Quantitative Network Monitoring with NetQRE

被引:44
|
作者
Yuan, Yifei [1 ]
Lin, Dong [2 ]
Mishra, Ankit [1 ]
Marwaha, Sajal [1 ]
Alur, Rajeev [1 ]
Loo, Boon Thau [1 ]
机构
[1] Univ Penn, Philadelphia, PA 19104 USA
[2] LinkedIn Inc, Mountain View, CA USA
基金
美国国家科学基金会;
关键词
NetQRE; network monitoring language; quantitative regular expression;
D O I
10.1145/3098822.3098830
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In network management today, dynamic updates are required for traffic engineering and for timely response to security threats. Decisions for such updates are based on monitoring network traffic to compute numerical quantities based on a variety of network and application-level performance metrics. Today's state-of-the-art tools lack programming abstractions that capture application or session-layer semantics, and thus require network operators to specify and reason about complex state machines and interactions across layers. To address this limitation, we present the design and implementation of NetQRE, a high-level declarative toolkit that aims to simplify the specification and implementation of such quantitative network policies. NetQRE integrates regular-expression-like pattern matching at flow-level as well as application-level payloads with aggregation operations such as sum and average counts. We describe a compiler for NetQRE that automatically generates an efficient implementation with low memory footprint. Our evaluation results demonstrate that NetQRE allows natural specification of a wide range of quantitative network tasks ranging from detecting security attacks to enforcing application-layer network management policies. NetQRE results in high performance that is comparable with optimized manually-written low-level code and is significantly more efficient than alternative solutions, and can provide timely enforcement of network policies that require quantitative network monitoring.
引用
收藏
页码:99 / 112
页数:14
相关论文
共 50 条
  • [1] Quantitative protein network monitoring in response to DNA damage
    Nishizuka, Satoshi
    Ramalingam, Sundhar
    Spurrier, Brett
    Washburn, Frank L.
    Krishna, Ramya
    Honkanen, Peter
    Young, Lynn
    Shimura, Tsutomu
    Steeg, Patricia S.
    Austin, John
    [J]. JOURNAL OF PROTEOME RESEARCH, 2008, 7 (02) : 803 - 808
  • [2] Design and Implementation of a Quantitative Network Health Monitoring and Recovery System
    Gujral, Harshit
    Sharma, Abhinav
    Jain, Pulkit
    Juneja, Shriya
    Mittal, Sangeeta
    [J]. WIRELESS PERSONAL COMMUNICATIONS, 2022, 125 (01) : 367 - 397
  • [3] Design and Implementation of a Quantitative Network Health Monitoring and Recovery System
    Harshit Gujral
    Abhinav Sharma
    Pulkit Jain
    Shriya Juneja
    Sangeeta Mittal
    [J]. Wireless Personal Communications, 2022, 125 : 367 - 397
  • [4] An Experimental Approach to Network Monitoring Using Quantitative Security Metrics
    El-Hassan, Fadi
    Matrawy, Ashraf
    Seddigh, Nabil
    Nandy, Biswajit
    [J]. JOURNAL OF INFORMATION ASSURANCE AND SECURITY, 2011, 6 (01): : 48 - 62
  • [5] NEST: A quantitative model for detecting emerging trends using a global monitoring expert network and Bayesian network
    Kim, Seonho
    Kim, You-Eil
    Bae, Kuk-Jin
    Choi, Sung-Bae
    Park, Jong-Kyu
    Koo, Young-Duk
    Park, Young-Wook
    Choi, Hyun-Kyoo
    Kang, Hyun-Moo
    Hong, Sung-Wha
    [J]. FUTURES, 2013, 52 : 59 - 73
  • [6] Quantitative decision making for a groundwater monitoring and subsurface contamination early warning network
    Li, Huishu
    Gu, Jianli
    Hanif, Asma
    Dhanasekar, Ashwin
    Carlson, Kenneth
    [J]. SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 683 : 498 - 507
  • [7] Quantitative protein network monitoring for uPA and PAI-1 in breast cancer
    Malinowsky, K.
    Boellner, C.
    Berg, D.
    Walch, A.
    Schuster, T.
    Schmitt, M.
    Becker, K. -F.
    [J]. VIRCHOWS ARCHIV, 2010, 457 (02) : 172 - 172
  • [8] Quantitative Online Monitoring of an Immobilized Enzymatic Network by Ion Mobility-Mass Spectrometry
    Duez, Quentin
    van de Wiel, Jeroen
    van Sluijs, Bob
    Ghosh, Souvik
    Baltussen, Mathieu G.
    Derks, Max T. G. M.
    Roithova, Jana
    Huck, Wilhelm T. S.
    [J]. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, 2024, 146 (30) : 20778 - 20787
  • [9] Quantitative Monitoring of Software
    Henzinger, Thomas A.
    [J]. SOFTWARE VERIFICATION, 2022, 13124 : 3 - 6
  • [10] Quantitative and Approximate Monitoring
    Henzinger, Thomas A.
    Sarac, N. Ege
    [J]. 2021 36TH ANNUAL ACM/IEEE SYMPOSIUM ON LOGIC IN COMPUTER SCIENCE (LICS), 2021,