Centralized and Decentralized Computing
Summary
PDF p.158Centralized computing involves data processing and storage in a single location, typically a central server, while decentralized computing distributes these tasks across multiple locations or devices. The choice between these architectures depends on organizational needs for control, resilience, and flexibility.
In plain words
Supplementary — not from your PDFCentralized computing keeps processing and data in one place (a mainframe or central server), which makes control easier but creates a single point of dependence. Decentralized computing spreads the work across many devices or sites, such as blockchain, P2P networks, CDNs and IoT, which improves resilience and flexibility but makes central control harder.
Detailed explanation
PDF p.158-
Centralized Computing
- Definition: All data processing and storage are performed in a single location, usually a central server.
- Dependence: Users and devices rely on the central server for data access and processing.
- Control: The server administrator and organization control security and privacy decisions.
- Examples: Mainframe computers, client-server architectures.
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Decentralized Computing
- Definition: Data processing and storage are distributed across multiple locations or devices.
- Independence: No single device or location is responsible for all data processing and storage.
- Trend: Increasingly important in modern infrastructures for resilience and flexibility.
- Examples: Blockchain, peer-to-peer (P2P) networks, content delivery networks (CDNs), Internet of Things (IoT) devices, distributed databases, Tor network.
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Choosing Between Architectures
- Centralized: Suitable for large organizations needing strict control and management.
- Decentralized: Ideal for situations where resilience and flexibility are prioritized over central control.
Important terms
taken from the text above- Centralized Computing
- All data processing and storage are performed in a single location, usually a central server.
- Dependence
- Users and devices rely on the central server for data access and processing.
- Decentralized Computing
- Data processing and storage are distributed across multiple locations or devices.
- Independence
- No single device or location is responsible for all data processing and storage.
- Trend
- Increasingly important in modern infrastructures for resilience and flexibility.
- Centralized
- Suitable for large organizations needing strict control and management.
- Decentralized
- Ideal for situations where resilience and flexibility are prioritized over central control.
Examples & real-world scenarios
Supplementary — not from your PDF- A mainframe running a bank's core ledger (centralized).
- A CDN serving web content from many edge locations (decentralized).
- A distributed database replicated across regions.
Scenario
A retailer's single central server goes down and every store stops trading. Moving some processing to a decentralized design would keep stores running during an outage.
Common mistakes
Supplementary — not from your PDF- Assuming decentralized is always more secure. It gains resilience but loses central control.
- Forgetting that client-server is an example of centralized computing.
Practical skills
Supplementary — not from your PDF- Choose centralized or decentralized computing for given control and resilience needs.
What I should remember
Key Points PDF p.158-
Centralized Computing
- Single Location: Data processing and storage.
- Dependence: On central server.
- Control: By server administrator and organization.
- Examples: Mainframes, client-server.
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Decentralized Computing
- Distributed: Data processing and storage.
- Independence: No single point of responsibility.
- Trend: Modern infrastructures.
- Examples: Blockchain, P2P networks, CDNs, IoT, distributed databases, Tor.
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Benefits of Decentralized Architecture
- Fault Tolerance: Improved resilience.
- Scalability: Better scalability.
- Security: Unique security features.