Yutong Liu, L. Kong, Muhammad Hassan, Long Cheng, Guangtao Xue, Guihai Chen
Wireless surveillance systems are rapidly gaining popularity due to their easier deployability and improved performance. However, cameras inside are generating a large amount of data, which brings challenges to the transmission through resource-constrained wireless networks. Observing that most collected consecutive frames are redundant with few objects of interest (OoIs), the filtering of these frames can dramatically relieve the transmission pressure. Additionally, real-world environment may bring shielding or blind areas in videos, which notoriously affects the accuracy of frame analysis. The collaboration between cameras facing at different angles can compensate for such accuracy loss. In this work, we present Litedge, a light-weight edge computing strategy to improve the QoS (i. e., the latency and accuracy) of wireless surveillance systems. Two main modules are designed on edge cameras: (i) the light-weight video compression module for frame filtering, mainly realized by model compression and convolutional acceleration; and (ii) the collaborative validation module for error compensation between the master-slave camera pair. We also implement an enhanced surveillance system prototype from real-time monitoring and pre-processing on edge cameras to the backend data analysis on a server. Experiments based on real-world collected videos show the efficiency of Litedge. It achieves 82% transmission latency reduction with a maximal 0.119s additional processing delay, compared with the full video transmission. Remarkably, 91.28% of redundant frames are successfully filtered out, greatly reducing the transmission burden. Litedge outperforms state-of-the-art light-weight AI models and video compression methods by balancing the QoS balance ratio between accuracy and latency.
{"title":"Litedge","authors":"Yutong Liu, L. Kong, Muhammad Hassan, Long Cheng, Guangtao Xue, Guihai Chen","doi":"10.1145/3326285.3329066","DOIUrl":"https://doi.org/10.1145/3326285.3329066","url":null,"abstract":"Wireless surveillance systems are rapidly gaining popularity due to their easier deployability and improved performance. However, cameras inside are generating a large amount of data, which brings challenges to the transmission through resource-constrained wireless networks. Observing that most collected consecutive frames are redundant with few objects of interest (OoIs), the filtering of these frames can dramatically relieve the transmission pressure. Additionally, real-world environment may bring shielding or blind areas in videos, which notoriously affects the accuracy of frame analysis. The collaboration between cameras facing at different angles can compensate for such accuracy loss. In this work, we present Litedge, a light-weight edge computing strategy to improve the QoS (i. e., the latency and accuracy) of wireless surveillance systems. Two main modules are designed on edge cameras: (i) the light-weight video compression module for frame filtering, mainly realized by model compression and convolutional acceleration; and (ii) the collaborative validation module for error compensation between the master-slave camera pair. We also implement an enhanced surveillance system prototype from real-time monitoring and pre-processing on edge cameras to the backend data analysis on a server. Experiments based on real-world collected videos show the efficiency of Litedge. It achieves 82% transmission latency reduction with a maximal 0.119s additional processing delay, compared with the full video transmission. Remarkably, 91.28% of redundant frames are successfully filtered out, greatly reducing the transmission burden. Litedge outperforms state-of-the-art light-weight AI models and video compression methods by balancing the QoS balance ratio between accuracy and latency.","PeriodicalId":269719,"journal":{"name":"Proceedings of the International Symposium on Quality of Service","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121743303","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}
Xianghao Xu, Fang Wang, Hong Jiang, Yongli Cheng, Yu Hua, D. Feng, Yongxuan Zhang
Big data applications increasingly rely on the analysis of large graphs. In recent years, a number of out-of-core graph processing systems have been proposed to process graphs with billions of edges on just one commodity computer, by efficiently using the secondary storage (e.g., hard disk, SSD). On the other hand, the vertex-centric computing model is extensively used in graph processing thanks to its good applicability and expressiveness. Unfortunately, when implementing vertex-centric model for out-of-core graph processing, the large number of random memory accesses required to construct subgraphs lead to a serious performance bottleneck that substantially weakens cache access locality and thus leads to very long waiting time experienced by users for the computing results. In this paper, we propose an efficient out-of-core graph processing system, LOSC, to substantially reduce the overhead of subgraph construction without sacrificing the underlying vertex-centric computing model. LOSC proposes a locality-optimized subgraph construction scheme that significantly improves the in-memory data access locality of the subgraph construction phase. Furthermore, LOSC adopts a compact edge storage format and a lightweight replication of vertices to reduce I/O traffic and improve computation efficiency. Extensive evaluation results show that LOSC is respectively 6.9x and 3.5x faster than GraphChi and GridGraph, two state-of-the-art out-of-core systems.
