Editing Emerging Research Directions (section)

===7.5.5 Latency and Throughput Considerations===

Latency and throughput are two pivotal metrics that directly affect the performance and reliability of edge data persistence systems. Their impact is especially critical in applications requiring real-time response and high data fidelity, such as autonomous vehicles, industrial automation, and AR/VR systems.

Latency refers to the time delay from the generation of data to its availability for consumption or action. Edge computing reduces this delay by processing and storing data closer to the source, circumventing long cloud transmission cycles. However, resource constraints, task prioritization, and variable network conditions at the edge can still introduce latency. According to [1], designing adaptive reference architectures for highly uncertain environments is critical to minimizing latency and ensuring responsiveness.

Throughput, by contrast, defines the volume of data processed or transmitted within a given time. With edge deployments supporting data-intensive applications like federated learning and predictive maintenance, maintaining high throughput is essential. Federated models, as discussed in [3], rely on consistent, rapid data handling across decentralized nodes without raw data sharing, further stressing throughput capabilities.

Achieving a balance between low latency and high throughput presents architectural challenges. Caching strategies and lightweight local databases—such as SQLite and Redis—help accelerate data access and reduce I/O bottlenecks. Recent work in MQTT broker optimization using these tools demonstrates significant performance gains in edge scenarios [7]. Embedded databases like RocksDB and SQLite are also recognized for their fast read/write performance, particularly in constrained environments [9].

Edge object storage, which bypasses hierarchical file systems in favor of flat, scalable models, supports high-throughput access to unstructured data across distributed systems [10]. Partial offloading and adaptive data compression are further techniques that reduce transmission load while preserving speed and data integrity.

Data consistency protocols also impact throughput. Conflict-Free Replicated Data Types (CRDTs) [6] and replicated database systems [5] allow updates to propagate across distributed nodes without costly synchronization steps, maintaining system fluidity. These solutions enable high availability while tolerating intermittent connections and reducing coordination overhead.

As edge systems continue to evolve, future research must address bottlenecks by integrating intelligent scheduling, data-aware caching, and predictive throughput modeling. Techniques like edge-aware sharding [11] and fault-tolerant consensus algorithms [2] are among promising directions to ensure edge persistence systems can scale effectively while delivering low-latency and high-throughput performance.