Orca-SR: A Real-Time Traffic Engineering Framework leveraging Similarity Joins

被引:0
|
作者
Augustine, Jees [1 ]
Shetiya, Suraj [1 ]
Asudeh, Abolfazl [2 ]
Thirumuruganathan, Saravanan [3 ]
Nazi, Azade [4 ]
Zhang, Nan [5 ]
Das, Gautam [1 ]
Srivastava, Divesh [6 ]
机构
[1] Univ Texas Arlington, Arlington, TX 76019 USA
[2] Univ Illinois, Chicago, IL 60680 USA
[3] HBKU, QCRI, Doha, Qatar
[4] Google Brain, Mountain View, CA USA
[5] Amer Univ, Sch Business, Washington, DC 20016 USA
[6] AT&T Labs Res, Dallas, TX USA
来源
PROCEEDINGS OF THE VLDB ENDOWMENT | 2020年 / 13卷 / 12期
基金
美国国家科学基金会;
关键词
Image reconstruction;
D O I
10.14778/3415478.3415523
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Reconstructing a high dimensional unknown signal, using lower dimensional observations is a challenging problem, known as signal reconstruction problem (SRP), with diverse applications including network traffic engineering, medical image reconstruction, and astronomy. Recently the database community has shown significant advancements in solving the SRP problem efficiently, effectively, and in scale by leveraging database techniques such as similarity joins. In this demo, we demonstrate Orca-SR that highlights the benefits of signal reconstruction in scale by demonstrating real-time network traffic flow analysis on large networks that were not possible before. Orca-SR is a web application that enables a user to generate network flow and load the network for interactive analysis of the impact of different traffic patterns on signal reconstruction.
引用
收藏
页码:2977 / 2980
页数:4
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