Towards GPU-enabled serverless cloud edge platforms for accelerating HEVC video coding

Multimedia streaming has become integral to modern living, reshaping entertainment consumption, information access, and global engagement. The ascent of cloud computing, particularly serverless architectures, plays a pivotal role in this transformation, offering dynamic resource allocation, parallel...

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Bibliographic Details
Published in:Cluster computing 2025-02, Vol.28 (1), p.68, Article 68
Main Authors: Salcedo-Navarro, Andoni, Peña-Ortiz, Raúl, Claver, Jose M., Garcia-Pineda, Miguel, Gutiérrez-Aguado, Juan
Format: Article
Language:English
Subjects:
Automation
Cloud computing
Codec
Coding
Computer centers
Computer Communication Networks
Computer Science
Concurrency control
Consumption
Data integrity
Efficiency
Flexibility
Graphics processing units
Infrastructure
Multimedia
Operating Systems
Processor Architectures
Resource allocation
Software services
Streaming media
Streaming services
Video compression
Virtual environments
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Summary:Multimedia streaming has become integral to modern living, reshaping entertainment consumption, information access, and global engagement. The ascent of cloud computing, particularly serverless architectures, plays a pivotal role in this transformation, offering dynamic resource allocation, parallel execution, and automatic scaling-especially beneficial in HTTP Adaptive Streaming (HAS) applications. This study presents an event-driven serverless cloud edge platform with graphics processing units (GPUs), managed by Knative, tailored for video encoding. Two implementations of the High Efficiency Video Coding (HEVC) codec have been encapsulated in the functions: HEVC NVENC (Nvidia Encoder), that uses GPU acceleration, and x265 that only uses CPUs. Experiments focused on measuring the impact of replica requested resources on cold start, scalability and resource consumption with different allocated resources on slim and fat virtual machines (VMs). The best results are obtained when four slim replicas of the functions are deployed on a fat VM with a 8.4% reduction in encoding time for x265 and a 15.2% improvement for HEVC NVENC compared with other deployment scenarios. Comparatively, HEVC NVENC encoding is 8.3 times faster than x265. In multiresolution scenarios, GPU encoding drastically reduces segment encoding time by a factor of 12.4 between non-GPU and GPU-accelerated. These findings are important for live streaming applications where low latency is critical at all stages of the streaming process.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-024-04692-0