Optimized Application Deployment in the Fog

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 11895 LNCS, Issue , 2019, Pages 283-298

  Mann, Zoltán Ádám ^a   Metzger, Andreas ^a   Prade, Johannes ^b   Seidl, Robert ^c  

^a UNIVERSITY OF DUISBURG ESSEN   (Germany)

^b NOKIA   (Germany)

^c LUCENT TECHNOLOGIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FOG;

APPLICATION COMPONENTS; CAPACITY CONSTRAINTS; COMBINATORIAL PROBLEM; COMPUTE RESOURCES; CRITICAL APPLICATIONS; FINANCIAL COSTS; OPTIMIZATION ALGORITHMS; OPTIMIZED APPLICATION;

FOG COMPUTING;

EID: 85076362686     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-030-33702-5_22     Document Type: Conference Paper

References (29)

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)
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)
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 16
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. 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 18
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)
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)
8. Chopra, N., Singh, S.: Deadline and cost based workflow scheduling in hybrid cloud. In: ICACCI, pp. 840–846 (2013)
9. Dastjerdi, A.V., Buyya, R.: Fog computing: helping the Internet of Things realize its potential. Computer 49(8), 112–116 (2016)
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)
11. Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Techn. J. 49(2), 291–307 (1970)
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 15
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 5
14. Mahmud, R., Ramamohanarao, K., Buyya, R.: Latency-aware application module management for fog computing environments. ACM ToIT 19(1), 9 (2018)
15. Malawski, M., Figiela, K., Nabrzyski, J.: Cost minimization for computational applications on hybrid cloud infrastructures. FGCS 29(7), 1786–1794 (2013)
16. Mann, Z.Á.: Partitioning algorithms for hardware/software co-design. Ph.D. thesis, Budapest University of Technology and Economics (2004)
17. Mann, Z.Á.: Optimization in Computer Engineering-Theory and Applications. Scientific Research Publishing, Irvine (2011)
18. Mann, Z.Á., Metzger, A.: Optimized cloud deployment of multi-tenant software considering data protection concerns. In: CCGRID, pp. 609–618 (2017)
19. Mann, Z.Á., Orbán, A., Farkas, V.: Evaluating the Kernighan-Lin heuristic for hardware/software partitioning. AMCS 17(2), 249–267 (2007)
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)
21. Mann, Z.Á., Papp, P.A.: Guiding SAT solving by formula partitioning. Int. J. Artif. Intell. Tools 26(4), 1750011 (2017)
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)
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)
24. Ravindra, P., Khochare, A., Reddy, S.P., Sharma, S., Varshney, P., Simmhan, Y.: ECHO: an adaptive Orchestration platform for Hybrid dataflows across Cloud and Edge. 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 28
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 14
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)
27. Taneja, M., Davy, A.: Resource aware placement of IoT application modules in fog-cloud computing paradigm. In: IEEE IM, pp. 1222–1228 (2017)
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)
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)