2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)最新文献_第10页

Collaborative Framework of Cloud Transcoding and Distribution Supporting Cost-Efficient Crowdsourced Live Streaming 支持高性价比众包直播的云转码和分发协作框架

2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2021-12-01 DOI: 10.1109/ICPADS53394.2021.00122

Jiannan Zheng, Haitao Zhang, Yilin Jin, Huadong Ma

{"title":"Collaborative Framework of Cloud Transcoding and Distribution Supporting Cost-Efficient Crowdsourced Live Streaming","authors":"Jiannan Zheng, Haitao Zhang, Yilin Jin, Huadong Ma","doi":"10.1109/ICPADS53394.2021.00122","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00122","url":null,"abstract":"With the rapid development of high-speed Internet access and popularization of high-performance smart devices, past decade has witnessed the great development of crowdsourced live streaming (CLS) service. Transcoding and video distribution are essential in CLS service to guarantee viewer engagement. Large CLS systems gradually migrate their services to multi-cloud platforms. However, highly dynamic viewers' requests influence transcoding and CDN distribution decisions, eventually lead to fluctuation in QoE and increase in operational cost. It is challenging for the CLS system to serve viewer's requests in multi-cloud platforms with fluctuation in cloud transcoding and distribution performance. In this paper, we propose a collaborative framework of cloud transcoding and distribution supporting CLS service. First, we define cost model and QoE model in multi-cloud platforms, comprehensively considering cloud transcoding and distribution. Second, we propose a collaborative cost-efficient approach based on multi-agent decision model. We use a G-Greedy exploration approach to learn what actions to take by exploration and exploitation based on the state of current environment. The trace-driven experiments demonstrate that our proposed approach is cost-efficient and QoE-maintained and can reduce operational cost compared with alternatives (5.37%-21.21%) while maintaining QoE of viewers.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"32 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":"131502174","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: 0

TRAN: Task Replication with Guarantee via Multi-armed Bandit TRAN:通过多臂强盗保证任务复制

2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2021-12-01 DOI: 10.1109/ICPADS53394.2021.00048

Yitong Zhou, Bowen Peng, Jingmian Wang, Weiwei Miao, Zeng Zeng, Yibo Jin, Sheng Z. Zhang, Zhuzhong Qian

引用次数: 1

[Copyright notice] (版权)

2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2021-12-01 DOI: 10.1109/icpads53394.2021.00003

引用次数: 0

A Framework for Evaluating BFT 评价BFT的框架

2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2021-12-01 DOI: 10.1109/ICPADS53394.2021.00030

James R. Clavin, Yue Huang, Xin Wang, Pradeep M. Prakash, Sisi Duan, Jianwu Wang, S. Peisert

引用次数: 1

Post-Quantum User Authentication and Key Exchange Based on Consortium Blockchain 基于联盟区块链的后量子用户认证和密钥交换

2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2021-12-01 DOI: 10.1109/ICPADS53394.2021.00089

Shiwei Xu, Ao Sun, Xiaowen Cai, Zhengwei Ren, Yizhi Zhao, Jianying Zhou

{"title":"Post-Quantum User Authentication and Key Exchange Based on Consortium Blockchain","authors":"Shiwei Xu, Ao Sun, Xiaowen Cai, Zhengwei Ren, Yizhi Zhao, Jianying Zhou","doi":"10.1109/ICPADS53394.2021.00089","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00089","url":null,"abstract":"Consortium blockchain has been widely used in many application scenarios, where there is the demand for a universal user authentication and key exchange mechanism for all the application users in the system like Know Your Customer. Since current solutions heavily rely on traditional public-key cryptosystems that are vulnerable to attacks from quantum computers, we design and implement the first post-quantum (PQ) user authentication and key exchange system for consortium blockchain, which is integrated with all the PQ public-key (i.e., signature and encryption/KEM) algorithms in the current round of NIST call for national standard. Furthermore, we also provide chaincodes, related APIs together with client codes for further development. Last but not least, we perform a systematic evaluation on the performance of the system including the consumed time of chaincodes execution and the needed on-chain storage space. Based on the experiment results, we discuss the implications of our findings, which are helpful for the PQ blockchain-based application developers, the undergoing NIST call and the developers of the PQ algorithms.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"8 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":"116944716","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

An Intelligent Game Theory Framework for Detecting Advanced Persistent Threats 一种检测高级持续威胁的智能博弈论框架

