Algorithms and data structures to accelerate network analysis
As the sheer amount of computer generated data continues to grow exponentially, new bottlenecks are unveiled that require rethinking our traditional software and hardware architectures. In this paper we present five algorithms and data structures (long queue emulation, lockless bimodal queues, tail...
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| Vydáno v: | Future generation computer systems Ročník 86; číslo C; s. 535 - 545 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
Elsevier B.V
01.09.2018
Elsevier |
| Témata: | |
| ISSN: | 0167-739X, 1872-7115 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | As the sheer amount of computer generated data continues to grow exponentially, new bottlenecks are unveiled that require rethinking our traditional software and hardware architectures. In this paper we present five algorithms and data structures (long queue emulation, lockless bimodal queues, tail early dropping, LFN tables, and multiresolution priority queues) designed to optimize the process of analyzing network traffic. We integrated these optimizations on R-Scope, a high performance network appliance that runs the Bro network analyzer, and present benchmarks showcasing performance speed ups of 5X at traffic rates of 10 Gbps.
•A New queuing algorithm to reduce packet drops in hardware queues.•New lockless bimodal producer–consumer queue to eliminate multi-thread contention.•Algorithm to dynamically shunt traffic while maximizing information entropy.•Lockless hash table with low false negatives to eliminate memory contention overheads.•Multiresolution priority queues to reduce the complexity of a priority queue down to O(1). |
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| Bibliografie: | SC0004400; SC0006343; SC0017184 USDOE Office of Science (SC) |
| ISSN: | 0167-739X 1872-7115 |
| DOI: | 10.1016/j.future.2018.04.034 |