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A grand spread estimator using a graphics processing unit
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AI
DOI：10.1016/j.jpdc.2013.10.007.png)
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Abstract
En 中文
The spread of a source is defined as the number of distinct destinations to which the source has sent packets during a measurement period. Spread estimation is essential in traffic monitoring, measurement, intrusion detection, to mention a few. To support high speed networking, recent research suggests implementing a spread estimator in fast but small on-chip memory such as SRAM. A state-of-the-art estimator can hold succinct information about 10 million distinct packets using 1 MB SRAM. This implies that a measurement period should restart whenever every 10 million distinct packets fill up the SRAM. Spread estimation is a challenging problem because two spread values from different measurement periods cannot be aggregated to derive the total value. Therefore, current spread estimators have a serious limitation concerning the length of the measurement period because SRAM is available a few megabytes at most. In this paper, we propose a spread estimator that utilizes a large memory space of a graphics processing unit on a commodity PC. The proposed estimator utilizes a 1 GB memory, a hundred times larger than those of current spread estimators, and its throughput is still around 160 Gbps. According to our experiments, the proposed scheme can cover a measurement period of a few dozen hours while the current state-of-the-art can cover only one hour. To the best of our knowledge, this has not been achieved by any spread estimators thus far. (C) 2013 Elsevier Inc. All rights reserved.
Keywords:
Spread estimation
Traffic measurement
Software router
Graphics processing unit
Intrusion detection
Journal
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3.8K
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