Overlay Networks for Edge Management

Abstract

Edge computing has emerged as a solution to address existing limitations of cloud computing for bandwidth-heavy and time-sensitive applications, by moving (some) computations from bandwidth saturated Cloud infrastructures closer to client devices, where data is effectively produced and consumed. However, existing materializations of the edge computing paradigm take limited advantage of computational and storage power that exists in the edge and between client devices and the cloud. Most of these leverage static hierarchical topologies (e.g., Fog Computing) to pre-process data before sending it to the Cloud, which limits the advantages that can be extracted from the edge computing paradigm. In the past, peer-to-peer systems have sought to tackle the challenges of increasing scalability and availability for very large systems, with a large number of solutions being proposed namely, distributed overlay networks for resource management. In this paper, we argue that the clever adaptation of peer-to-peer solutions can enable novel applications to fully exploit the potential of the edge. In particular, we study the viability of taking advantage of specialized overlay networks in edge environments to enable the management of a large number of computational resources. Contrary to previous proposals, that assume the environment to be composed of mostly homogeneous devices, our proposal embraces existing heterogeneity and exploits the location of computational resources to devise a (partially) self-organizing overlay network that can be exploited both to provide membership information to applications, but also do efficiently disseminate management information across edge devices. We have conducted an experimental evaluation using container-based emulation in an heterogeneous network composed by 100 devices, with results showing that our protocol is able to maximize the bandwidth usage of the system, allowing more data to flow throughout the network, while retaining high robustness to failures.