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Emerging Research Directions
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===7.5.2 Architectural Models for Edge Storage=== '''Overview''' The primary goal of edge computing is to shift data processing closer to the edge, in doing so it reduces latency. Although this decentralized approach also brings challenges on how data is stored. Architectural models for edge persistence must balance [1] : #performance #scalability #consistency '''Centralized Storages''' In a centralized model edge nodes collect data and are sent to a central node. This model is prominent in cloud computing which simplifies data management, but also introduces latency and single point of failure [2]. Centralization offers maintainability and control over data policies. It also is subjected to vulnerabilities in network failures[3]. '''Decentralized Storages''' Decentralized storage involves storing and processing data in edge devices across a distributed system of nodes. This model offers fault tolerance and a reduction in latency, but a higher complexity of data management[4]. It offers better support for real-time applications, but a more complex model in need of more data synchronizations and data consistency[5]. '''Hierarchical Storage Architectures''' Hierarchical models organize storage across tiers: *Tier 1 (Device Level): Sensors, gateways, or IoT devices store small amounts of critical data locally. *Tier 2 (Edge Nodes): More capable edge servers store larger volumes, perform processing, and temporarily cache data. *Tier 3 (Cloud/Core): Central data centers serve as long-term storage and coordination points16. This tiered approach helps balance performance and cost while offering flexibility in data handling. '''Peer-to-Peer and Mesh-Based Storage''' Peer-to-peer (P2P) or mesh-based architectures involve edge nodes communicating directly to share and replicate data. These systems are used in scenarios with unreliable connectivity, employing techniques like gossip protocols, data sharding, and replication to maintain availability and redundancy[6].
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