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| Content Provider | ACM Digital Library |
|---|---|
| Author | Han, Xianglong Gill, Harjot Nguyen, Cam Loo, Boon Thau Gill, Tanveer Lin, Dong |
| Abstract | This paper presents SCALANYTICS, a declarative platform that supports high-performance application layer analysis of network traffic. SCALANYTICS uses (1) stateful network packet processing techniques for extracting application-layer data from network packets, (2) a declarative rule-based language called ANALOG for compactly specifying analysis pipelines from reusable modules, and (3) a task-stealing architecture for processing network packets at high throughput within these pipelines, by leveraging multi-core processing capabilities in a load-balanced manner without the need for explicit performance profiling. We have developed a prototype of SCALANYTICS that enhances a declarative networking engine with support for ANALOG and various stateful components, integrated with a parallel task-stealing execution model. We evaluate our SCALANYTICS prototype on a wide range of pipelines for analyzing SMTP and SIP traffic, and for detecting malicious traffic flows. Our evaluation on a 16-core machine demonstrate that SCALANYTICS achieves up to 11.4× improvement in throughput compared with the best uniprocessor implementation. Moreover, SCALANYTICS outperforms the Bro intrusion detection system by an order of magnitude when used for analyzing SMTP traffic. |
| Starting Page | 61 |
| Ending Page | 72 |
| Page Count | 12 |
| File Format | |
| ISBN | 9781450319102 |
| DOI | 10.1145/2462902.2462905 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-06-17 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Applications of parallel and distributed computing Programming languages and environments Data-intensive computing |
| Content Type | Text |
| Resource Type | Article |
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