| Full text | |
| Author(s): |
Gama, Eduardo S.
;
Rodrigues-Filho, Roberto
;
Madeira, Edmundo R. M.
;
Immich, Roger
;
Bittencourt, Luiz F.
Total Authors: 5
|
| Document type: | Journal article |
| Source: | 2024 IEEE 8TH INTERNATIONAL CONFERENCE ON FOG AND EDGE COMPUTING, ICFEC 2024; v. N/A, p. 8-pg., 2024-01-01. |
| Abstract | |
One key challenge in Adaptive Video Streaming is the ever-changing edge network conditions at the last mile of access networks. The edge environment is particularly dynamic, influenced by user locations, fluctuation in resource demands and resource capabilities, in contrast to traditional Content Delivery Network (CDN) setups, where content routing decisions are relatively known. To address the dynamism of edge computing environments and to enable applications to better exploit edgecloud computing resources, this article focuses on content steering technology, a recent addition to adaptive video protocols such as HLS and DASH. We present AVENUE as an architecture for Content Steering Services to orchestrate video delivery dynamically across the Edge-Cloud Continuum. This work designs the principles of the Content Steering Service to create a mechanism that involves two modules - monitoring and selector: The monitoring module captures real-time context metrics, and the selector module chooses an edge server according to the Select Server Algorithm. Our study addresses three steering algorithms with different performance profiles. Numerical results demonstrate that different configurations may yield varying network performance in terms of Quality of Experience (QoE), cache hits, and request load. Moreover, the appropriate selection of heuristics in the Selector module can also have a significant impact, depending on the metric being evaluated. (AU) | |
| FAPESP's process: | 21/00199-8 - SMART NEtworks and ServiceS for 2030 (SMARTNESS) |
| Grantee: | Christian Rodolfo Esteve Rothenberg |
| Support Opportunities: | Research Grants - Research Centers in Engineering Program |