2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2021-12-01 DOI: 10.1109/ICPADS53394.2021.00062

Hao Yan, Qianzhen Zhang, Junjie Xie, Ziyue Lu, Sheng Chen, Deke Guo

{"title":"An Intelligent Game Theory Framework for Detecting Advanced Persistent Threats","authors":"Hao Yan, Qianzhen Zhang, Junjie Xie, Ziyue Lu, Sheng Chen, Deke Guo","doi":"10.1109/ICPADS53394.2021.00062","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00062","url":null,"abstract":"The advanced persistent threat (APT) is a stealthy cyber attack perpetrated by a group that gains unauthorized access to a computer network and remains undiscovered to steal specific data and resources. Fast detection and defense of APT attacks are critical tasks in cyber security. Previous works use simple feature extraction and classification methods to distinguish APT information flow from the normal one. However, APT attacks are latent, with very little flow and mixed in many normal information flows. Moreover, APT attacks can adjust their behavior according to the environment, making it challenging to be discovered and extract features. Meanwhile, dynamic information flow tracking (DIFT) is a tool for tracking information flow, which can also adjust the marking strategy according to the environment and is often used to track and detect APT information flow. On the other hand, game theory is a mathematical model that expresses the game of two or more parties. Therefore, this motivates us to model a game theory to solve the above challenge. In this paper, to solve the above obstacles, we propose an intelligent game theory framework named DPS, which models the strategic interaction between APTs and DIFT and aims to get a high reward for DIFT. Our proposed DPS framework utilizes deep reinforcement learning to find the Nash equilibrium. The game model is a nonzero-sum, average reward stochastic game. Specifically, we design a subgraph pruning strategy and deep Q-network to guide the player in exploring new strategies in the information flow graph. Finally, we implement our framework to compute an optimal defender strategy to defend cyber security. Based on 2 real-world datasets, the experiment results demonstrate that the DPS framework can delay APT intrusions under equilibrium in 3 epochs and get a better reward than the Uniform policy.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"52 6 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":"124215227","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: 0

GraFin: An Applicable Graph-based Fingerprinting Approach for Robust Indoor Localization GraFin:一种适用于室内定位的基于图形的指纹识别方法

2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2021-12-01 DOI: 10.1109/ICPADS53394.2021.00099

Han Zheng, Yan Zhang, Lan Zhang, Hao Xia, Shaojie Bai, G. Shen, Tian He, Xiangyang Li

{"title":"GraFin: An Applicable Graph-based Fingerprinting Approach for Robust Indoor Localization","authors":"Han Zheng, Yan Zhang, Lan Zhang, Hao Xia, Shaojie Bai, G. Shen, Tian He, Xiangyang Li","doi":"10.1109/ICPADS53394.2021.00099","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00099","url":null,"abstract":"Wi-Fi fingerprinting using the received signal strength (RSS) of the access point (AP) as a physical signal feature is widely studied in the indoor localization area with various applications. One main problem with fingerprinting based approach is the uncertainty of RSS measurements, which often leads to instability and decline of localization performance. In this work, we propose GraFin, a graph-based fingerprinting approach, to provide accurate and robust indoor localization without tedious site surveys and extra assistant information. The key idea lies in the insight that despite the RSS measurement of one AP at one reference point (RP) can be noisy, the proximity pattern, which describes one AP's relative position to other APs and RPs, is usually more stable. Specifically, GraFin models APs and RPs on a graph based on limited RSS measurements and provides position-aware fingerprints for APs and RPs based on an inductive deep graph model. We evaluate GraFin on a public indoor localization dataset, and the results demonstrate the effectiveness and robustness of our approach. Furthermore, we apply our approach to the arrival-departure time estimation task for instant delivery service. Experiment results on the enterprise dataset from one of the largest instant delivery platforms in China show that GraFin outperforms baseline approaches with significantly lower time estimation error.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"189 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132655285","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: 5

JointCS: Joint Search for Deep Model Compression and Segmentation on Heterogeneous IoT Devices JointCS:异构物联网设备上深度模型压缩和分割的联合搜索

2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2021-12-01 DOI: 10.1109/ICPADS53394.2021.00059

