Online Reconfiguration of IoT Applications in the Fog: The Information-Coordination Trade-Off

Abstract

The evolution of the Internet of Things (IoT) is driving an extraordinary growth of traffic and processing demands, persuading 5G players to change their infrastructures. In this context, Fog computing emerges as a potential solution, providing nearby resources to run IoT applications. However, the Fog raises several challenges which hinders its adoption. In this paper, we consider the reconfiguration problem, i.e., how to dynamically adapt the placement of IoT applications running on the Fog, depending on application needs and evolution of resource usage. We propose and evaluate a series of reconfiguration algorithms, based on both online scheduling and online learning approaches. Through an extensive set of experiments in a realistic testbed, we demonstrate that the performance strongly depends on the quality and availability of information from both Fog infrastructure and IoT applications. This information mainly concerns the application's resource usage (estimated by the user during the design of the application) and the availability of resources in the infrastructure (collected by commercial off-the-shelf monitoring tools). Finally, we show that a reactive and greedy strategy, which relies on this additional information, can overcome the performance of state-of-the-art online learning algorithms, even in a scenario with inaccurate information.

Security and Makespan Trade-Off Strategy in Fog-Enabled IoT Networks

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