Advertisement

Optimized Application Deployment in the Fog

  • Zoltán Ádám MannEmail author
  • Andreas Metzger
  • Johannes Prade
  • Robert Seidl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11895)

Abstract

Fog computing uses geographically distributed fog nodes that can supply nearby end devices with low-latency access to cloud-like compute resources. If the load of a fog node exceeds its capacity, some non-latency-critical application components may be offloaded to the cloud. Using commercial cloud offerings for such offloading incurs financial costs. Optimally deciding which application components to keep in the fog node and which ones to offload to the cloud is a difficult combinatorial problem. We introduce an optimization algorithm that (i) guarantees that the deployment always satisfies capacity constraints, (ii) achieves near-optimal cloud usage costs, and (iii) is fast enough to be run online. Experimental results show that our algorithm can optimize the deployment of hundreds of components in a fraction of a second on a commodity computer, while leading to only slightly higher costs than the optimum.

Notes

Acknowledgments

Research leading to these results received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements no. 731678 (RestAssured) and 731932 (TransformingTransport).

References

  1. 1.
    Abbas, Z., Li, J., Yadav, N., Tariq, I.: Computational task offloading in mobile edge computing using learning automata. In: IEEE ICCC, pp. 57–61 (2018)Google Scholar
  2. 2.
    Alkhanak, E.N., Lee, S.P., Rezaei, R., Parizi, R.M.: Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: a review, classifications, and open issues. J. Syst. Softw. 113, 1–26 (2016)CrossRefGoogle Scholar
  3. 3.
    Bermbach, D., et al.: A research perspective on fog computing. In: Braubach, L., et al. (eds.) ICSOC 2017. LNCS, vol. 10797, pp. 198–210. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-91764-1_16CrossRefGoogle Scholar
  4. 4.
    Brogi, A., Forti, S., Guerrero, C., Lera, I.: How to place your apps in the fog - state of the art and open challenges. arXiv preprint, arXiv:1901.05717 (2019)
  5. 5.
    Cai, X., Kuang, H., Hu, H., Song, W., Lü, J.: Response time aware operator placement for complex event processing in edge computing. In: Pahl, C., Vukovic, M., Yin, J., Yu, Q. (eds.) ICSOC 2018. LNCS, vol. 11236, pp. 264–278. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-03596-9_18CrossRefGoogle Scholar
  6. 6.
    Candeia, D., Araújo, R., Lopes, R., Brasileiro, F.: Investigating business-driven cloudburst schedulers for e-science bag-of-tasks applications. In: CloudCom, pp. 343–350 (2010)Google Scholar
  7. 7.
    Chang, Y.S., Fan, C.T., Sheu, R.K., Jhu, S.R., Yuan, S.M.: An agent-based workflow scheduling mechanism with deadline constraint on hybrid cloud environment. Int. J. Commun Syst 31(1), e3401 (2018)CrossRefGoogle Scholar
  8. 8.
    Chopra, N., Singh, S.: Deadline and cost based workflow scheduling in hybrid cloud. In: ICACCI, pp. 840–846 (2013)Google Scholar
  9. 9.
    Dastjerdi, A.V., Buyya, R.: Fog computing: helping the Internet of Things realize its potential. Computer 49(8), 112–116 (2016)CrossRefGoogle Scholar
  10. 10.
    Deng, S., Xiang, Z., Yin, J., Taheri, J., Zomaya, A.Y.: Composition-driven IoT service provisioning in distributed edges. IEEE Access 6, 54258–54269 (2018)CrossRefGoogle Scholar
  11. 11.
    Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Techn. J. 49(2), 291–307 (1970)CrossRefGoogle Scholar
  12. 12.
    Lai, P., et al.: Optimal edge user allocation in edge computing with variable sized vector bin packing. In: Pahl, C., Vukovic, M., Yin, J., Yu, Q. (eds.) ICSOC 2018. LNCS, vol. 11236, pp. 230–245. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-03596-9_15CrossRefGoogle Scholar
  13. 13.
