2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)最新文献

{"title":"LBNN: Perceiving the State Changes of a Core Telecommunications Network via Linear Bayesian Neural Network","authors":"Yanying Lin, Kejiang Ye, Ming Chen, Naitian Deng, Tailin Wu, Chengzhong Xu","doi":"10.1109/ICPADS51040.2020.00020","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00020","url":null,"abstract":"The core network is the most basic facility in the entire telecommunications network, which is consists of large number of routers, switches and firewalls. Network management like re-planning routes or adjusting policies is very important to avoid failures. However, the timing of intervention is very challenging. Too early intervention will incur unnecessary overheads, and too late intervention will cause serious disaster. In this paper, we analyzed a large data set from a real-world core telecommunications network and proposed Linear Bayesian Neural Networks (LBNN)11Code available at https://github.com/YanyingLin/Lbnn to perceive the core network state changes and make decisions about network intervention. In particular, we considered three aspects of complexity, including the weight of the mutual effect between devices, the dependence on the time dimension of the network states, and the randomness of the network state changes. The entire model is extended to a probability model based on the Bayesian framework to better capture the randomness and variability of the data. Experimental results on real-world data set show that LBNN achieves very high detection accuracy, with an average of 92.1%.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124279062","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":"Intelligent Detection Algorithm Against UAVs' GPS Spoofing Attack","authors":"Shenqing Wang, Jian Wang, Chunhua Su, Xinshu Ma","doi":"10.1109/ICPADS51040.2020.00058","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00058","url":null,"abstract":"Unmanned Aerial Vehicle (UAV) technology is more and more widely used in the field of civil and military information acquisition. GPS plays the most critical part of UAVs' navigation and positioning. However, since the communication channel of the GPS signals is open, attackers can disguise as real GPS signals to launch GPS spoofing attacks on civilian UAVs. At present, the detection schemes for GPS spoofing attacks can be divided into three categories respectively based on encryption and digital signatures, the characteristics of the GPS signal and various external characteristics of UAVs. However, there are some problems in these methods, such as low computing efficiency, difficulty in equipment upgrading, and limited application scenarios. To solve these problems, we propose a new GPS spoofing attack detection method based on Long Short-Term Memory (LSTM) which is a machine learning algorithm. In order to improve the detection ratio, after the machine learning algorithm, we let the UAVs fly according to the path of a specific shape to accurately detect GPS spoofing attacks. This is also the first time machine learning has been used to detect GPS spoofing attacks. According to our algorithm, we can detect GPS spoofing attacks accurately and quickly in a short time. This paper describes in detail the algorithm we proposed to resist GPS spoofing attacks, and the corresponding experiments are carried out in the simulation environment. The experimental results show that our method can quickly and accurately detect UAV GPS spoofing attacks without requiring upgrades to existing equipment.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129207919","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":"Dynamic-Static-based Spatiotemporal Multi-Graph Neural Networks for Passenger Flow Prediction","authors":"Jingyan Ma, Jingjing Gu, Qiang Zhou, Qiuhong Wang, Ming Sun","doi":"10.1109/ICPADS51040.2020.00095","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00095","url":null,"abstract":"Various sensing and computing technologies have gradually outlined the future of the intelligent city. Passenger flow prediction of public transports has become an important task in Intelligent Transportation System (ITS), which is the prerequisite for traffic management and urban planning. There exist many methods based on deep learning for learning the spatiotemporal features from high non-linearity and complexity of traffic flows. However, they only utilize temporal correlation and static spatial correlation, such as geographical distance, which is insufficient in the mining of dynamic spatial correlation. In this paper, we propose the Dynamic-Static-based Spatiotemporal Multi-Graph Neural Networks model (DSSTMG) for predicting traffic passenger flows, which can concurrently incorporate the temporal and multiple static and dynamic spatial correlations. Firstly, we exploit the multiple static spatial correlations by multi-graph fusion convolution operator, including adjacent relation, station functional zone similarity and geographical distance. Secondly, we exploit the spatial dynamic correlations by calculating the similarity between the flow pattern of stations over a period of time, and build the dynamic spatial attention. Moreover, we use time attention and encoder-decoder architecture to capture temporal correlation. The experimental results on two realworld datasets show that the proposed DSSTMG outperforms state-of-the-art methods.