We are delighted to announce that a research paper developed under the mF2C Project, has been accepted for presentation in the IEEE Global Communications Conference (GLOBECOM) 2019, to be held on 9-13 December 2019 in Waikoloa, USA
Title “Engineering a QoS Provider Mechanism for Edge Computing with Deep Reinforcement Learning”,
by Francisco Carpio*, Admela Jukan*, Roman Sosa Gonzales+, Ana Juan Ferrer+
* Technische Universität Braunschweig, Germany
+ ATOS Research, Spain
With the development of new system solutions that integrate traditional cloud computing with the edge/fog computing paradigm, dynamic optimization of service execution has become a challenge due to the edge computing resources being more distributed and dynamic. How to optimize the execution to provide Quality of Service (QoS) in edge computing depends on both the system architecture and the resource allocation algorithms in place. We design and develop a QoS provider mechanism, as an integral component of a fog-to-cloud system, to work in dynamic scenarios by using deep reinforcement learning. We choose reinforcement learning since it is particularly well suited for solving problems in dynamic and adaptive environments where the decision process needs to be frequently updated. We specifically use a Deep Q-learning algorithm that optimizes QoS by identifying and blocking devices that potentially cause service disruption due to dynamicity. We compare the reinforcement learning based solution with state-of-the-art heuristics that use telemetry data, and analyze pros and cons.