Abstract
As the Internet of Things (IoT) paradigm becomes omnipresent, so does fog computing, a paradigm aimed at bringing applications closer to the end devices, aiding in lowering stress over the network and improving latency. However, to efficiently place application tasks in the fog, task placement coordination is needed. In this paper, task placement in the fog and corresponding problems are addressed. We look at the fundamental issue of solving Multi-Objective Optimization problems and treat different techniques for distributed coordination. We review how this research can be used in a smart vehicle environment, and finish with some preliminary tests results.
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References
Vinitsky, E., Parvate, K., Kreidieh, A., Wu, C., Bayen, A.: Lagrangian Control through Deep-RL: applications to bottleneck decongestion. In: IEEE Intelligent Transportation Systems Conference, pp. 759–765 (2018)
Cisco: Cisco Global Cloud Index: Forecast and Methodology 2014-2019 (white paper). Cisco, pp. 2016–2021 (2016)
Iorga, M., et al.: Fog computing conceptual model. NIST, Gaithersburg, MD, Technical report, March 2018
Xia, Y., et al.: Combining heuristics to optimize and scale the placement of IoT applications in the fog. In: 2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC), pp. 153–163 (2018)
Huybrechts, T., Mercelis, S., Hellinckx, P.: A new hybrid approach on WCET analysis for real-time systems using machine learning, no. 5, pp. 1–5 (2018)
IDLab, Imec, and University of Antwerp: COBRA Framework. http://cobra.idlab.uantwerpen.be/
Sharma, B., Chudnovsky, V., Hellerstein, J.L., Rifaat, R., Das, C.R.: Modeling and synthesizing task placement constraints in Google compute clusters. In: Proceedings of the 2nd ACM Symposium on Cloud Computing - SOCC 2011, pp. 1–14. ACM Press (2011)
Vanneste, S., et al.: Distributed uniform streaming framework: towards an elastic fog computing platform for event stream processing. In: Proceedings of the 13th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 426–436 (2019)
Abowd, G.D., et al.: Towards a better understanding of context and context-awareness. In: Lecture Notes in Computer Science, vol. 1707, pp. 304–307 (1999)
Wen, Z., et al.: Fog orchestration for internet of things services. IEEE Internet Comput. 21(2), 16–24 (2017)
Brogi, A., Forti, S.: QoS-aware deployment of IoT applications through the fog. IEEE Internet Things J. 4(5), 1–8 (2017)
Wang, S., Zafer, M., Leung, K.K.: Online placement of multi-component applications in edge computing environments. IEEE Access 5, 2514–2533 (2017)
Eyckerman, R., Sharif, M., Mercelis, S., Hellinckx, P.: Context-aware distribution in constrained IoT environments, pp. 437–446 (2019)
Smith, R.G.: Communication and control in a distributed problem solver. IEEE Trans. Comput. C(12), 1104–1113 (1980)
Talukdar, S., Baerentzen, L., Gove, A., De Souza, P.: Asynchronous teams: cooperation schemes for autonomous agents. J. Heurist. 4(4), 295–321 (1998)
Barbucha, D., Je, P.: An agent-based approach to vehicle routing problem. Int. J. Appl. Math. Comput. Sci. 4(1), 36–41 (2007)
Barbucha, D.: Agent-based optimization. In: Agent-Based Optimization, vol. 456, pp. 55–75 (2013)
Casanova, H., Giersch, A., Legrand, A., Quinson, M., Suter, F.: Versatile, scalable, and accurate simulation of distributed applications and platforms. J. Parallel Distrib. Comput. 74(10), 2899–2917 (2014)
Zeng, X., et al.: IOTSim: a simulator for analysing IoT applications. J. Syst. Architect. 72, 93–107 (2017)
Marler, R., Arora, J.: Survey of multi-objective optimization methods for engineering. Struct. Multidisc. Optim. 26(6), 369–395 (2004)
Marler, R.T., Arora, J.S.: The weighted sum method for multi-objective optimization: new insights. Struct. Multidisc. Optim. 41, 853–862 (2010)
Kaufman, K.A., Michalski, R.S.: Learning from inconsistent and noisy data: the AQ18 approach. In: Symposium A Quarterly Journal In Modern Foreign Literatures, pp. 411–419 (1999)
Gavras, A., Karila, A., Fdida, S., May, M., Potts, M.: Future internet research and experimentation: the FIRE initiative. Sigcomm 37(3), 89–92 (2007)
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Eyckerman, R., Mercelis, S., Marquez-Barja, J., Hellinckx, P. (2020). Distributed Task Placement in the Fog: A Positioning Paper. In: Barolli, L., Hellinckx, P., Natwichai, J. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2019. Lecture Notes in Networks and Systems, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-030-33509-0_63
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DOI: https://doi.org/10.1007/978-3-030-33509-0_63
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