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Adaptive Routing for Datacenter Networks Using Ant Colony Optimization

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Algorithms and Architectures for Parallel Processing (ICA3PP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14489))

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Abstract

Modern datacenter networks (DCNs) employ Clos topologies that providing sufficient cross-sectional bandwidth, various load balancing mechanisms are proposed to make full use of multiple parallel paths between end-hosts. Faced with a large number of heterogeneous flows, existing load balancing schemes cannot work well and cause performance degradation, such as latency-sensitive short flows experiencing large tail delay and severe link bandwidth waste due to random rerouting. To solve these issues, we propose an adaptive routing mechanism based on ant colony optimization algorithm (RACO), which adopts different (re)routing strategies for heterogeneous flows. Specifically, RACO uses the improved ant colony optimization algorithm to make optimal rerouting decisions to obtain high throughput and low latency for both elephant flows and mice flows, respectively. The experimental results based on Mininet simulation show that RACO effectively increases the throughput of long flows and reduces the average flow completion time (FCT) of short flows by up to 42% and 61%, respectively, compared with the state-of-the-art load balancing mechanisms.

This work is supported by the National Natural Science Foundation of China (62102046, 62072056), the Natural Science Foundation of Hunan Province (2023JJ50331, 2022JJ30618, 2020JJ2029), the Hunan Provincial Key Research and Development Program (2022GK2019), the Scientific Research Fund of Hunan Provincial Education Department (22B0300).

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References

  1. Wei, W., Gu, H., Wang, K., et al.: Multi-dimensional resource allocation in distributed data centers using deep reinforcement learning. IEEE Trans. Netw. Serv. Manage. 20(2), 1817–1829 (2022)

    Article  Google Scholar 

  2. Li, H., Zhang, Y., Li, D., et al.: URSA: hybrid block storage for cloud-scale virtual disks. In: Proceedings of the Fourteenth EuroSys Conference, pp. 1–17 (2019)

    Google Scholar 

  3. Zhao, Y., Huang, Y., Chen, K., Yu, M., et al.: Joint VM placement and topology optimization for traffic scalability in dynamic datacenter networks. Comput. Netw. 80, 109–123 (2015)

    Article  Google Scholar 

  4. Wang, J., Yuan, D., Luo, W., et al.: Congestion control using in-network telemetry for lossless datacenters. Comput. Mater. Continua. 75(1), 1195–1212 (2023)

    Article  Google Scholar 

  5. Wang, Y., Wang, W., Liu, D., et al.: Enabling edge-cloud video analytics for robotics applications. IEEE Trans. Cloud Comput. 11(2), 1500–1513 (2023)

    Article  MathSciNet  Google Scholar 

  6. Hu, J., Huang, J., Li, Z., Wang, J., He, T.: A receiver-driven transport protocol with high link utilization using anti-ECN marking in data center networks. IEEE Trans. Netw. Serv. Manage. 20(2), 1898–1912 (2023)

    Article  Google Scholar 

  7. Zheng, J., Du, Z., Zha, Z., et al.: Learning to configure converters in hybrid switching data center networks. IEEE/ACM Trans. Netw., 1–15 (2023)

    Google Scholar 

  8. Guo, C., Wu, H., Tan, K., Shi, L., Zhang, Y., Lu, S.: DCell: a scalable and fault-tolerant network structure for data centers. In: Proceedings of ACM SIGCOMM, pp. 75–86 (2008)

    Google Scholar 

  9. Al-Fares, M., Loukissas, A., Vahdat, A.: A scalable, commodity data center network architecture. ACM SIGCOMM Comput. Commun. Rev. 38(4), 63–74 (2008)

    Article  Google Scholar 

  10. Guo, C., Lu, G., Li, D., et al.: BCube: a high performance, server-centric network architecture for modular data centers. In: Proceedings of ACM SIGCOMM, pp. 63–74 (2009)

    Google Scholar 

  11. Hu, J., Huang, J., Lv, W., Zhou, Y., Wang, J., He, T.: CAPS: coding-based adaptive packet spraying to reduce flow completion time in data center. In: Proceedings of IEEE INFOCOM, pp. 2294–2302 (2018)

    Google Scholar 

  12. Hu, J., Huang, J., Lv, W., Li, W., Wang J., He, T.: TLB: Trafficaware load balancing with adaptive granularity in data center networks. In: Proceedings of ACM ICPP, pp. 1–10 (2019)

    Google Scholar 

  13. Hu, J., He, Y., Wang, J., et al.: RLB: Reordering-robust load balancing in lossless datacenter network. In: Proceedings of ACM ICPP (2023)

    Google Scholar 

  14. Hu, J., Zeng, C., Wang, Z., et al.: Enabling load balancing for lossless datacenters. In: Proceedings of IEEE ICNP (2023)

    Google Scholar 

  15. Zhang, H., Zhang, J., Bai, W., Chen, K., Chowdhury, M.: Resilient datacenter load balancing in the wild. In: Proceedings of ACM SIGCOMM, pp. 253–266 (2017)

    Google Scholar 

  16. Liu, Y., Li, W., Qu, W., Qi, H.: BULB: lightweight and automated load balancing for fast datacenter networks. In: Proceedings of ACM ICPP, pp. 1–11 (2022)

    Google Scholar 

  17. Hu, J., Zeng, C., Wang, Z., Xu, H., Huang, J., Chen, K.: Load balancing in PFC-enabled datacenter networks. In: Proceedings of ACM APNet (2022)

    Google Scholar 

  18. Xu, R., Li, W., Li, K., Zhou, X., Qi, H.: DarkTE: towards dark traffic engineering in data center networks with ensemble learning. In: Proceedings of IEEE/ACM IWQOS, pp. 1–10 (2021)

