The tremendous increase in the number of mobile devices and the proliferation of all kinds of new types of sensors is creating new value opportunities by analyzing, developing insights from, and actuating upon large volumes of data streams generated at the edge of the network. While general purpose processing required to unleash this value is abundant in Cloud datacenters, bringing raw IoT data streams to the Cloud poses critical challenges, including: (i) regulatory constraints related to data sensitivity, (ii) significant bandwidth costs and (iii) latency barriers inhibiting near-real-time applications. Edge Computing aspires to extend the traditional cloud model to the "edge of the network", to deliver low-latency, bandwidth-efficiencies and controlled privacy. For all the commonalities between the two models, transitioning the provisioning and orchestration of a distributed analytics platform from Cloud to Edge is not trivial. The two models present totally different cost structures such as price of bandwidth, data communication latency, power density and availability. In this paper, we address the challenge associated with transitioning scalable provisioning from Cloud to distributed Edge platforms. We identify current scalability challenges in Linux container provisioning at the Edge; we propose a novel peer-to-peer model taking on them; we present a prototype of this model designed for and tested on real Edge testbeds, and we report a scalability evaluation on a scale-out virtualized platform. Our results demonstrate significant savings in terms of provisioning latency and bandwidth utilization.
展开▼
