Editing Emerging Research Directions (section)

===7.5.7 Edge Persistence in Modern Frameworks===

'''Integration with Kubernetes and K3s'''

Modern edge computing frameworks increasingly rely on Kubernetes (K8s) and its lightweight derivative K3s to manage containerized workloads at the edge. These orchestration platforms allow edge nodes to operate with greater autonomy and reliability, particularly in disconnected or resource-constrained environments. Persistent Volumes (PVs) in Kubernetes facilitate stable, long-term data storage across container lifecycles, a key requirement for edge persistence [1][2]. K3s, designed specifically for edge deployments, reduces overhead while maintaining compatibility with standard Kubernetes storage classes, enabling efficient orchestration and data management across distributed edge clusters [3].

'''Leveraging Persistent Volumes in Edge Clusters'''

Persistent Volumes enable critical applications to maintain data integrity during container restarts, node failures, and network partitions. This is especially important in edge environments where connectivity is often intermittent and fault tolerance is paramount. Strategies such as replication, snapshotting, and dynamic provisioning have been adapted from core cloud infrastructure to support edge persistence more efficiently. Lightweight block storage (e.g., SQLite) and in-memory solutions (e.g., Redis) are often used to store state and cache frequently accessed data, thereby reducing latency and maintaining responsiveness [7].

'''Edge DBs and Lightweight Orchestration Tools'''

Embedded and lightweight databases such as SQLite, DuckDB, and RocksDB are increasingly used in edge deployments due to their minimal resource requirements and robust offline capabilities [9]. These databases can seamlessly integrate with messaging systems like MQTT for real-time processing and data synchronization. To ensure consistency and eventual convergence, conflict-free replicated data types (CRDTs) have emerged as a popular model, allowing updates to propagate and resolve autonomously across distributed nodes [6]. In tandem, orchestration tools like K3s simplify cluster management while enabling operators to deploy, monitor, and update stateful services with minimal effort [3][7].