Section: New Results

FoG Computing

To this day, the Internet of Things (IoT) continues its explosive growth. Nevertheless, with the exceptional evolution of traffic demand, existing infrastructures are struggling to resist. In this context, Fog computing is shaping the future of IoT applications. It offers nearby computational, networking and storage resources to respond to the stringent requirements of these applications. However, despite its several advantages, Fog computing raises new challenges which slow its adoption down. Hence, there is a lack of practical solutions to enable the exploitation of this novel concept.

In [19], we propose FITOR, an orchestration system for IoT applications in the Fog environment. This solution builds a realistic Fog environment while offering efficient orchestration mechanisms. In order to optimize the provisioning of Fog-Enabled IoT applications, FITOR relies on O-FSP, an optimized fog service provisioning strategy which aims to minimize the provisioning cost of IoT applications, while meeting their requirements. Based on extensive experiments, the results obtained show that O-FSP optimizes the placement of IoT applications and outperforms the related strategies in terms of i) provisioning cost ii) resource usage and iii) acceptance rate. In [46], we propose a novel strategy, which we call GO-FSP and which optimizes the placement of IoT application components while coping with their strict performance requirements. To do so, we first propose an Integer Linear Programming (ILP) formulation for the IoT application provisioning problem. The latter targets to minimize the deployment cost while ensuring a load balancing between heterogeneous devices. Then, a GRASP-based approach is proposed to achieve the aforementioned objectives. Finally, we make use of the FITOR orchestration system to evaluate the performance of our solution under real conditions. Obtained results show that our scheme outperforms the related strategies. We are currently comparing such strategy with other strategies based on online learning mechanisms under various information scenarios (delayed and noisy feedback, inaccurate application load information, etc.).

Last, fog computing also extends the capacities of the cloud to the edge of the network, near the physical world, so that Internet of Things (IoT) applications can benefit from properties such as short delays, real-time and privacy. Unfortunately, devices in the Fog-IoT environment are usually unstable and prone to failures. In this context, the consequences of failures may impact the physical world and can, therefore, be critical. In [28], we present a framework for end-to-end resilience of Fog-IoT applications. The framework was implemented and experimented on a smart home testbed.