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124177292","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":"A protocol-independent container network observability analysis system based on eBPF","authors":"Chang Liu, Zhengong Cai, Bingshen Wang, Zhimin Tang, Jiaxu Liu","doi":"10.1109/ICPADS51040.2020.00099","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00099","url":null,"abstract":"Technologies such as microservices, containerization and Kubernetes in cloud-native environments make large-scale application delivery easier and easier, but problem troubleshooting and fault location in the face of massive applications is becoming more and more complex. Currently, the data collected by the mainstream monitoring technologies based on sampling is difficult to cover all anomalies, and the kernel's lack of observability also makes it difficult to monitor more detailed data in container environments such as the Kuber-netes platform. In addition, most of the current technology solutions use tracing and application performance monitoring tools (APMs), but these technologies limit the language used by the application and need to be invasive into the application code, many scenarios require more general network performance detection diagnostic methods that do not invade the user application. In this paper, we propose to introduce network monitoring at the kernel level below the application for the Kubernetes cluster in Alibaba container service. By nonintrusive collection of user application L7/L4 layer network protocol interaction information based on eBPF, data collection of more than 10M throughputs per second can be achieved without modifying any kernel and application code, while the impact on the system application is less than 1%. It also uses machine learning methods to analyze and diagnose application network performance and problems, analyze network performance bottlenecks and locate specific instance information for different applications, and realize protocol-independent network performance problem location and analysis.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"17 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113979627","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":"Adaptive DNN Partition in Edge Computing Environments","authors":"Weiwei Miao, Zeng Zeng, Lei Wei, Shihao Li, Chengling Jiang, Zhen Zhang","doi":"10.1109/ICPADS51040.2020.00097","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00097","url":null,"abstract":"Deep Neural Network (DNN) has been applied widely nowadays, making remarkable achievements in a wide variety of research fields. With the improvement of the accuracy requirements for the inference results, the topology of DNN tends to be more and more complex, evolving from chain topology to directed acyclic graph (DAG) topology, which leads to the huge amount of computation. For those end devices which have limited computing resources, the delay of running DNN models independently may be intolerable. As a solution, edge computing can make use of all available devices in the edge computing environments comprehensively to run DNN inference tasks, so as to achieve the purpose of acceleration. In this case, how to split DNN inference task into several small tasks and assign them to different edge devices is the central issue. This paper proposes a load-balancing algorithm to split DNN with DAG topology adaptively according to the environment. Extensive experimental results show the the propose adaptive algorithm can effectively accelerate the inference speed.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131205436","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":"A similarity clustering-based deduplication strategy in cloud storage systems","authors":"Saiqin Long, Zhetao Li, Zihao Liu, Qingyong Deng, Sangyoon Oh, N. Komuro","doi":"10.1109/ICPADS51040.2020.00015","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00015","url":null,"abstract":"Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"24 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123512374","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":"A Solution to Data Accessibility Across Heterogeneous Blockchains","authors":"Zhihui Wu, Yang Xiao, Enyuan Zhou, Qingqi Pei, Quan Wang","doi":"10.1109/ICPADS51040.2020.00062","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00062","url":null,"abstract":"Cross-heterogeneous blockchain interactions have been attracting much attention due to their application in depository blockchains mutual access and cross-blockchain identity authentication. Trusted access across heterogeneous chains is gradually becoming a hot challenge. In order to ensure cross-blockchain trusted access, the majority of the current works focus on on-chain notaries and the relay chain model. However, these methods have the following drawbacks: 1) notaries on the chain are more vulnerable to attacks due to their high degree of centralization, which causes off-chain users to lose their trust and thus exacerbates the off-chain trust crisis; 2) although the relay model involves multiple parties in maintenance and supervision and enjoys a more robust trust, the paticipatant nodes are relatively fixed, which impose a terrible dilemma that invalid nodes cannot participate in consensus formation in a timely manner, thus progressively disrupting the connectivity of the relay across heterogeneous chains and eventually reducing the rate of trusted mutual access. In this article, we propose a novel general framework for cross-heterogeneous blockchain communication based on a periodical committee rotation mechanism to support information exchange of diverse transactions across multiple heterogeneous blockchain systems. Connecting heterogeneous blockchains through committees has a more robust trust than the notary method. In order to eliminate the impact of downtime nodes in a timely manner, we periodically reorganize the committee and give priority to replacing downed nodes to ensure the reliability of the system. In addition, a message-oriented verification mechanism is designed to improve the rate of trusted intervisit across heterogeneous chains. We have built a prototype of the scheme and conducted simulation experiments on the current mainstream blockchain for message exchange across heterogeneous chains. The results show that our solution has a good performance both in inter-chain access rate and system stability.