research-article
Authors: Jinbin Hu, Yi He, Jin Wang, Wangqing Luo, and Jiawei Huang
ICPP '23: Proceedings of the 52nd International Conference on Parallel Processing
August 2023
Pages 576 - 584
Published: 13 September 2023 Publication History
- 0citation
- 124
- Downloads
Metrics
Total Citations0Total Downloads124Last 12 Months124
Last 6 weeks23
New Citation Alert added!
This alert has been successfully added and will be sent to:
You will be notified whenever a record that you have chosen has been cited.
To manage your alert preferences, click on the button below.
Manage my Alerts
New Citation Alert!
Please log in to your account
Get Access
- Get Access
- References
- Media
- Tables
- Share
Abstract
Many existing load balancing mechanisms work effectively in lossy datacenter networks (DCNs), but they suffer from serious packet reordering in lossless Ethernet DCNs deployed with the hop-by-hop Priority-based Flow Control (PFC). The key reason is that the prior solutions are not able to correctly and timely perceive PFC triggering when making load balancing decisions. Once the forwarding path pauses transmission due to PFC triggering, the packets allocated on it are blocked, inevitably leading to out-of-order packets and retransmission. In this paper, we present a Reordering-robust Load Balancing (RLB) scheme with PFC prediction in lossless DCNs. At its heart, RLB leverages the derivative of ingress queue length to predict PFC triggering and proactively notifies the upstream switches to choose an appropriate rerouting path or perform packet recirculation to avoid reordering. As a building block for existing load balancing mechanisms, we have integrated RLB into Presto, LetFlow, Hermes and DRILL. The test results show that the RLB-enhanced solutions deliver significant performance by avoiding packet reordering. For example, it reduces the 99th percentile flow completion time (FCT) by up to 58%, 67%, 72% and 54% over Presto, LetFlow, Hermes and DRILL, respectively.
Supplementary Material
Appendix (apdx175s2-filled_template_compiled_pdf_outfn.pdf)
- Download
- 277.10 KB
References
[1]
[1]M. Alizadeh, A. Greenberg, D. A. Maltz, et al. Data Center TCP (DCTCP). In Proc. ACM SIGCOMM, 2010.
Digital Library
[2]
[2]Y. Zhu, H. Eran, D. Firestone, C. Guo, M. Lipshteyn, Y. Liron, J. Padhye, S. Raindel, M. H. Yahia, and M. Zhang. Congestion Control for Large-Scale RDMA Deployments. In Proc. ACM SIGCOMM, 2015.
Digital Library
[3]
[3]W. Cheng, K. Qian, W. Jiang, T. Zhang, and F. Ren. Re-architecting Congestion Management in Lossless Ethernet. In Proc. USENIX NSDI, 2020.
[4]
[4]K. Qian, W. Cheng, T. Zhang, and F. Ren. Gentle Flow Control: Avoiding Deadlock in Lossless Networks. In Proc. ACM SIGCOMM, 2019.
Digital Library
[5]
[5]Y. Zhu, M. Ghobadi, V. Misra, and J. Padhye. ECN or Delay: Lessons Learnt from Analysis of DCQCN and TIMELY. In Proc. ACM CoNEXT, 2016.
Digital Library
[6]
[6]C. Guo, H. Wu, Z. Deng, G. Soni, J. Ye, J. Padhye, and M. Lipshteyn. RDMA over Commodity Ethernet at Scale. In Proc. ACM SIGCOMM, 2016.
Digital Library
[7]
[7] W. Bai, A. Agrawal, A. Bhagat, M. Elhaddad, N. John, J. Padhye, et al. Empowering Azure Storage with 100 × 100 RDMA. In Proc. USENIX NSDI, 2023.
[8]
[8]J. Xue, M. U. Chaudhry, B. Vamanan, T. N. Vijaykumar, and M. Thottethodi. Dart: Divide and Specialize for Fast Response to Congestion in RDMA-based Datacenter Networks. IEEE/ACM Transactions on Networking, 28(1):322-335, 2020.
Digital Library
[9]
[9]Y. Lu, G. Chen, B. Li, K. Tan, Y. Xiong, P. Cheng, J. Zhang, E. Chen, and Thomas Moscibroda. multipath Transport for RDMA in Datacenters. In Proc. USENIX NSDI, 2018.
[10]
[10]C. Tian, B. Li, L. Qin, J. Zheng, J. Yang, W. Wang, G. Chen, and W. Dou. P-PFC: Reducing Tail Latency with Predictive PFC in Lossless Data Center Networks. IEEE Transactions on Parallel and Distributed Systems, 31(6):1447-1459, 2020.
