2024/05/26 by Christian Makaya, Makaya, Christian, Keith Grueneberg +9
Computer Science · Engineering · #Cognitive Computing and Networks #Distributed #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #Parallel #Robotics and Automated Systems #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2405.16685
openalex publication_date 2024/05/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Computing at the edge is increasingly important as Internet of Things (IoT) devices at the edge generate massive amounts of data and pose challenges in transporting all that data to the Cloud where they can be analyzed. On the other hand, harnessing the edge data is essential for offering cognitive applications, if the challenges, such as device capabilities, connectivity, and heterogeneity can be overcome. This paper proposes a novel three-tier architecture, called EdgeSphere, which harnesses resources of the edge devices, to analyze the data in situ at the edge. In contrast to the state-of-the-art cloud and mobile applications, EdgeSphere applications span across cloud, edge gateways, and edge devices. At its core, EdgeSphere builds on Apache Mesos to optimize resources usage and scheduling. EdgeSphere has been applied to practical scenarios and this paper describes the engineering challenges faced as well as innovative solutions.