Abstract
Blockchain technology relies on distributed networks, and Remote Direct Memory Access (RDMA) technology, characterized by ultra-low latency, high bandwidth, has the potential to significantly improve the transmission performance of such networks. RDMA requires a underlying lossless network (usually guaranteed by link-layer flow control called PFC) to fully exploit its performance, wherein congestion control emerges as a key technology in RDMA. However, we find that existing congestion control schemes have limitations in rapidly allocating network bandwidth to eliminate congestion, thus even aggravating side effects of PFC (e.g., head-of-line blocking, unfairness, and deadlock). In this paper, we propose an RDMA congestion control scheme based on receiver-aided network state awareness (RRCC). This research introduces the following key innovations: 1) calculating congestion information through the ECN signals in data packets to achieve a more precise network state sensing method; 2) monitoring the throughput in the receiver side in real-time, and in combination with network state information, periodically adjusting the sender’s rate to achieve rapid rate convergence, accurately preventing and controlling congestion, and addressing issues such as increased flow completion time and slow network congestion recovery. We evaluate RRCC using realistic traffic traces under a three-layer Clos network architecture. The results show that RRCC significantly outperforms existing congestion control schemes in terms of throughput and flow completion time while reducing the side effects of PFC.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lee, X.T., Khan, A., Sen Gupta, S., Ong, Y.H., Liu, X.: Measurements, analyses, and insights on the entire Ethereum blockchain network. In: Proceedings of The Web Conference 2020, pp. 155–166 (2020)
Amiri, M.J., Agrawal, D., El Abbadi, A.: SharPer: sharding permissioned blockchains over network clusters. In: Proceedings of the 2021 International Conference on Management of Data, pp. 76–88 (2021)
Peng, Y., Du, M., Li, F., Cheng, R., Song, D.: FalconDB: blockchain-based collaborative database. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 637–652 (2020)
Xu, C., Zhang, C., Xu, J.: vChain: enabling verifiable Boolean range queries over blockchain databases. In: Proceedings of the 2019 International Conference on Management of Data, pp. 141–158 (2019)
Al-Fares, M., Loukissas, A., Vahdat, A.: A scalable, commodity data center network architecture. ACM SIGCOMM Comput. Commun. Rev. 38(4), 63–74 (2008)
Alibaba-Edu: High-precision congestion control. GitHub Repository (2021). https://github.com/alibaba-edu/High-Precision-Congestion-Control
Alizadeh, M., et al.: Data center TCP (DCTCP). In: Proceedings of the ACM SIGCOMM 2010 Conference, pp. 63–74 (2010)
Broadcom: in-band network telemetry in Broadcom tomahawk 3 (2019). https://www.broadcom.com/company/news/product-releases/2372840
Guo, C., Wu, H., Deng, Z., Soni, G., Lipshteyn, M.: RDMA over commodity ethernet at scale. In: the 2016 Conference (2016)
Krawczyk, R.D., Colombo, T., Neufeld, N., Pisani, F., Valat, S.: Feasibility tests of RoCE v2 for LHCb event building. In: EPJ Web of Conferences, vol. 245, p. 01011. EDP Sciences (2020)
Li, Y., et al.: HPCC: high precision congestion control. In: Proceedings of the ACM Special Interest Group on Data Communication, pp. 44–58 (2019)
Lu, X., Shankar, D., Gugnani, S., Panda, D.: High-performance design of apache spark with RDMA and its benefits on various workloads. In: IEEE International Conference on Big Data (2016)
Marinos, I., Watson, R., Handley, M.: Network stack specialization for performance. In: ACM Conference on SIGCOMM (2014)
Mittal, R., et al.: TIMELY: RTT-based congestion control for the datacenter. In: ACM Conference on Special Interest Group on Data Communication, pp. 537–550 (2015)
Mittal, R., et al.: Revisiting network support for RDMA. In: Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, pp. 313–326 (2018)
Qiu, X., Chen, W., Tang, B., Liang, J., Dai, H.N., Zheng, Z.: A distributed and privacy-aware high-throughput transaction scheduling approach for scaling blockchain. IEEE Trans. Dependable Secure Comput. 01, 1–15 (2022)
Roy, A., Zeng, H., Bagga, J., Porter, G., Snoeren, A.C.: Inside the social network’s (datacenter) network. In: Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication, pp. 123–137 (2015)
Shpiner, A., et al.: RoCE rocks without PFC: detailed evaluation. In: Proceedings of the Workshop on Kernel-Bypass Networks, pp. 25–30 (2017)
Zhu, Y., et al.: Congestion control for large-scale RDMA deployments. ACM SIGCOMM Comput. Commun. Rev. 45(4), 523–536 (2015)
Zhu, Y., Ghobadi, M., Misra, V., Padhye, J.: ECN or Delay: lessons learnt from analysis of DCQCN and TIMELY. In: Proceedings of the 12th International on Conference on emerging Networking EXperiments and Technologies, pp. 313–327 (2016)
Acknowledgment
This work is supported by the National Key R &D Program of China (2020YFB1805500) and NSFC (62032003, U21B2016 and 62192784).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, T., Zhang, Y., Zhou, A., Li, R., Zhao, K., Wang, S. (2024). Congestion Control with Receiver-Aided Network Status Awareness in RDMA Transmission. In: Chen, J., Wen, B., Chen, T. (eds) Blockchain and Trustworthy Systems. BlockSys 2023. Communications in Computer and Information Science, vol 1897. Springer, Singapore. https://doi.org/10.1007/978-981-99-8104-5_17
Download citation
DOI: https://doi.org/10.1007/978-981-99-8104-5_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8103-8
Online ISBN: 978-981-99-8104-5
eBook Packages: Computer ScienceComputer Science (R0)