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A Task Scheduling Scheme in the DC Access Network

  • Yuchao Zhang
  • Ke Xu
Chapter
  • 67 Downloads

Abstract

State-of-the-art microservices are starting to get more and more attention in recent years. A broad spectrum of online interactive applications are now programmed to service chains on cloud, seeking for better system scalability and lower operation cost. Different from the conventional batch jobs, most of these applications are composed of multiple stand-alone services that communicate with each other. These step-by-step operations unavoidably introduce higher latency to the delay-sensitive chained services.

In this chapter, we aim at designing an optimization approach to reduce the latency of chained services. Specifically, presenting the measurement and analysis of chained services on Baidu’s cloud platform, our real-world trace indicates that these chained services are suffering from significantly high latency because they are mostly handled by different queues on cloud servers for multiple times. Such a unique feature, however, introduces significant challenge to optimize microservice’s overall queueing delay. To address this problem, we propose a delay-guaranteed approach to accelerate the overall queueing of chained services while obtaining fairness across all the workloads. Our real-world deployments on Baidu shows that the proposed design can successfully reduce the latency of chained services by 35% with minimal affect to other workloads.

References

  1. 1.
    Wang, H., Shea, R., Ma, X., Wang, F., Liu, J.: On design and performance of cloud-based distributed interactive applications. In: 2014 IEEE 22nd International Conference on Network Protocols (ICNP), pp. 37–46. IEEE (2014)Google Scholar
  2. 2.
    Pujol, E., Richter, P., Chandrasekaran, B., Smaragdakis, G., Feldmann, A., Maggs, B.M., Ng, K.-C.: Back-office web traffic on the internet. In: Proceedings of the 2014 Conference on Internet Measurement Conference, pp. 257–270. ACM (2014)Google Scholar
  3. 3.
    Zaki, Y., Chen, J., Potsch, T., Ahmad, T., Subramanian, L.: Dissecting web latency in ghana. In: Proceedings of the 2014 Conference on Internet Measurement Conference, pp. 241–248. ACM (2014)Google Scholar
  4. 4.
    Yue, K., Wang, X.-L., Zhou, A.-Y., et al.: Underlying techniques for web services: a survey. J. Softw. 15(3), 428–442 (2004)zbMATHGoogle Scholar
  5. 5.
    Seung, Y., Lam, T., Li, L.E., Woo, T.: Cloudflex: seamless scaling of enterprise applications into the cloud. In: INFOCOM, 2011 Proceedings IEEE, pp. 211–215. IEEE (2011)Google Scholar
  6. 6.
    Xu, K., Zhang, Y., Shi, X., Wang, H., Wang, Y., Shen, M.: Online combinatorial double auction for mobile cloud computing markets. In: Performance Computing and Communications Conference (IPCCC), 2014 IEEE International, pp. 1–8. IEEE (2014)Google Scholar
  7. 7.
    Guo, J., Liu, F., Zeng, D., Lui, J.C., Jin, H.: A cooperative game based allocation for sharing data center networks. In: INFOCOM, 2013 Proceedings IEEE, pp. 2139–2147. IEEE (2013)Google Scholar
  8. 8.
    Vik, K.-H., Halvorsen, P., Griwodz, C.: Multicast tree diameter for dynamic distributed interactive applications. In: INFOCOM 2008. The 27th Conference on Computer Communications. IEEE. IEEE (2008)Google Scholar
  9. 9.
    Webb, S.D., Soh, S., Lau, W.: Enhanced mirrored servers for network games. In: Proceedings of the 6th ACM SIGCOMM Workshop on Network and System Support for Games, pp. 117–122. ACM (2007)Google Scholar
  10. 10.
    Shao, Z., Jin, X., Jiang, W., Chen, M., Chiang, M.: Intra-data-center traffic engineering with ensemble routing. In: INFOCOM, 2013 Proceedings IEEE, pp. 2148–2156. IEEE (2013)Google Scholar
  11. 11.
    Sivaraman, A., Cheung, A., Budiu, M., Kim, C., Alizadeh, M., Balakrishnan, H., Varghese, G., McKeown, N., Licking, S.: Packet transactions: high-level programming for line-rate switches. In: ACM SIGCOMM, pp. 15–28 (2016)Google Scholar
  12. 12.
    McKeown, N., Anderson, T., Balakrishnan, H., Parulkar, G., Peterson, L., Rexford, J., Shenker, S., Turner, J.: Openflow: enabling innovation in campus networks. ACM SIGCOMM 38(2), 69–74 (2008)CrossRefGoogle Scholar
  13. 13.
    McKeown, N.: Software-defined networking. INFOCOM Keynote Talk 17(2), 30–32 (2009)Google Scholar
  14. 14.
    Hong, C.-Y., Kandula, S., Mahajan, R., Zhang, M., Gill, V., Nanduri, M., Wattenhofer, R.: Achieving high utilization with software-driven WAN. In: ACM SIGCOMM, pp. 15–26 (2013)Google Scholar
  15. 15.
    Jain, S., Kumar, A., Mandal, S., Ong, J., Poutievski, L., Singh, A., Venkata, S., Wanderer, J., Zhou, J., Zhu, M., et al.: B4: experience with a globally-deployed software defined WAN. ACM SIGCOMM 43(4), 3–14 (2013)CrossRefGoogle Scholar
  16. 16.
    Zhang, H., Chen, L., Yi, B., Chen, K., Chowdhury, M., Geng, Y.: CODA: toward automatically identifying and scheduling coflows in the dark. In: ACM SIGCOMM, pp. 160–173 (2016)Google Scholar
  17. 17.
    Chowdhury, M., Stoica, I.: Coflow: an application layer abstraction for cluster networking. In: ACM Hotnets. Citeseer (2012)Google Scholar
  18. 18.
    Pebesma, E., Cornford, D., Dubois, G., Heuvelink, G.B., Hristopulos, D., Pilz, J., Stohlker, U., Morin, G., Skoien, J.O.: Intamap: the design and implementation of an interoperable automated interpolation web service. Comput. Geosci. 37(3), 343–352 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Yuchao Zhang
    • 1
  • Ke Xu
    • 2
  1. 1.Beijing University of Posts and TelecommBeijingChina
  2. 2.Tsinghua UniversityBeijingChina

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