Deadline-Aware Deployment for Time Critical Applications in Clouds

  • Yang Hu
  • Junchao Wang
  • Huan Zhou
  • Paul Martin
  • Arie Taal
  • Cees de Laat
  • Zhiming Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10417)


Time critical applications are appealing to deploy in clouds due to the elasticity of cloud resources and their on-demand nature. However, support for deploying application components with strict deadlines on their deployment is lacking in current cloud providers. This is particularly important for adaptive applications that must automatically and seamlessly scale, migrate, or recover swiftly from failures. A common deployment procedure is to transmit application packages from the application provider to the cloud, and install the application there. Thus, users need to manually deploy their applications into clouds step by step with no guarantee regarding deadlines. In this work, we propose a Deadline-aware Deployment System (DDS) for time critical applications in clouds. DDS enables users to automatically deploy applications into clouds. We design bandwidth-aware EDF scheduling algorithms in DDS that minimize the number of deployments that miss their deadlines and maximize the utilization of network bandwidth. In the evaluation, we show that DDS leverages network bandwidth sufficiently, and significantly reduces the number of missed deadlines during deployment.



This research has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreements 643963 (SWITCH project), 654182 (ENVRIPLUS project) and 676247 (VRE4EIC project). The research is also partially funded by the COMMIT project.


  1. 1.
    Alizadeh, M., Yang, S., Sharif, M., Katti, S., McKeown, N., Prabhakar, B., Shenker, S.: pFabric: minimal near-optimal datacenter transport. In: ACM SIGCOMM Computer Communication Review, vol. 43, pp. 435–446. ACM (2013)Google Scholar
  2. 2.
    Baldin, I., Chase, J., Xin, Y., Mandal, A., Ruth, P., Castillo, C., Orlikowski, V., Heermann, C., Mills, J.: ExoGENI: a multi-domain infrastructure-as-a-service testbed. In: McGeer, R., Berman, M., Elliott, C., Ricci, R. (eds.) The GENI Book, pp. 279–315. Springer, Cham (2016). doi: 10.1007/978-3-319-33769-2_13 CrossRefGoogle Scholar
  3. 3.
    Casalicchio, E., Silvestri, L.: Mechanisms for SLA provisioning in cloud-based service providers. Comput. Netw. 57(3), 795–810 (2013)CrossRefGoogle Scholar
  4. 4.
    Chen, L., Chen, K., Bai, W., Alizadeh, M.: Scheduling mix-flows in commodity datacenters with Karuna. In: Proceedings of the 2016 conference on ACM SIGCOMM 2016 Conference, pp. 174–187. ACM (2016)Google Scholar
  5. 5.
    Gao, W., Jin, H., Wu, S., Shi, X., Yuan, J.: Effectively deploying services on virtualization infrastructure. Front. Comput. Sci. 6(4), 398–408 (2012)MathSciNetGoogle Scholar
  6. 6.
    Hu, Y., Li, H., Peng, Y.: NVLAN: a novel VLAN technology for scalable multi-tenant datacenter networks. In: 2014 Second International Conference on Advanced Cloud and Big Data (CBD), pp. 190–195. IEEE (2014)Google Scholar
  7. 7.
    Li, W., Svärd, P., Tordsson, J., Elmroth, E.: A general approach to service deployment in cloud environments. In: 2012 Second International Conference on Cloud and Green Computing (CGC), pp. 17–24. IEEE (2012)Google Scholar
  8. 8.
    Liu, C.L., Layland, J.W.: Scheduling algorithms for multiprogramming in a hard-real-time environment. J. ACM (JACM) 20(1), 46–61 (1973)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Merkel, D.: Docker: lightweight Linux containers for consistent development and deployment. Linux J. 2014(239), 2 (2014)Google Scholar
  10. 10.
    Peng, C., Kim, M., Zhang, Z., Lei, H.: VDN: virtual machine image distribution network for cloud data centers. In: 2012 Proceedings IEEE INFOCOM, pp. 181–189. IEEE (2012)Google Scholar
  11. 11.
    Smith, W., Foster, I., Taylor, V.: Predicting application run times using historical information. In: Feitelson, D.G., Rudolph, L. (eds.) JSSPP 1998. LNCS, vol. 1459, pp. 122–142. Springer, Heidelberg (1998). doi: 10.1007/BFb0053984 CrossRefGoogle Scholar
  12. 12.
    Tirumala, A., Qin, F., Dugan, J., Ferguson, J., Gibbs, K.: Iperf: the TCP/UDP bandwidth measurement tool (2005).
  13. 13.
    Tsai, W., Bai, X., Huang, Y.: Software-as-a-service (SaaS): perspectives and challenges. Sci. China Inf. Sci. 57(5), 1–15 (2014)CrossRefGoogle Scholar
  14. 14.
    Vamanan, B., Hasan, J., Vijaykumar, T.: Deadline-aware datacenter TCP (D2TCP). ACM SIGCOMM Comput. Commun. Rev. 42(4), 115–126 (2012)CrossRefGoogle Scholar
  15. 15.
    Vaquero, L.M., Celorio, A., Cuadrado, F., Cuevas, R.: Deploying large-scale datasets on-demand in the cloud: treats and tricks on data distribution. IEEE Trans. Cloud Comput. 3(2), 132–144 (2015)CrossRefGoogle Scholar
  16. 16.
    Wang, J., Taal, A., Martin, P., Hu, Y., Zhou, H., Pang, J., de Laat, C., Zhao, Z.: Planning virtual infrastructures for time critical applications with multiple deadline constraints. Future Gener. Comput. Syst. (2017)Google Scholar
  17. 17.
    Wilson, C., Ballani, H., Karagiannis, T., Rowtron, A.: Better never than late: meeting deadlines in datacenter networks. In: ACM SIGCOMM Computer Communication Review, vol. 41, pp. 50–61. ACM (2011)Google Scholar
  18. 18.
    Zhao, Z., Martin, P., De Laat, C., Jeffery, K., Jones, A., Taylor, I., Hardisty, A., Atkinson, M., Zuiderwijk, A., Yin, Y., Chen, Y.: Time critical requirements and technical considerations for advanced support environments for data-intensive research. In: 2nd International Workshop on Interoperable Infrastructures for Interdisciplinary Big Data Sciences (IT4RIs) in the Context of IEEE Real-Time System Symposium (RTSS) (2016)Google Scholar
  19. 19.
    Zhao, Z., Martin, P., Wang, J., Taal, A., Jones, A., Taylor, I., Stankovski, V., Vega, I.G., Suciu, G., Ulisses, A., et al.: Developing and operating time critical applications in clouds: the state of the art and the SWITCH approach. Procedia Comput. Sci. 68, 17–28 (2015)CrossRefGoogle Scholar
  20. 20.
    Zhou, H., Hu, Y., Wang, J., Martin, P., De Laat, C., Zhao, Z.: Fast and dynamic resource provisioning for quality critical cloud applications. In: 2016 IEEE 19th International Symposium on Real-Time Distributed Computing (ISORC), pp. 92–99. IEEE (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yang Hu
    • 1
    • 2
  • Junchao Wang
    • 1
  • Huan Zhou
    • 1
    • 2
  • Paul Martin
    • 1
  • Arie Taal
    • 1
  • Cees de Laat
    • 1
  • Zhiming Zhao
    • 1
  1. 1.University of AmsterdamAmsterdamThe Netherlands
  2. 2.National University of Defense TechnologyChangshaChina

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