Addressing Application Latency Requirements through Edge Scheduling

  • Atakan AralEmail author
  • Ivona Brandic
  • Rafael Brundo Uriarte
  • Rocco De Nicola
  • Vincenzo Scoca
Open Access


Latency-sensitive and data-intensive applications, such as IoT or mobile services, are leveraged by Edge computing, which extends the cloud ecosystem with distributed computational resources in proximity to data providers and consumers. This brings significant benefits in terms of lower latency and higher bandwidth. However, by definition, edge computing has limited resources with respect to cloud counterparts; thus, there exists a trade-off between proximity to users and resource utilization. Moreover, service availability is a significant concern at the edge of the network, where extensive support systems as in cloud data centers are not usually present. To overcome these limitations, we propose a score-based edge service scheduling algorithm that evaluates network, compute, and reliability capabilities of edge nodes. The algorithm outputs the maximum scoring mapping between resources and services with regard to four critical aspects of service quality. Our simulation-based experiments on live video streaming services demonstrate significant improvements in both network delay and service time. Moreover, we compare edge computing with cloud computing and content delivery networks within the context of latency-sensitive and data-intensive applications. The results suggest that our edge-based scheduling algorithm is a viable solution for high service quality and responsiveness in deploying such applications.


Edge computing Scheduling Live streaming 



This work has been partially funded by the Rucon project (Runtime Control in Multi Clouds), FWF Y 904 START-Programm 2015, by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 838949, and by the Italian National Interuniversity Consortium for Informatics (CINI).

Funding Information

Open access funding provided by Austrian Science Fund (FWF).


