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

TL;DR

MAPO is a multi-objective model for IoT application placement in fog environments that balances completion time, energy, and cost, improving efficiency and reducing expenses.

Contribution

It introduces a Pareto-based multi-objective placement approach modeling applications with finite state machines for IoT in fog computing.

Findings

Reduces economic cost by up to 27%

Decreases energy requirements by 23-68%

Optimizes completion time by up to 7.3 times

Abstract

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