# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 3 VBDA Architectures and Core Technologies
### System-Level Architectural Models in VBDA  
Building on the evolutionary trends outlined earlier, this section synthesizes the VBDA architecture landscape into four core system-level models: Centralized, Cloud-Centric, Edge Computing, and Hybrid Cloud-Edge. These models reflect the prevailing deployment strategies in contemporary systems, each characterized by specific trade-offs in latency, scalability, privacy, cost, and complexity. They serve as foundational blueprints for designing and optimizing VBDA systems across diverse operational contexts. Table 2 summarizes a structured comparison of these architectures, outlining their respective strengths and limitations in real-world application scenarios. Centralized architectures offer simplicity and low cost, making them suitable for small-scale or archival VBDA systems. However, they rely on a monolithic design with single-node processing, resulting in poor scalability and high latency. A critical risk is the Single Point of Failure (SPOF), where the failure of a central node disrupts the entire system. These architectures are best for static environments with low-performance demands [6; 100]. Cloud-Centric architectures leverage elastic computing and storage, supporting large-scale analytics and model training, while powerful, their dependency on remote data transfer leads to latency and privacy challenges, limiting their use in real-time scenarios. As shown by [PERSON] et al. (2021) [101] and [PERSON] et al. (2020) [102], they are most effective for batch analytics, archival processing, and AI training pipelines. \begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Architecture** & **Key Characteristics** & **Advantages** & **Limitations** & **VBDA Suitability** & **Reference** \\ \hline \multirow{3}{*}{**Centralized**} & Single-node processing: local storage: no distribution. & Low deployment cost; simple management control. & High latency; poor scalability; privacy risks; SPOF. & Limited: Suitable for small-scale or archival VBDA. & [PERSON] et al. (2022) [6] \\ \hline \multirow{3}{*}{**Cloud-Centric**} & Compute and storage offloaded to cloud; centralized orchestration. & High scalability; deep learning support; elastic computing; lower hardware burden. & Latency from data upload; privacy concerns; higher recurring cost. & Moderate to High: Batch analytics, model training. & [PERSON] et al. (2021) [101] \\ \hline \multirow{3}{*}{**Edge Computing**} & Local inference near data source, minimal cloud dependency. & Ultra-low latency; enhanced privacy; bandwidth-efficient real-time response. & Limited compute; synchronization overhead; hardware heterogeneity. & High: Suitable for real-time apps (e.g., smart cities, surveillance). & [PERSON] et al. (2022) [104] \\ \hline \multirow{3}{*}{**Hybrid Cloud-Edge**} & Edge handles real-time inference; cloud for increasing storage, orchestration. & Balanced latency and scalability. & Complex deployment; resource orchestration, FL, adaptive workload distribution. & Very High: Best for large-scale, real-time VBDA systems. & [PERSON] et al. (2020) [107] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of VBDA system architectures. Edge computing architectures enable low-latency, privacy-preserving analytics by performing inference close to data sources. Ideal for real-time applications (e.g., smart surveillance), they reduce bandwidth usage and ensure timely responses. However, edge devices face hardware constraints and synchronization complexity [104; 105], requiring lightweight models and careful orchestration. Hybrid cloud-edge architectures integrate the advantages of both paradigms, supporting real-time processing at the edge while utilizing the cloud for training, storage, and system orchestration. They support FL and dynamic workload allocation, making them highly scalable and adaptive [107; 109]. While complex to deploy, they offer the best trade-off for future-proof VBDA systems across diverse and dynamic environments.