Intelligent Edge-Cloud Architecture for Ultra-Low Latency Real-Time Remote Musical Collaboration
Keywords:
Adaptive network routing, AI-Based latency prediction, Audio signal processing, Cloud synchronization, Edge computing, Real-Time musical collaboration, Ultra-low latencyAbstract
This work explores the design and implementation of an intelligent edge-cloud architecture to enable ultra-low latency real-time remote musical collaboration. In distributed musical environments, maintaining precise timing and synchronization is critical, as even minor delays can negatively impact performance quality. Conventional cloud-centric systems often struggle to meet these strict latency requirements due to centralized processing, longer transmission paths, and network variability. To overcome these challenges, this study proposes a hybrid framework that combines the strengths of edge computing and cloud-based coordination. In the proposed architecture, edge nodes are strategically deployed near users to perform delay-sensitive operations such as audio capture, preprocessing, mixing, and temporary buffering. This localized processing significantly reduces round-trip communication time. The cloud layer complements this by managing global synchronization, coordinating multiple participants, and applying intelligent algorithms for latency prediction and compensation. Additionally, an adaptive network control mechanism dynamically optimizes routing paths and bandwidth allocation to maintain consistent performance under varying network conditions. The system was evaluated through a series of experiments involving multiple users, high-resolution audio streams, and mixed network environments, including 5G and fiber connections. Key performance indicators such as end-to-end latency, jitter, packet loss, and synchronization accuracy were measured and analyzed. The results indicate that the proposed solution achieves an average latency of approximately 15 milliseconds, which falls within the acceptable threshold for real-time musical interaction. Furthermore, improvements in synchronization stability and reduction in packet loss were observed compared to traditional approaches. Overall, this work demonstrates that integrating edge intelligence with cloud capabilities provides an effective and scalable solution for real-time collaborative applications, particularly in latency-sensitive domains such as remote music performance.
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