2022IEEE Transactions on Network and Service ManagementRequires access

LossDetection: Real-Time Packet Loss Monitoring System for Sampled Traffic Data

Hua Wu, Ya Liu, Shanshan Ni, Guang Cheng, Xiaoyan Hu

Open publisher page 17 citations

Abstract

Packet loss is common in networks, which leads to network quality of service degradation. Packet loss is an essential and concerning symptom when the quality of service is degraded. Therefore, real-time passive packet loss detection is conducive to estimating network services. Existing passive packet loss detection methods mainly study the packet loss for TCP using header information from full traffic. However, it cannot infer packet loss status for UDP due to its limited header information and is too costly to perform full acquisition in real networks. To address these problems, we propose a framework called LossDetection based on packet sampling and Feature-Sketch to detect packet loss in real time for both TCP and UDP. The result shows that our methodology can detect packet loss with an accuracy of 98%-100% at a sampling rate of 1/16. Furthermore, our extensive evaluation demonstrates that LossDetection is easy to implement in a software router and achieves low memory and detection latency while providing real-time information about packet loss.

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What this paper is about

Packet loss is common in networks, which leads to network quality of service degradation. Packet loss is an essential and concerning symptom when the quality of service is degraded. Therefore, real-time passive packet loss detection is conducive to estimating network services. Existing passive packet loss detection methods mainly study the packet loss for TCP using header information from full traffic. However, it cannot infer packet loss status for UDP due to its limited header information and is too costly to perform full acquisition in real networks. To address these problems, we propose a framework called LossDetection based on packet sampling and Feature-Sketch to detect packet loss in real time for both TCP and UDP. The result shows that our methodology can detect packet loss with an accuracy of 98%-100% at a sampling rate of 1/16. Furthermore, our extensive evaluation demonstrates that LossDetection is easy to implement in a software router and achieves low memory and detection latency while providing real-time information about packet loss.

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OpenAlex reports 17 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Packet loss is common in networks, which leads to network quality of service degradation. Packet loss is an essential and concerning symptom when the quality of service is degraded. Therefore, real-time passive packet loss detection is conducive to estimating network services. Existing passive packet loss detection methods mainly study the packet loss for TCP using header information from full traffic. However, it cannot infer packet loss status for UDP due to its limited header information and is too costly to perform full acquisition in real networks. To address these problems, we propose a framework called LossDetection based on packet sampling and Feature-Sketch to detect packet loss in real time for both TCP and UDP. The result shows that our methodology can detect packet loss with an accuracy of 98%-100% at a sampling rate of 1/16. Furthermore, our extensive evaluation demonstrates that LossDetection is easy to implement in a software router and achieves low memory and detection latency while providing real-time information about packet loss.

Key concepts: Packet loss, Computer science, Packet analyzer, Computer network, Processing delay, Real-time computing, End-to-end delay, Header

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