    Mahmud, R., Kotagiri, R., Buyya, R.: Fog computing: a taxonomy, survey and future directions. In: Di Martino, B., Li, K.-C., Yang, L.T., Esposito, A. (eds.) Internet of Everything. IT, pp. 103–130. Springer, Singapore (2018).  https://doi.org/10.1007/978-981-10-5861-5_5CrossRefGoogle Scholar
  14. 14.
    Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM ToIT 19(1), 9 (2018)Google Scholar
  15. 15.
    Malawski, M., Figiela, K., Nabrzyski, J.: Cost minimization for computational applications on hybrid cloud infrastructures. FGCS 29(7), 1786–1794 (2013)CrossRefGoogle Scholar
  16. 16.
    Mann, Z.Á.: Partitioning algorithms for hardware/software co-design. Ph.D. thesis, Budapest University of Technology and Economics (2004)Google Scholar
  17. 17.
    Mann, Z.Á.: Optimization in Computer Engineering - Theory and Applications. Scientific Research Publishing, Irvine (2011)Google Scholar
  18. 18.
    Mann, Z.Á., Metzger, A.: Optimized cloud deployment of multi-tenant software considering data protection concerns. In: CCGRID, pp. 609–618 (2017)Google Scholar
  19. 19.
    Mann, Z.Á., Orbán, A., Farkas, V.: Evaluating the Kernighan-Lin heuristic for hardware/software partitioning. AMCS 17(2), 249–267 (2007)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Mann, Z.Á., Papp, P.A.: Formula partitioning revisited. In: 5th Pragmatics of SAT Workshop, vol. 27, pp. 41–56. EasyChair Proceedings in Computing (2014)Google Scholar
  21. 21.
    Mann, Z.Á., Papp, P.A.: Guiding SAT solving by formula partitioning. Int. J. Artif. Intell. Tools 26(4), 1750011 (2017)CrossRefGoogle Scholar
  22. 22.
    Mouradian, C., Kianpisheh, S., Abu-Lebdeh, M., Ebrahimnezhad, F., Jahromi, N.T., Glitho, R.H.: Application component placement in NFV-based hybrid cloud/fog systems with mobile fog nodes. IEEE JSAC 37(5), 1130–1143 (2019)Google Scholar
  23. 23.
    Nan, Y., Li, W., Bao, W., Delicato, F.C., Pires, P.F., Zomaya, A.Y.: A dynamic tradeoff data processing framework for delay-sensitive applications in cloud of things systems. J. Parallel Distrib. Comput. 112, 53–66 (2018)CrossRefGoogle Scholar
  24. 24.
    Ravindra, P., Khochare, A., Reddy, S.P., Sharma, S., Varshney, P., Simmhan, Y.: ECHO: an adaptive \( \underline{\rm O}\)rchestration platform for \( \underline{\rm H}\)ybrid dataflows across \( \underline{\rm C}\)loud and \( \underline{\rm E}\)dge. In: Maximilien, M., Vallecillo, A., Wang, J., Oriol, M. (eds.) ICSOC 2017. LNCS, vol. 10601, pp. 395–410. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69035-3_28CrossRefGoogle Scholar
  25. 25.
    da Silva Veith, A., de Assunção, M.D., Lefèvre, L.: Latency-aware placement of data stream analytics on edge computing. In: Pahl, C., Vukovic, M., Yin, J., Yu, Q. (eds.) ICSOC 2018. LNCS, vol. 11236, pp. 215–229. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-03596-9_14CrossRefGoogle Scholar
  26. 26.
    Skarlat, O., Nardelli, M., Schulte, S., Borkowski, M., Leitner, P.: Optimized IoT service placement in the fog. Service Oriented Comp. Appl. 11(4), 427–443 (2017)CrossRefGoogle Scholar
  27. 27.
    Taneja, M., Davy, A.: Resource aware placement of IoT application modules in fog-cloud computing paradigm. In: IEEE IM, pp. 1222–1228 (2017)Google Scholar
  28. 28.
    Van den Bossche, R., Vanmechelen, K., Broeckhove, J.: Cost-optimal scheduling in hybrid IaaS clouds for deadline constrained workloads. In: IEEE CLOUD, pp. 228–235 (2010)Google Scholar
  29. 29.
    Zhu, J., Li, X., Ruiz, R., Xu, X.: Scheduling stochastic multi-stage jobs to elastic hybrid cloud resources. IEEE TPDS 29(6), 1401–1415 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zoltán Ádám Mann
    • 1
    Email author
  • Andreas Metzger
    • 1
  • Johannes Prade
    • 2
  • Robert Seidl
    • 3
  1. 1.University of Duisburg-EssenEssenGermany
  2. 2.NokiaMunichGermany
  3. 3.Nokia Bell LabsMunichGermany

Personalised recommendations