Xinyu Li, Bin Guo, Sicong Liu, Chen Qiu, Yunji Liang, Zhiwen Yu

{"title":"JointCS: Joint Search for Deep Model Compression and Segmentation on Heterogeneous IoT Devices","authors":"Xinyu Li, Bin Guo, Sicong Liu, Chen Qiu, Yunji Liang, Zhiwen Yu","doi":"10.1109/ICPADS53394.2021.00059","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00059","url":null,"abstract":"Deep neural networks (DNNs) play an important role in a variety of intelligent applications (e.g. image classification and target recognition), yet at the cost of heavy computation burden, that makes DNNs difficult to deploy on resource-constrained IoT devices. To solve this problem, there are two categories of model computation adjustment methods: model compression and model segmentation. However, model compression mainly reduces resource consumption at the cost of accuracy while model segmentation reduces resource consumption according to the cost of communication latency. In this paper, we propose Joint Search for Model Compression and Segmentation (JointCS) that highlights the following aspects: 1) we integrate both model compression and model segmentation under an automatic and progressive framework, it simplifies model to fit the different IoT resource requirements. JointCS achieves a series slim models that outperform better both in accuracy and latency. 2) we train a network architecture-aware latency predictor to fast measure the latency of the slimed model on heterogeneous IoT devices. 3) we introduce a search algorithm to select the optimal state in progressively joint search. Finally, we evaluate the performance of our proposed method for image classification on CIFAR datasets comparing with the state-of-the-art approach, the inference time of the proposed method has inference speedup of 12.2 % −30.9 % under the same accuracy.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"2 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":"133080756","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: 0

Two-Layer Traffic Signal Optimization: A Edge-assisted Pressure Balance Approach Based on Cooperative Game 两层交通信号优化:一种基于合作博弈的边缘辅助压力平衡方法

2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2021-12-01 DOI: 10.1109/ICPADS53394.2021.00016

Zhenhua Han, Mingjun Xiao, Haisheng Tan, Guoju Gao

{"title":"Two-Layer Traffic Signal Optimization: A Edge-assisted Pressure Balance Approach Based on Cooperative Game","authors":"Zhenhua Han, Mingjun Xiao, Haisheng Tan, Guoju Gao","doi":"10.1109/ICPADS53394.2021.00016","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00016","url":null,"abstract":"Traffic signal control is essential to efficient transportation networks since it can mitigate traffic congestion significantly. Trial-and-error approach in reinforcement learning will lead to traffic jams, even traffic accidents in the real scene, which is in violation of safety for traffic signal control. Besides, most signal control systems still rely on oversimplified information, which makes item challenging to adapt to dynamic traffic. In this paper, we focus on the edge coordinated optimization of large-scale traffic signal control, and propose a two-layeR edge-assisted pressUre balaNce (RUN) approach based on cooperative game. The external layer utilizes cooperative game to divide the traffic network into multiple coalitions. The internal layer uses pressure control and weighted queue to coordinate actions within each coalition and handle dynamic traffic situations over time. We derive a Pareto stable solution for the multi-intersection signal cooperative game with pressure control, and prove that it is non-superadditive. Moreover, we conduct extensive simulations to verify the significant performances of RUN based on both real data and synthetic data.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"284 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":"132229128","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: 0

Modeling User Interest Changes with Dynamic Differential Graphs for Item Recommendation 基于动态差分图的商品推荐用户兴趣变化建模

2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS) Pub Date : 2021-12-01 DOI: 10.1109/ICPADS53394.2021.00091

Chengyu Zhu, Yanmin Zhu, Xuansheng Lu

{"title":"Modeling User Interest Changes with Dynamic Differential Graphs for Item Recommendation","authors":"Chengyu Zhu, Yanmin Zhu, Xuansheng Lu","doi":"10.1109/ICPADS53394.2021.00091","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00091","url":null,"abstract":"User interests are significant components in recommendation systems. Modeling user interests based on users' historical behaviors is a challenging problem, and many recommendation models have been proposed for user interests modeling, such as long-term and short-term interests modeling. In the real world, users' interests always change over time, however, existing models rarely consider users' interest changes. The purpose of this research is to apply graph neural networks to capture users' interest changes. This research first conducts data analysis on two public datasets, and results show that there are considerable amounts of users with a trend of interest changes. Based on this analysis, we construct user-category dynamic differential graphs, and we design a novel neural network based on dynamic differential graphs to learn users' interest changes representations from dynamic differential graphs. The learned representations are integrated with long-term and short-term interest representations to get users' final representations and make recommendations by getting scores with items. Different types of experiments are conducted to evaluate the performance of our proposed model, and experiment results show that the proposed model outperforms other baseline models.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"52 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":"132233745","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: 0