Which architecture distributes data processing and storage across multiple locations or devices?

Which architecture distributes data processing and storage across multiple locations or devices?

• A. Edge computing architecture
• B. Centralized computing architecture
• C. Distributed computing architecture
• D. Decentralized computing architecture

Answer: (D)

Distributing data processing and storage across multiple locations or devices means there isn’t a single central point controlling everything; work and data are shared among many nodes in the network. That aligns with decentralized computing architecture, where decision-making and resources are spread across independent nodes, so processing and storage occur across various locations rather than in one central hub. This setup improves resilience and scalability because the system can continue operating even if one location goes down, and you can add more nodes without redesigning a centralized control system. Edge computing moves processing toward data sources, usually at or near specific edge devices, but it tends to focus on proximity to the data source rather than distributing responsibility across a broad network with no central authority. Centralized computing, by contrast, concentrates processing and storage in a single location, which is the opposite of distributing across multiple sites. Distributed computing spreads tasks across machines, but decentralization emphasizes there being no single controlling node, which is why it best matches the notion of distributing processing and storage across multiple locations or devices.

Distributing data processing and storage across multiple locations or devices means there isn’t a single central point controlling everything; work and data are shared among many nodes in the network. That aligns with decentralized computing architecture, where decision-making and resources are spread across independent nodes, so processing and storage occur across various locations rather than in one central hub. This setup improves resilience and scalability because the system can continue operating even if one location goes down, and you can add more nodes without redesigning a centralized control system.

Edge computing moves processing toward data sources, usually at or near specific edge devices, but it tends to focus on proximity to the data source rather than distributing responsibility across a broad network with no central authority. Centralized computing, by contrast, concentrates processing and storage in a single location, which is the opposite of distributing across multiple sites. Distributed computing spreads tasks across machines, but decentralization emphasizes there being no single controlling node, which is why it best matches the notion of distributing processing and storage across multiple locations or devices.