[11]
[11]R. Mittal, V. T. Lam, N. Dukkipati, E. Blem, H. Wassel, M. Ghobadi, A. Vahdat, Y. Wang, D. Wetherall, and D. Zats. TIMELY: RTT-based Congestion Control for the Datacenter. In Proc. ACM SIGCOMM, 2015.
Digital Library
[12]
[12]G. Kumar, N. Dukkipati, K. Jang, et al. Swift: Delay is Simple and Effective for Congestion Control in the Datacenter. In Proc. ACM SIGCOMM, 2020.
[13]
[13]M. Alizadeh, T. Edsall, S. Dharmapurikar, R. Vaidyanathan, K. Chu, A. Fingerhut, V. T. Lam, F. Matus, R. Pan, N. Yadav, G. Varghese. CONGA: Distributed Congestion-Aware Load Balancing for Datacenters. In Proc. ACM SIGCOMM, 2014.
Digital Library
[14]
[14]K. He, E. Rozner, K. Agarwal, W. Felter, J. Carter and A. Akellay. Presto: Edge-based Load Balancing for Fast Datacenter networks. In Proc. ACM SIGCOMM, 2015.
Digital Library
[15]
[15]E. Vanini, R. Pan, M. Alizadeh, P. Taheri and T. Edsall. Let It Flow: Resilient Asymmetric Load Balancing with Flowlet Switching. In Proc. USENIX NSDI, 2017.
[16]
[16]H. Zhang, J. Zhang, W. Bai, K. Chen, and M. Chowdhury. Resilient Datacenter Load Balancing in the Wild. In Proc. ACM SIGCOMM, 2017.
Digital Library
[17]
[17]S. Ghorbani, Z. Yang, P. Godfrey, Y. Ganjali, and A. Firoozshahian. DRILL: Micro Load Balancing for Low-Latency Data Center Networks. In Proc. ACM SIGCOMM, 2017.
Digital Library
[18]
[18]The P4.org Architecture Working Group. P416 Portable Switch Architecture (PSA). https://p4.org/p4-spec/docs/PSA-v0.9.0-draft.html#sec-recirculate.
[19]
[19]Z. Liu,K. Chen, H. Wu,S. Hu, Y. Hu, Y. Wang, G. Zhang. Enabling work-conserving bandwidth guarantees for multi-tenant datacenters via dynamic tenant-queue binding. In Proc. IEEE INFOCOM 2018.
Digital Library
[20]
[20]W. Bai, S. Hu, K. Chen, K. Tan, Y. Xiong. One more config is enough: Saving (DC) TCP for high-speed extremely shallow-buffered datacenters.IEEE/ACM Transactions on Networking,2020, 29(2), 489-502.
[21]
[21]IEEE 802.1 Qbb - Priority-based Flow Control. https://1.ieee802.org/dcb/802-1qbb/.
[22]
[22]C. Guo, L. Yuan, D. Xiang, Y. Dang, R. Huang, D. Maltz, Z. Liu, V. Wang, B. Pang, H. Chen, Z. Lin, V. Kurien. Pingmesh: A Large-Scale System for Data Center Network Latency Measurement and Analysis. In Proc. ACM SIGCOMM, 2015.
Digital Library
[23]
[23]N. Katta, M. Hira, C. Kim, A. Sivaraman, and J. Rexford. Hula: Scalable Load Balancing Using Programmable Data Planes. In Proc. ACM Symposium on SDN Research, 2016.
Digital Library
[24]
[24]N. Katta, A. Ghag, M. Hira, I. Keslassy, A. Bergman, C. Kim, and J. Rexford. Clove: Congestion-Aware Load Balancing at the Virtual Edges. In Proc. ACM CoNEXT, 2017.
Digital Library
[25]
[25]W.Bai, S. Hu, K. Chen, K. Tan, Y. Xiong. One More Config is Enough: Saving (DC)TCP for High-speed Extremely Shallow-buffered Datacenters. In Proc. IEEE INFOCOM 2020.
[26]
[26]J. Hu, J. Huang, Z. Li, Y. Li, W. Jiang, K. Chen, J. Wang and T. He. RPO: Receiver-driven Transport Protocol Using Opportunistic Transmission in Data Center. In Proc. IEEE ICNP, 2021.
[27]
[27]J. Hu, J. Huang, Z. Li, J. Wang and T. He. A Receiver-Driven Transport Protocol with High Link Utilization Using Anti-ECN Marking in Data Center Networks. IEEE Transactions on Network and Service Management. 2022.
