Per-Packet Traffic Measurement in Storage, Computation and Bandwidth Limited Data Plane | IEEE Journals & Magazine | IEEE Xplore

Per-Packet Traffic Measurement in Storage, Computation and Bandwidth Limited Data Plane


Abstract:

Packet level measurement in the data plane provides a microscopic view of the network’s state. Although advances in programmable switches and routers make it possible to ...Show More

Abstract:

Packet level measurement in the data plane provides a microscopic view of the network’s state. Although advances in programmable switches and routers make it possible to measure the Sequence of Packet Lengths and Arrival Times (SPLT) in the data plane, collecting this information remains challenging due to limited storage, processing resources, and bandwidth. To address this issue, we propose MES, which Measures and Encodes Simultaneously the packet length and timestamp when each packet passes through the Network Processor (NP) in the switch/router. We design the packet length compression and timestamp compression algorithms to be lightweight and implement the designed algorithms using simple operations supported by the network processor, while taking into account the computation constraints of the NP. Through extensive experiments on five packet traces, we demonstrate that our MES achieves high precision SPLT measurements (up to 99.82% cosine similarity) while reducing storage and bandwidth overhead by up to 87%. Simulations conducted on the BMV2 P4 software switch demonstrate that our designed SPLT measurement mechanism imposes little impact on network throughput and delay.
Published in: IEEE/ACM Transactions on Networking ( Volume: 32, Issue: 5, October 2024)
Page(s): 3730 - 3742
Date of Publication: 28 May 2024

ISSN Information:

Funding Agency:

References is not available for this document.

I. Introduction

Network measurement plays a crucial role in network operations and management tasks [7], [12], [42]. Packet-level measurement in the data plane provides a microscopic view of the network’s state. Monitoring the Sequence of Packet Lengths and Times (SPLT) has become increasingly important recently [15].