    Google Scholar 

  19. Li, W., Chen, S., Li, K., Qi, H., Xu, R., Zhang, S.: Efficient online scheduling for coflow-aware machine learning clusters. IEEE Trans. Cloud Comput. 10(4), 2564–2579 (2020)

    Article  Google Scholar 

  20. Wang, J., Rao, S., Liu, Y., et al.: Load balancing for heterogeneous traffic in datacenter networks. J. Netw. Comput. Appl., 217 (2023)

    Google Scholar 

  21. Wei, W., Gu, H., Deng, W., et al.: ABL-TC: a lightweight design for network traffic classification empowered by deep learning. Neurocomputing 489, 333–344 (2022)

    Article  Google Scholar 

  22. He, X., Li, W., Zhang, S., Li, K.: Efficient control of unscheduled packets for credit-based proactive transport. In: Proceedings of ICPADS, pp. 593–600 (2023)

    Google Scholar 

  23. Li, W., Yuan, X., Li, K., Qi, H., Zhou, X.: Leveraging endpoint flexibility when scheduling coflows across geo-distributed datacenters. In: Proceedings of IEEE INFOCOM, pp. 873–881 (2018)

    Google Scholar 

  24. Hu, C., Liu, B., Zhao, H., Chen, K., et al.: DISCO: memory efficient and accurate flow statistics for network measurement. In: Proceedings of IEEE ICDCS, pp. 665–674 (2010)

    Google Scholar 

  25. Bai, W., Chen, K., Hu, S., Tan, K., Xiong, Y.: Congestion control for high-speed extremely shallow-buffered datacenter networks. In: Proceedings of ACM APNet, pp. 29–35 (2017)

    Google Scholar 

  26. Cho, I., Jang, K., Han, D.: Credit-scheduled delay-bounded congestion control for datacenters. In: Proceedings of ACM SIGCOMM, pp. 239–252 (2017)

    Google Scholar 

  27. Hu, C., Liu, B., Zhao, H., et al.: Discount counting for fast flow statistics on flow size and flow volume. IEEE/ACM Trans. Netw. 22(3), 970–981 (2013)

    Article  Google Scholar 

  28. Li, Z., Bai, W., Chen, K., et al.: Rate-aware flow scheduling for commodity data center networks. In: Proceedings of IEEE INFOCOM, pp. 1–9 (2017)

    Google Scholar 

  29. Zhang, J., Bai, W., Chen, K.: Enabling ECN for datacenter networks with RTT variations. In Proceedings of ACM the 15th International Conference on Emerging Networking Experiments And Technologies, pp. 233–245 (2020)

    Google Scholar 

  30. Wang, J., Liu, Y., Rao, S., et al.: Enhancing security by using GIFT and ECC encryption method in multi-tenant datacenters. Comput. Mater. Continua. 75(2), 3849–3865 (2023)

    Article  Google Scholar 

  31. Hopps, C.E.: Analysis of an equal-cost multi-path algorithm (2000)

    Google Scholar 

  32. Dixit, A., Prakash, P., Hu, Y. C., Kompella, R.R.: On the impact of packet spraying in data center networks. In: Proceedings of IEEE INFOCOM, pp. 2130–2138 (2013)

    Google Scholar 

  33. Vanini, E., Pan, R., Alizadeh, M., Taheri, P., Edsall, T.: Let it flow: resilient asymmetric load balancing with flowlet switching. In: Proceedings of NSDI, pp. 407–420 (2017)

    Google Scholar 

  34. Lv, J., Wang, X., Ren, K., Huang, M., Li, K.: ACO-inspired information-centric networking routing mechanism. Comput. Netw. 126, 200–217 (2017)

    Article  Google Scholar 

  35. Gupta, A., Garg, R.: Load balancing based task scheduling with ACO in cloud computing. In: Proceedings of IEEE ICCA, pp. 174–179 (2017)

    Google Scholar 

  36. Wang, J., Liu, Y., Rao, S., et al.: A novel self-adaptive multi-strategy artificial bee colony algorithm for coverage optimization in wireless sensor networks. Ad Hoc Netw., 150 (2023)

    Google Scholar 

  37. Katta, N., Hira, M., Kim, C., Sivaraman, A., Rexford, J.: HULA: scalable load balancing using programmable data planes. In: Proceedings of ACM SOSR, pp. 1–12 (2016)

    Google Scholar 

  38. Al-Fares, M., Radhakrishnan, S., Raghavan, B., Huang, N., Vahdat, A.: Hedera: dynamic flow scheduling for data center networks. In: Proceedings of NSDI, pp. 89–92 (2010)

    Google Scholar 

  39. Curtis, A.R., Kim, W., Yalagandula, P.: Mahout: low-overhead datacenter traffic management using end-host-based elephant detection. In: Proceedings of IEEE INFOCOM, pp. 1629–1637 (2011)

    Google Scholar 

  40. Alizadeh, M., Edsall, T., Dharmapurikar, S., et al.: CONGA: distributed congestion-aware load balancing for datacenters. In: Proceedings of ACM SIGCOMM, pp. 503–514 (2014)

    Google Scholar 

  41. Ghorbani, S., Yang, Z., Godfrey, P.B., et al.: DRILL: micro load balancing for low-latency data center networks. In: Proceedings of ACM SIGCOMM, pp. 225–238 (2017)

    Google Scholar 

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Correspondence to Shiming He .

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Hu, J., He, M., Rao, S., Wang, Y., Wang, J., He, S. (2024). Adaptive Routing for Datacenter Networks Using Ant Colony Optimization. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14489. Springer, Singapore. https://doi.org/10.1007/978-981-97-0798-0_17

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  • DOI: https://doi.org/10.1007/978-981-97-0798-0_17

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