Digital Library
[28]
[28]J. Zhang, W. Bai, K. Chen. Enabling ECN for datacenter networks with RTT variations. In Proc. ACM CoNEXT, 2019.
Digital Library
[29]
[29]IEEE. 802.1Qau – Congestion Notification. http://www.ieee802.org/1/pages/802.1au.html.
[30]
[30]A. Saeed, V. Gupta, P. Goyal, M. Sharif, R. Pan, M. Ammar, E. Zegura, K. Jang, M. Alizadeh, A. Kabbani, and A. Vahdat. Annulus: A Dual Congestion Control Loop for Datacenter and WAN Traffic Aggregates. In Proc. ACM SIGCOMM, 2020.
Digital Library
[31]
[31]A. Dixit, P. Prakash, Y. C. Hu, and R. R. Kompella. On the Impact of Packet Spraying in Data Center Networks. In Proc. of IEEE INFOCOM, 2013.
[32]
[32]M. Al-Fares, S. Radhakrishnan, B. Raghavan, N. Huang, and A. Vahdat. Hedera: Dynamic Flow Scheduling for Data Center Networks. In Proc. USENIX NSDI, 2010.
Digital Library
[33]
[33]T. Benson, A. Anand, A. Akella, and M. Zhang. MicroTE: Fine Grained Traffic Engineering for Data Centers. In Proc. ACM CoNEXT, 2011.
Digital Library
[34]
[34]J. Hu, J. Huang, W. Lv, W. Li, J. Wang and T. He. TLB: Traffic-aware Load Balancing with Adaptive Granularity in Data Center Networks. In Proc. ACM ICPP, 2019.
Digital Library
[35]
[35]C. Raiciu, S. Barre, C. Pluntke, A. Greenhalgh, D. Wischik, and M. Handley. Improving Datacenter Performance and Robustness with Multipath TCP. In Proc. ACM SIGCOMM, 2011.
Digital Library
[36]
[36]R. Mittal, A. Shpiner, A. Panda, E. Zahavi, A. Krishnamurthy, S. Ratnasamy, and S. Shenker. Revisiting Network Support for RDMA. In Proc. ACM SIGCOMM, 2018.
Digital Library
Index Terms
RLB: Reordering-Robust Load Balancing in Lossless Datacenter Networks
Networks
Network architectures
Network protocols
Network layer protocols
Routing protocols
Network types
Data center networks
Recommendations
- A distributed backoff-channel deflection algorithm with load balancing for optical burst switching networks
Optical burst contention is one of the major factors that cause the burst loss in the optical burst switching (OBS) networks. So far, various contention resolution schemes have been proposed. Among them, the deflection path is more attractive due to its ...
Read More
- Load balancing for heterogeneous traffic in datacenter networks
Abstract
In modern datacenter networks (DCNs), the overwhelming heterogeneous flows have various stringent demands, ranging from delay-sensitive short flows, throughput-sensitive long flows to best-effort flows without deadline. Recently, many ...
Highlights
- We conduct in-depth research to analyze the two main issues brought about by heterogeneous traffic transmission of the same granularity: mixed transmission ...
Read More
- Load Balancing in PFC-Enabled Datacenter Networks
APNet '22: Proceedings of the 6th Asia-Pacific Workshop on Networking
In Priority Flow Control (PFC) enabled datacenter networks (DCNs), PFC is inevitably triggered due to bursty traffic even with end-to-end congestion control. Load balancing as a complementary mechanism to transport protocols can make rerouting ...
Read More
Comments
Information & Contributors
Information
Published In
ICPP '23: Proceedings of the 52nd International Conference on Parallel Processing
August 2023
858 pages
ISBN:9798400708435
DOI:10.1145/3605573
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [emailprotected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 September 2023
Permissions
Request permissions for this article.
Check for updates
Author Tags
- Data Center
- Load Balancing
- Lossless Networks
- Reordering
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- The Scientific Research Fund of Hunan Provincial Education Department
- The Natural Science Foundation of Hunan Province
- The National Natural Science Foundation of China
Conference
ICPP 2023
ICPP 2023: 52nd International Conference on Parallel Processing
August 7 - 10, 2023
UT, Salt Lake City, USA
Acceptance Rates
Overall Acceptance Rate 91 of 313 submissions, 29%
Contributors
Other Metrics
View Article Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
Total Citations
124
Total Downloads
- Downloads (Last 12 months)124
- Downloads (Last 6 weeks)23
Other Metrics
View Author Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in
Full Access
Get this Publication
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML FormatMedia
Figures
Other
Tables