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Computational model where components on networked computers coordinate to achieve common goals

Core Idea: Distributed computing is a model where multiple autonomous computers communicate through a network to achieve shared objectives, enabling more powerful computing capabilities, fault tolerance, and geographic distribution of resources.

Key Elements

Core Characteristics

• Component Distribution: System components run on separate networked computers
• Concurrency: Components execute simultaneously across multiple machines
• Independent Failures: Components can fail independently without system collapse
• Resource Sharing: Hardware, software, and data resources are shared across nodes
• Scalability: Ability to add more nodes to increase capacity
• Geographic Distribution: Components can operate across different physical locations
• Heterogeneity: Can incorporate different hardware, operating systems, and networks

Architectural Models

• Client-Server: Clients request services from dedicated servers
  • Centralized resource management
  • Clear separation of concerns
  • Examples: Web applications, database systems
• Peer-to-Peer (P2P): Nodes act as both clients and servers
  • Decentralized structure
  • Self-organizing networks
  • Examples: BitTorrent, blockchain networks
• Three-Tier/N-Tier: Multiple logical layers of functionality
  • Presentation, application logic, data layers
  • Enhanced modularity and scalability
• Microservices: Application composed of loosely coupled services
  • Fine-grained service boundaries
  • Independent development and deployment
  • Often containerized applications

Key Technologies

• Communication Protocols:
  • Remote Procedure Call (RPC)
  • REST/HTTP APIs
  • Message queues (RabbitMQ, Kafka)
  • gRPC and Protocol Buffers
• Middleware:
  • Enterprise Service Bus (ESB)
  • API Gateways
  • Service Mesh
• Orchestration Systems:
  • Kubernetes
  • Apache Mesos
  • Docker Swarm

Consistency Models

• Strong Consistency: All nodes see the same data at the same time
• Eventual Consistency: System will become consistent over time
• CAP Theorem: Trade-offs between Consistency, Availability, and Partition tolerance
• ACID vs. BASE: Traditional vs. distributed transaction approaches

Challenges and Solutions

• Fault Tolerance:
  • Redundancy and replication
  • Failure detection
  • Recovery mechanisms
• Synchronization:
  • Clock synchronization
  • Distributed locking
  • Consensus algorithms (Paxos, Raft)
• Load Balancing:
  • Static and dynamic strategies
  • Geographic load distribution
  • Auto-scaling
• Security:
  • Distributed authentication
  • Network segmentation
  • End-to-end encryption

Evolution and Trends

• Early distributed systems (1970s-1980s)
• Grid computing (1990s)
• Cloud Computing emergence (2000s)
• Containerization and microservices (2010s)
• Edge Computing expansion (2020s)

Additional Connections

• Broader Context: Parallel Computing (related computing model)
• Applications: Big Data Processing (major use case)
• See Also: Distributed Databases (storage aspect)

References

1. "Distributed Systems: Principles and Paradigms" by Andrew S. Tanenbaum
2. "Designing Data-Intensive Applications" by Martin Kleppmann

#distributed-computing #systems-architecture #computer-science #networking

Connections:

Sources:

• From: Hyper-V - Wikipedia