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Vol. 5 No. 1 (2024)
2024 Edition

Articles

AdaptiveGenBackend A Scalable Architecture for Low-Latency Generative AI Video Processing in Content Creation Platforms

Published 2024-11-11

  • Yizhe Chen
    • ,
  • Chunhe Ni
    • ,
  • Hongbo Wang

Yizhe Chen
Computer Science, University of California, San Diego, CA, USA

Chunhe Ni
Computer Science, University of Texas at Dallas, Richardson, TX, USA

Hongbo Wang
Computer Science, University of Southern California, Los Angeles, CA, USA

Abstract

This paper introduces AdaptiveGenBackend, a novel scalable architecture designed to address the growing demand for low-latency generative AI capabilities in content creation platforms. The proposed system leverages distributed computing resources and optimized AI model integration to enable real-time video processing while maintaining high-quality outputs. We present a multi-tiered architectural approach that dynamically balances computational efficiency with output fidelity through adaptive resource allocation mechanisms. Experimental evaluation across diverse workload scenarios demonstrates that AdaptiveGenBackend achieves sub-second response times for interactive preview generation and significantly outperforms baseline architectures in throughput capacity, with up to 94.2 requests per second for lightweight tasks and 10.2 requests per second for computationally intensive operations. The architecture exhibits near-linear scaling up to 12 compute nodes with 89.8% throughput maintenance under peak load conditions. Real-world deployment in production environments revealed a 37% reduction in content production time and a 42% increase in creative iteration frequency. Our approach addresses fundamental technical challenges in generative video processing through specialized model optimization techniques including mixed precision quantization and attention mechanism pruning, which reduce inference latency by 42.3% and 28.9% respectively. The system architecture provides a foundation for future research in temporal coherence optimization and collaborative creation paradigms within AI-enhanced content platforms.

Keywords

Generative AI, Video Processing, Distributed Computing, Content Creation Platforms

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