ICPADS53394.2021.00085","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00085","url":null,"abstract":"As the most widely applied public-key cryptographic algorithm, RSA is now integrated into many low-cost devices such as IoT devices. Due to the limited resource, most low-cost devices only ship a 2048-bit multiplier, making the longest supported private key length as 2048 bits. Unfortunately, 2048-bit RSA keys are gradually considered insecure. Utilizing the existing 2048-bit multiplier is challenging because a 4096-bit message cannot be stored in the multiplier. In this paper, we perform a thorough study of RSA and propose a new method that achieves the 4096-bit RSA cryptography with the existing hardware. We use the Montgomery modular multiplication and the Chinese Remainder Theorem to reduce the computational cost and construct the necessary components to compute the RSA private key operation. To further validate the correctness of the method and evaluate its performance, we implement this method on a micro-controller and build a testbed named CanoKey with three commonly used cryptography protocols. The result shows that our method is over 200x faster than the naive method, a.k.a., software-based big number multiplications.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114823942","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}
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