ACS Applied Computer Science

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IoT networks generate numerous amounts of data that is then transferred to the cloud for processing. Transferring data cleansing and parts of calculations towards these edge-level networks improves system’s, latency, energy consumption, network bandwidth and computational resources utilization, fault tolerance and thus operational costs. On the other hand, these fog nodes are resource-constrained, have extremely distributed and heterogeneous nature, lack horizontal scalability, and, thus, the vanilla SOA approach is not applicable to them. Utilization of Software Defined Network (SDN) with task distribution capabilities advocated in this paper addresses these issues. Suggested framework may utilize various routing and data distribution algorithms allowing to build flexible system most relevant for particular use-case. Advocated architecture was evaluated in agent-based simulation environment and proved its’ feasibility and performance gains compared to conventional event-stream approach.

  • APA 6th style
Pysmennyi, I., Petrenko, A., & Kyslyi, R. (2020). Graph-based fog computing network model. Applied Computer Science, 16(4), 5-20. doi:10.23743/acs-2020-25
  • Chicago style
Pysmennyi, Ihor, Anatolii Petrenko, and Roman Kyslyi. "Graph-Based Fog Computing Network Model." Applied Computer Science 16, no. 4 (2020): 5-20.
  • IEEE style
I. Pysmennyi, A. Petrenko, and R. Kyslyi, "Graph-based fog computing network model," Applied Computer Science, vol. 16, no. 4, pp. 5-20, 2020, doi: 10.23743/acs-2020-25.
  • Vancouver style
Pysmennyi I, Petrenko A, Kyslyi R. Graph-based fog computing network model. Applied Computer Science. 2020;16(4):5-20.