Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.16685 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911888488529920 |
|---|---|
| author | Makaya, Christian Grueneberg, Keith Ko, Bongjun Wood, David Desai, Nirmit Wang, Xiping |
| author_facet | Makaya, Christian Grueneberg, Keith Ko, Bongjun Wood, David Desai, Nirmit Wang, Xiping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_16685 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EdgeSphere: A Three-Tier Architecture for Cognitive Edge Computing Makaya, Christian Grueneberg, Keith Ko, Bongjun Wood, David Desai, Nirmit Wang, Xiping Distributed, Parallel, and Cluster Computing 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. |
| title | EdgeSphere: A Three-Tier Architecture for Cognitive Edge Computing |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2405.16685 |