Ricerc@Sapienza

Fog Computing is today a widely used computing paradigm, since it is able to drastically reduce service latency by physically moving the computing units in a location that is near to the end users. The core feature of Fog Computing is that the computation is distributed within a set of Fog nodes, that are usually spread across a geographic domain, in general the user requests a service to the nearest node, and the installation of nodes at the cellular base stations is straightforward. In this context, we wonder how we can enable these nodes to share resources, indeed due to their physical position some of them can be overloaded and others can be completely idle. Moreover, in a green-oriented environment, a device could be backed up with a battery and some scheduling decision could be made taking into account how much energy they will require. The role of this project is to study algorithms and solutions to enable the cooperation of fog nodes by using the Reinforcement Learning approach, optimizing latency, energy consumption and computation performances. Reinforcement Learning is a clear step forward with respect to all of the fixed strategies that come into play in this context since it enables an agent to learn scheduling policies by means of the experience that is driven by reward signals. The developed strategies will be studied with simulations, based on event simulators but also by using a pseudo-real deployment of fog nodes based on Single-Board-Computers (SBCs) like the Raspberry Pis. Indeed, thanks to their low cost and to their non-negligible computing power they can be easily envisioned as a Fog Computing environment.