Towards a Distributed Storage Framework for Edge Computing Infrastructures

Due to the continuous development of Internet of Things (IoT), the volume of the data these devices generate are expected to grow dramatically in the future. As a result, managing and processing such massive data amounts at the edge becomes a vital issue. Edge computing moves data and computation closer to the client enabling latency- and bandwidth-sensitive applications, that would not be feasible using cloud and remote processing alone. Nevertheless, implementing an efficient edge-enabled storage system is challenging due to the distributed and heterogeneous nature of the edge and its limited resource capabilities. To this end, we propose a lightweight hybrid distributed edge/cloud storage framework which aims to improve the Quality of Experience (QoE) of the end-users by migrating data close to them, thus reducing data transfers delays and network utilization. The proposed edge storage component (ESC) exploits the Dynamic Lifecycle Framework, in order to enable transparent and automated access for containerized applications to remote workloads. The effectiveness of the ESC is evaluated by employing a number of resource utilization and Quality of Service (QoS) metrics.