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123939099","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":"Themis: Malicious Wear Detection and Defense for Persistent Memory File Systems","authors":"Wenbin Wang, Chaoshu Yang, Runyu Zhang, Shun Nie, Xianzhang Chen, Duo Liu","doi":"10.1109/ICPADS51040.2020.00028","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00028","url":null,"abstract":"The persistent memory file systems can significantly improve the performance by utilizing the advanced features of emerging Persistent Memories (PMs). Unfortunately, the PMs have the problem of limited write endurance. However, the design of persistent memory file systems usually ignores this problem. Accordingly, the write-intensive applications, especially for the malicious wear attack virus, can damage underlying PMs quickly by calling the common interfaces of persistent memory file systems to write a few cells of PM continuously. Which seriously threat to the data reliability of file systems. However, existing solutions to solve this problem based on persistent memory file systems are not systematic and ignore the unlimited write endurance of DRAM. In this paper, we propose a malicious wear detection and defense mechanism for persistent memory file systems, called Themis, to solve this problem. The proposed Themis identifies the malicious wear attack according to the write traffic and the set lifespan of PM. Then, we design a wear-leveling scheme and migrate the writes of malicious wear attackers into DRAM to improve the lifespan of PMs. We implement the proposed Themis in Linux kernel based on NOVA, a state-of-the-art persistent memory file system. Compared with DWARM, the state-of-the-art and wear-aware memory management technique, experimental results show that Themis can improve 5774× lifetime of PM and 1.13× performance, respectively.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129985751","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":"A Fair Task Assignment Strategy for Minimizing Cost in Mobile Crowdsensing","authors":"Yujun Liu, Yongjian Yang, E. Wang, Wenbin Liu, Dongming Luan, Xiaoying Sun, Jie Wu","doi":"10.1109/ICPADS51040.2020.00016","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00016","url":null,"abstract":"Mobile CrowdSensing (MCS) is a promising paradigm that recruits mobile users to cooperatively perform various sensing tasks. When assigning tasks to users, most existing works only consider the fairness of users, i.e., the user's processing ability, with the goal of minimizing the assignment cost. However, in this paper, we argue that it is necessary to not only give full use of all the users' ability to process the tasks (e.g., not exceeding the maximum capacity of each user while also not letting any user idle too long), but also satisfy the assignment frequency of all corresponding tasks (e.g., how many times each task should be assigned within the whole system time) to ensure a long-term, double-fair and stable participatory sensing system. Hence, to solve the task assignment problem which aims to reasonably assign tasks to users with limited task processing ability while ensuring the assignment frequency, we first model the two fairness constraints simultaneously by converting them to user processing queues and task virtual queues, respectively. Then, we propose a Fair Task Assignment Strategy (FTAS) utilizing Lyapunov optimization and we provide the proof of the optimality for the proposed assignment strategy to ensure that there is an upper bound to the total assignment cost and queue backlog. Finally, extensive simulations have been conducted over three real-life mobility traces: Changchun/taxi, Epfl/mobility, and Feeder. The simulation results prove that the proposed strategy can achieve a trade-off between the objective of minimizing the cost and the fairness of tasks and users compared with other baseline approaches.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125372167","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":"Accelerating Deep Learning Tasks with Optimized GPU-assisted Image Decoding","authors":"Lipeng Wang, Qiong Luo, Shengen Yan","doi":"10.1109/ICPADS51040.2020.00045","DOIUrl":"https://doi.org/10.1109/ICPADS51040.2020.00045","url":null,"abstract":"In computer vision deep learning (DL) tasks, most of the input image datasets are stored in the JPEG format. These JPEG datasets need to be decoded before DL tasks are performed on them. We observe two problems in the current JPEG decoding procedures for DL tasks: (1) the decoding of image entropy data in the decoder is performed sequentially, and this sequential decoding repeats with the DL iterations, which takes significant time; (2) Current parallel decoding methods under-utilize the massive hardware threads on GPUs. To reduce the image decoding time, we introduce a pre-scan mechanism to avoid the repeated image scanning in DL tasks. Our pre-scan generates boundary markers for entropy data so that the decoding can be performed in parallel. To cooperate with the existing dataset storage and caching systems, we propose two modes of the pre-scan mechanism: a compatible mode and a fast mode. The compatible mode does not change the image file structure so pre-scanned files can be stored back to disk for subsequent DL tasks. In comparison, the fast mode crafts a JPEG image into a binary format suitable for parallel decoding, which can be processed directly on the GPU. Since the GPU has thousands of hardware threads, we propose a fine-grained parallel decoding method on the pre-scanned dataset. The fine-grained parallelism utilizes the GPU effectively, and achieves speedups of around 1.5× over existing GPU-assisted image decoding libraries on real-world DL tasks.","PeriodicalId":196548,"journal":{"name":"2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131533440","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}