{"title":"LOSC","authors":"Xianghao Xu, Fang Wang, Hong Jiang, Yongli Cheng, Yu Hua, D. Feng, Yongxuan Zhang","doi":"10.1145/3326285.3329069","DOIUrl":"https://doi.org/10.1145/3326285.3329069","url":null,"abstract":"Big data applications increasingly rely on the analysis of large graphs. In recent years, a number of out-of-core graph processing systems have been proposed to process graphs with billions of edges on just one commodity computer, by efficiently using the secondary storage (e.g., hard disk, SSD). On the other hand, the vertex-centric computing model is extensively used in graph processing thanks to its good applicability and expressiveness. Unfortunately, when implementing vertex-centric model for out-of-core graph processing, the large number of random memory accesses required to construct subgraphs lead to a serious performance bottleneck that substantially weakens cache access locality and thus leads to very long waiting time experienced by users for the computing results. In this paper, we propose an efficient out-of-core graph processing system, LOSC, to substantially reduce the overhead of subgraph construction without sacrificing the underlying vertex-centric computing model. LOSC proposes a locality-optimized subgraph construction scheme that significantly improves the in-memory data access locality of the subgraph construction phase. Furthermore, LOSC adopts a compact edge storage format and a lightweight replication of vertices to reduce I/O traffic and improve computation efficiency. Extensive evaluation results show that LOSC is respectively 6.9x and 3.5x faster than GraphChi and GridGraph, two state-of-the-art out-of-core systems.","PeriodicalId":269719,"journal":{"name":"Proceedings of the International Symposium on Quality of Service","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129954466","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}
Xiangrui Yang, Zhigang Sun, Junnan Li, Jinli Yan, Tao Li, W. Quan, Donglai Xu, G. Antichi
Theoretical estimates of neutron sputtering yields are in serious disagreement with experiment, unlike the situation with ion sputtering. Possible reasons for the discrepancy are sought without success. It is shown that chunk ejection by neutrons is not due to single neutron events nor to the dynamic interference of cascades. The need for more complete experimental data to guide development of the theory is emphasized.
{"title":"FAST","authors":"Xiangrui Yang, Zhigang Sun, Junnan Li, Jinli Yan, Tao Li, W. Quan, Donglai Xu, G. Antichi","doi":"10.1145/3326285.3329067","DOIUrl":"https://doi.org/10.1145/3326285.3329067","url":null,"abstract":"Theoretical estimates of neutron sputtering yields are in serious disagreement with experiment, unlike the situation with ion sputtering. Possible reasons for the discrepancy are sought without success. It is shown that chunk ejection by neutrons is not due to single neutron events nor to the dynamic interference of cascades. The need for more complete experimental data to guide development of the theory is emphasized.","PeriodicalId":269719,"journal":{"name":"Proceedings of the International Symposium on Quality of Service","volume":"43 3-6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116548723","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":"Proceedings of the International Symposium on Quality of Service","authors":"","doi":"10.1145/3326285","DOIUrl":"https://doi.org/10.1145/3326285","url":null,"abstract":"","PeriodicalId":269719,"journal":{"name":"Proceedings of the International Symposium on Quality of Service","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131830078","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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