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The emergence of the Fog computing paradigm that leverages in-networkvirtualized resources raises important challenges in terms of resource and IoTapplication management in a heterogeneous environment offering only limitedcomputing resources. In this work, we propose a novel Pareto-based approach forapplication placement close to the data sources called Multiobjective IoTapplication Placement in fOg (MAPO). MAPO models applications based on a finitestate machine and uses three conflicting optimization objectives, namelycompletion time, energy consumption, and economic cost, considering both thecomputation and communication aspects. In contrast to existing solutions thatoptimize a single objective value, MAPO enables multi-objective energy andcost-aware application placement. To evaluate the quality of the MAPOplacements, we created both simulated and real-world testbeds tailored for aset of medical IoT application case studies. Compared to the state-of-the-artapproaches, MAPO reduces the economic cost by up to 27%, while decreasing theenergy requirements by 23-68%, and optimizes the completion time by up to 7.3times.

MAPO: A Multi-Objective Model for IoT Application Placement in a Fog Environment