QUIC(K) Communication for GPU Virtualization in Edge Computing

The integration of graphics processing units through virtualization offers significant potential to optimize resource utilization in distributed systems. Particularly in edge computing scenarios, the architecture of edge devices can be simplified by consolidating GPU usage at the edge gateway, which offers the improvement of the GPU utilization by sharing the GPU by multiple edge client devices. However, the effectiveness of this approach is heavily contingent upon network communication, which in edge environments often lacks the high-speed and low latency links as well as specialized protocols available in traditional data center settings for high performance computing, especially if wireless networks are deployed. Therefore, our study aims to comprehensively analyze and evaluate the performance implications of using the conventional transmission control protocol (TCP) versus the emerging QUIC protocol in such environments. We specifically focus on assessing QUIC's unique features, including concurrent streams, stream cancellation, and stream prioritization, which are not inherently present in TCP. Furthermore, we investigate the potential performance gains achievable through the adoption of asynchronous API calls, aiming to provide insights into optimizing GPU utilization in edge computing. We show the advantages of most of QUIC's features and asynchronous API calls by an experimental evaluation.