  1. 1.
    Aazam, M., Huh, E.N.: Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT. In: IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 687–694. IEEE (2015)Google Scholar
  2. 2.
    Aral, A., Brandic, I.: Quality of service channelling for latency sensitive edge applications. In: IEEE International Conference on Edge Computing (EDGE), pp. 166–173. IEEE (2017)Google Scholar
  3. 3.
    Aral, A., Brandic, I.: Dependency mining for service resilience at the edge. In: ACM/IEEE Symposium on Edge Computing, pp. 228–242. ACM (2018)Google Scholar
  4. 4.
    Aral, A., Ovatman, T.: Network-aware embedding of virtual machine clusters onto federated cloud infrastructure. J. Syst. Softw. 120, 89–104 (2016)CrossRefGoogle Scholar
  5. 5.
    Aral, A., Ovatman, T.: A decentralized replica placement algorithm for edge computing. IEEE Trans. Netw. Serv. Manag. 15(2), 516–529 (2018)CrossRefGoogle Scholar
  6. 6.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Basic, F., Aral, A., Brandic, I.: Fuzzy handoff control in edge offloading. In: IEEE International Conference on Fog Computing. IEEE (2019)Google Scholar
  8. 8.
    Bilal, K., Erbad, A.: Edge computing for interactive media and video streaming. In: International Conference on Fog and Mobile Edge Computing (FMEC), pp. 68–73. IEEE (2017)Google Scholar
  9. 9.
    Bittencourt, L.F., Diaz-Montes, J., Buyya, R., Rana, O.F., Parashar, M.: Mobility-aware application scheduling in fog computing. IEEE Cloud Computing 4(2), 26–35 (2017)CrossRefGoogle Scholar
  10. 10.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience 41(1), 23–50 (2011)Google Scholar
  11. 11.
    Dastjerdi, A.V., Gupta, H., Calheiros, R.N., Ghosh, S.K., Buyya, R.: Fog computing: principles, architectures, and applications. In: Internet of Things, pp. 61–75. Elsevier (2016)Google Scholar
  12. 12.
    Dobrian, F., Sekar, V., Awan, A., Stoica, I., Joseph, D., Ganjam, A., Zhan, J., Zhang, H.: Understanding the impact of video quality on user engagement. ACM SIGCOMM Computer Communication Review 41(4), 362–373 (2011)CrossRefGoogle Scholar
  13. 13.
    Duong, T.N.B., Li, X., Goh, R.S.M., Tang, X., Cai, W.: Qos-aware revenue-cost optimization for latency-sensitive services in Iaas clouds. In: IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT), pp. 11–18. IEEE (2012)Google Scholar
  14. 14.
    Fan, C., Huang, J., Yang, D., Rong, Z.: Modeling poi transition network of human mobility. In: International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, pp. 364–367. IEEE (2016)Google Scholar
  15. 15.
    Gill, S.S., Buyya, R.: Resource provisioning based scheduling framework for execution of heterogeneous and clustered workloads in clouds: from fundamental to autonomic offering. Journal of Grid Computing, 17(3), 385–417 (2018)CrossRefGoogle Scholar
  16. 16.
    Guo, X., Singh, R., Zhao, T., Niu, Z.: An index based task assignment policy for achieving optimal power-delay tradeoff in edge cloud systems. In: IEEE International Conference on Communications (ICC), pp. 1–7. IEEE (2016)Google Scholar
  17. 17.
    Hess, A., Hummel, K.A., Gansterer, W.N., Haring, G.: Data-driven human mobility modeling: a survey and engineering guidance for mobile networking. ACM Computing Surveys (CSUR) 48(3), 38 (2016)Google Scholar
  18. 18.
    Hu, W., Gao, Y., Ha, K., Wang, J., Amos, B., Chen, Z., Pillai, P., Satyanarayanan, M.: Quantifying the impact of edge computing on mobile applications. In: Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems, p 5. ACM (2016)Google Scholar
  19. 19.
    Hu, Y.C., Patel, M., Sabella, D., Sprecher, N., Young, V.: Mobile edge computing—a key technology towards 5g. ETSI White Paper 11(11), 1–16 (2015)Google Scholar
  20. 20.
    Jamshidi, P., Ahmad, A., Pahl, C.: Autonomic resource provisioning for cloud-based software. In: International Symposium on Software Engineering for Adaptive and Self-Managing Systems, pp. 95–104. ACM (2014)Google Scholar
  21. 21.
    Konstanteli, K., Cucinotta, T., Psychas, K., Varvarigou, T.A.: Elastic admission control for federated cloud services. IEEE Transactions on Cloud Computing 2(3), 348–361 (2014)CrossRefGoogle Scholar
  22. 22.
    Mahmud, R., Kotagiri, R., Buyya, R.: Fog computing: a taxonomy, survey and future directions. In: Internet of Everything, pp. 103–130. Springer (2018)Google Scholar
  23. 23.
    Mao, Y., Zhang, J., Letaief, K.B.: Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE Journal on Selected Areas in Communications 34(12), 3590–3605 (2016)CrossRefGoogle Scholar
  24. 24.
    Medina, A., Lakhina, A., Matta, I., Byers, J.: BRITE: an approach to universal topology generation. In: Ninth International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, pp. 346–353, IEEE (2001)Google Scholar
  25. 25.
    Pang, J., Hendricks, J., Akella, A., De Prisco, R., Maggs, B., Seshan, S.: Availability, usage, and deployment characteristics of the domain name system. In: Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement, pp. 1–14. ACM (2004)Google Scholar
  26. 26.
    Papagianni, C., Leivadeas, A., Papavassiliou, S., Maglaris, V., Cervello-Pastor, C., Monje, A.: On the optimal allocation of virtual resources in cloud computing networks. IEEE Trans. Comput. 62(6), 1060–1071 (2013)MathSciNetzbMATHCrossRefGoogle Scholar
  27. 27.
    Pathan, A.M.K., Buyya, R.: A taxonomy and survey of content delivery networks. Grid Computing and Distributed Systems Laboratory, University of Melbourne, Technical Report (2007)Google Scholar
  28. 28.
    Piao, J.T., Yan, J.: A network-aware virtual machine placement and migration approach in cloud computing. In: International Conference on Grid and Cooperative Computing (GCC), pp. 87–92. IEEE (2010)Google Scholar
  29. 29.
