faaShark: An End-to-End Network Traffic Analysis System Atop Serverless Computing
The prosperity of the Internet has made network traffic analysis increasingly indispensable in network operation. With the development of machine learning, more researchers and engineers are using deep learning models for network traffic analysis. However, the rapidly growing data size and model com...
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| Vydáno v: | IEEE transactions on network science and engineering Ročník 11; číslo 3; s. 2473 - 2484 |
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| Hlavní autoři: | , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Piscataway
IEEE
01.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2327-4697, 2334-329X |
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| Abstract | The prosperity of the Internet has made network traffic analysis increasingly indispensable in network operation. With the development of machine learning, more researchers and engineers are using deep learning models for network traffic analysis. However, the rapidly growing data size and model complexity make resource scheduling a serious limitation, which is why cloud computing services are typically required for network analysis. To leverage the advantages of serverless platforms, we propose faaShark , an end-to-end network traffic analysis system based on a serverless computing platform. faaShark adapts distributed training to fully utilize the flexibility of serverless platforms. Additionally, we design a cold start optimization algorithm to reduce the hit rate of cold start when serving pretrained models to handle network analysis requests. Our extensive experiments evaluate the impact of several parameters of distributed training and confirm the effectiveness of our cold start optimization algorithm when building such a network analysis system atop serverless computing frameworks. |
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| AbstractList | The prosperity of the Internet has made network traffic analysis increasingly indispensable in network operation. With the development of machine learning, more researchers and engineers are using deep learning models for network traffic analysis. However, the rapidly growing data size and model complexity make resource scheduling a serious limitation, which is why cloud computing services are typically required for network analysis. To leverage the advantages of serverless platforms, we propose faaShark , an end-to-end network traffic analysis system based on a serverless computing platform. faaShark adapts distributed training to fully utilize the flexibility of serverless platforms. Additionally, we design a cold start optimization algorithm to reduce the hit rate of cold start when serving pretrained models to handle network analysis requests. Our extensive experiments evaluate the impact of several parameters of distributed training and confirm the effectiveness of our cold start optimization algorithm when building such a network analysis system atop serverless computing frameworks. |
| Author | Chen, Xinglei Guan, Haibing Pan, Shanxing Jin, Lingxiao Zhao, Hongyu Gao, Honghao Cai, Zinuo Wan, Shaohua Ma, Ruhui |
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| SubjectTerms | Algorithms Analytical models Cloud computing cold start Communications traffic Computational modeling Deep learning Design optimization distributed training Load modeling Machine learning Network analysis Network traffic analysis Optimization algorithms Resource scheduling Serverless computing Telecommunication traffic Traffic analysis Traffic models Training |
| Title | faaShark: An End-to-End Network Traffic Analysis System Atop Serverless Computing |
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