Select All
1.
N. K. Ahmed, J. Neville and R. Kompella, "Network sampling: From static to streaming graphs", ACM Trans. Knowl. Discov. Data, vol. 8, no. 2, pp. 1-56, 2013.
2.
B. Anderson and D. McGrew, "Machine learning for encrypted malware traffic classification: Accounting for noisy labels and non-stationarity", Proc. 23rd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, pp. 1723-1732, Aug. 2017.
3.
P. Bosshart et al., "P4: Programming protocol-independent packet processors", ACM SIGCOMM Comput. Commun. Rev., vol. 44, no. 3, pp. 87-95, Jul. 2014.
4.
P. Bosshart et al., "Forwarding metamorphosis: Fast programmable match-action processing in hardware for SDN", Proc. ACM SIGCOMM Conf. SIGCOMM, pp. 99-110, Aug. 2013.
5.
M. Canini, D. Fay, D. J. Miller, A. W. Moore and R. Bolla, "Per flow packet sampling for high-speed network monitoring", Proc. 1st Int. Commun. Syst. Netw. Workshops, pp. 1-10, Jan. 2009.
6.
W. Chen, Y. Tian, Z. Wei, J. Pan and X. Zhang, "Task scheduling for probabilistic in -Band network telemetry", IEEE/ACM Trans. Netw., vol. 30, no. 6, pp. 2858-2869, Dec. 2022.
7.
X. Chen et al., "Fine-grained queue measurement in the data plane", Proc. 15th Int. Conf. Emerg. Netw. Exp. Technol., pp. 15-29, Dec. 2019.
8.
Z. Chen, G. Cheng, B. Jiang, S. Tang, S. Guo and Y. Zhou, "Length matters: Fast internet encrypted traffic service classification based on multi-PDU lengths", Proc. 16th Int. Conf. Mobility Sens. Netw. (MSN), pp. 531-538, Dec. 2020.
9.
K. Cho, K. Mitsuya and A. Kato, "Traffic data repository at the WIDE project", Proc. USENIX Annu. Tech. Conf. (ATC), pp. 263-270, 2000.
10.
B. Claise, "Cisco systems netflow services export version 9", RFC, vol. 3954, pp. 1-33, 2004, [online] Available: https://doi.org/10.17487/RFC3954.
11.
Behavioral Model (BMV2), Jan. 2018, [online] Available: https://github.com/p4lang/behavioral-model.
12.
Y. Du, H. Huang, Y.-E. Sun, S. Chen and G. Gao, "Self-adaptive sampling for network traffic measurement", Proc. IEEE INFOCOM Conf. Comput. Commun., pp. 1-10, May 2021.
13.
J. Dugan, S. Elliott, B. A. Mah, J. Poskanzer and K. Prabhu, "iPerf—The ultimate speed test tool for TCP UDP and SCTP", 2021, [online] Available: https://iperf.fr/.
14.
C. Estan and G. Varghese, "New directions in traffic measurement and accounting", Proc. Conf. Appl. Technol. Architectures Protocols Comput. Commun., pp. 323-336, 2002.
15.
Y. Fu, H. Xiong, X. Lu, J. Yang and C. Chen, "Service usage classification with encrypted internet traffic in mobile messaging apps", IEEE Trans. Mobile Comput., vol. 15, no. 11, pp. 2851-2864, Nov. 2016.
16.
D. Hancock and J. van der Merwe, "HyPer4: Using P4 to virtualize the programmable data plane", Proc. 12th Int. Conf. Emerg. Netw. Exp. Technol., pp. 35-49, Dec. 2016.
17.
Y. Hou, A. Solin and J. Kannala, "Novel view synthesis via depth-guided skip connections", Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV), pp. 3118-3127, Jan. 2021.
18.
Q. Huang et al., "Toward nearly-zero-error sketching via compressive sensing", Proc. 18th USENIX Symp. Networked Syst. Design Implement., pp. 1027-1044, 2021.
19.
S. Jin, Z. Zhang, K. Chakrabarty and X. Gu, "Failure prediction based on anomaly detection for complex core routers", Proc. IEEE/ACM Int. Conf. Computer-Aided Design (ICCAD), pp. 1-6, Nov. 2018.
20.
K. Kaur, J. Singh and N. S. Ghumman, "Mininet as software defined networking testing platform", Proc. Int. Conf. Commun. Comput. Syst. (ICCCS), pp. 42-139, 2014.
21.
A. H. Lashkari, G. D. Gil, M. S. I. Mamun and A. A. Ghorbani, "Characterization of Tor traffic using time based features", Proc. 3rd Int. Conf. Inf. Syst. Secur. Privacy (ICISSP), pp. 253-262, Feb. 2017.
22.
Y. Lin et al., "NetView: Towards on-demand network-wide telemetry in the data center", Comput. Netw., vol. 180, Oct. 2020.
23.
H. Liu et al., "Escala: Timely elastic scaling of control channels in network measurement", Proc. IEEE INFOCOM Conf. Comput. Commun., pp. 1848-1857, May 2022.
24.
A. S. Maiya and T. Y. Berger-Wolf, "Benefits of bias: Towards better characterization of network sampling", Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, pp. 105-113, Aug. 2011.
25.
G. S. Manku and R. Motwani, "Approximate frequency counts over data streams", Proc. VLDB Endow., vol. 5, no. 12, pp. 1699, 2012.
26.
X.-J. Mao, C. Shen and Y.-B. Yang, "Image restoration using convolutional auto-encoders with symmetric skip connections", arXiv:1606.08921, 2016.
27.
M. H. Mazhar and Z. Shafiq, "Characterizing smart home IoT traffic in the wild", Proc. IEEE/ACM 5th Int. Conf. Internet-Things Design Implement. (IoTDI), pp. 203-215, Sep. 2020.
28.
Y. Mirsky, T. Doitshman, Y. Elovici and A. Shabtai, "Kitsune: An ensemble of autoencoders for online network intrusion detection", Proc. 25th Annu. Netw. Distrib. Syst. Secur. Symp., pp. 1-15, Feb. 2018.
29.
A. Moffat, "Huffman coding", ACM Comput. Surv., vol. 52, no. 4, pp. 1-35, 2019.
30.
N. Msadek, R. Soua and T. Engel, "IoT device fingerprinting: Machine learning based encrypted traffic analysis", Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), pp. 1-8, Apr. 2019.

Contact IEEE to Subscribe

References

References is not available for this document.