    Pittaras, C., Papagianni, C., Leivadeas, A., Grosso, P., van der Ham, J., Papavassiliou, S.: Resource discovery and allocation for federated virtualized infrastructures. Futur. Gener. Comput. Syst. 42, 55–63 (2015)CrossRefGoogle Scholar
  30. 30.
    Plachy, J., Becvar, Z., Strinati, E.C.: Dynamic resource allocation exploiting mobility prediction in mobile edge computing. In: IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications, pp. 1–6. IEEE (2016)Google Scholar
  31. 31.
    Satyanarayanan, M.: The emergence of edge computing. Computer 50(1), 30–39 (2017)CrossRefGoogle Scholar
  32. 32.
    Scoca, V., Uriarte, R.B., De Nicola, R.: Smart contract negotiation in cloud computing. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 592–599. IEEE (2017)Google Scholar
  33. 33.
    Scoca, V., Aral, A., Brandic, I., De Nicola, R., Uriarte, R.B.: Scheduling latency-sensitive applications in edge computing. In: CLOSER, pp. 158–168 (2018)Google Scholar
  34. 34.
    Selimi, M., Cerdà-Alabern, L., Freitag, F., Veiga, L., Sathiaseelan, A., Crowcroft, J.: A lightweight service placement approach for community network micro-clouds. Journal of Grid Computing 17(1), 169–189 (2019)CrossRefGoogle Scholar
  35. 35.
    Shamsi, J., Khojaye, M.A., Qasmi, M.A.: Data-intensive cloud computing: requirements, expectations, challenges, and solutions. Journal of Grid Computing 11(2), 281–310 (2013)CrossRefGoogle Scholar
  36. 36.
    Sharifi, L., Cerdà Alabern, L., Freitag, F., Veiga, L.: Energy efficient cloud service provisioning: keeping data center granularity in perspective. Journal of Grid Computing 14(2), 299–325 (2016)CrossRefGoogle Scholar
  37. 37.
    Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE Internet of Things Journal 3(5), 637–646 (2016)CrossRefGoogle Scholar
  38. 38.
    Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: issues and challenges. Journal of Grid Computing 14(2), 217–264 (2016)CrossRefGoogle Scholar
  39. 39.
    Skarlat, O., Nardelli, M., Schulte, S., Dustdar, S.: Towards Qos-aware fog service placement. In: IEEE 1st International Conference on Fog and Edge Computing (ICFEC), pp. 89–96. IEEE (2017)Google Scholar
  40. 40.
    Song, W., Xiao, Z., Chen, Q., Luo, H.: Adaptive resource provisioning for the cloud using online bin packing. IEEE Trans. Comput. 63(11), 2647–2660 (2014)MathSciNetzbMATHCrossRefGoogle Scholar
  41. 41.
    Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: an environment for performance evaluation of edge computing systems. In: International Conference on Fog and Mobile Edge Computing (FMEC), pp. 39–44. IEEE (2017)Google Scholar
  42. 42.
    Sripanidkulchai, K., Maggs, B., Zhang, H.: An analysis of live streaming workloads on the internet. In: ACM SIGCOMM Conference on Internet Measurement, pp. 41–54. ACM (2004)Google Scholar
  43. 43.
    Stanciu, A.: Blockchain based distributed control system for edge computing. In: 2017 21st International Conference on Control Systems and Computer Science (CSCS), pp. 667–671. IEEE (2017)Google Scholar
  44. 44.
    Sun, Y., Zhou, S., Xu, J.: EMM: energy-aware mobility management for mobile edge computing in ultra dense networks. IEEE J. Sel. Areas Commun. 35(11), 2637–2646 (2017)CrossRefGoogle Scholar
  45. 45.
    Tuli, S., Mahmud, R., Tuli, S., Buyya, R.: Fogbus: a blockchain-based lightweight framework for edge and fog computing. J. Syst. Softw. 154, 22–36 (2019)CrossRefGoogle Scholar
  46. 46.
    Uriarte, R.B., De Nicola, R.: Blockchain-based decentralised cloud/fog solutions: challenges, opportunities and standards. IEEE Communications Standards Magazine. 2(3), 22–28 (2018)CrossRefGoogle Scholar
  47. 47.
    Uriarte, R.B., Tiezzi, F., De Nicola, R.: Dynamic slas for clouds. In: European Conference on Service-Oriented and Cloud Computing, pp. 34–49. Springer (2016)Google Scholar
  48. 48.
    Uriarte, R.B., Tiezzi, F., Tsaftaris, S.A.: Supporting autonomic management of clouds: service clustering with random forest. IEEE Trans. Netw. Serv. Manag. 13(3), 595–607 (2016)CrossRefGoogle Scholar
  49. 49.
    Uriarte, R.B., De Nicola, R., Scoca, V., Tiezzi, F.: Defining and guaranteeing dynamic service levels in clouds. Futur. Gener. Comput. Syst. 99, 27–40 (2019)CrossRefGoogle Scholar
  50. 50.
    Verbelen, T., Simoens, P., De Turck, F., Dhoedt, B.: Cloudlets: bringing the cloud to the mobile user. In: ACM Workshop on Mobile Cloud Computing and Services, pp. 29–36. ACM (2012)Google Scholar
  51. 51.
    Wang, S., Zhang, X., Zhang, Y., Wang, L., Yang, J., Wang, W.: A survey on mobile edge networks: convergence of computing, caching and communications. IEEE Access 5, 6757–6779 (2017)CrossRefGoogle Scholar
  52. 52.
    Yu, C., Lumezanu, C., Sharma, A., Xu, Q., Jiang, G., Madhyastha, H.V.: Software-defined latency monitoring in data center networks. In: International Conference on Passive and Active Network Measurement, pp. 360–372. Springer (2015)Google Scholar
  53. 53.
    Zeng, L., Veeravalli, B., Wei, Q.: Space4time: optimization latency-sensitive content service in cloud. J. Netw. Comput. Appl. 41, 358–368 (2014)CrossRefGoogle Scholar
  54. 54.
    Zhang, H., Qiu, Y., Chu, X., Long, K., Leung, V.C.: Fog radio access networks: mobility management, interference mitigation, and resource optimization. IEEE Wirel. Commun. 24(6), 120–127 (2017)CrossRefGoogle Scholar
  55. 55.
    Zhao, T., Zhou, S., Guo, X., Zhao, Y., Niu, Z.: A cooperative scheduling scheme of local cloud and internet cloud for delay-aware mobile cloud computing. In: IEEE Globecom Workshops, pp. 1–6. IEEE (2015)Google Scholar

Copyright information

© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Vienna University of TechnologyViennaAustria
  2. 2.IMT School for Advanced Studies LuccaLuccaItaly

Personalised recommendations