Key Concepts in Distributed Algorithms to Know for Parallel and Distributed Computing

Distributed algorithms are key to ensuring reliable communication and coordination among multiple nodes in a system. They tackle challenges like consensus, leader election, and fault tolerance, making them essential for building robust parallel and distributed computing applications.

1. Consensus algorithms (e.g., Paxos, Raft)

   • Ensure that multiple distributed nodes agree on a single value or state, even in the presence of failures.
   • Paxos is known for its theoretical foundation, while Raft emphasizes understandability and practical implementation.
   • Both algorithms handle network partitions and node failures, making them crucial for fault-tolerant systems.

2. Leader election algorithms

   • Facilitate the selection of a single node as the coordinator or leader among distributed nodes.
   • Common algorithms include Bully and Ring, which differ in their approach to electing a leader.
   • Leader election is essential for coordinating tasks and ensuring consistency in distributed systems.

3. Distributed mutual exclusion

   • Provides a mechanism for ensuring that multiple processes do not access shared resources simultaneously.
   • Algorithms like Ricart-Agrawala and Token Ring are used to manage access to critical sections.
   • Essential for maintaining data integrity and preventing race conditions in distributed environments.

4. Clock synchronization algorithms

   • Ensure that distributed nodes maintain a consistent view of time, which is critical for coordination.
   • Algorithms such as NTP (Network Time Protocol) and Berkeley Algorithm help synchronize clocks across nodes.
   • Accurate timekeeping is vital for event ordering and consistency in distributed systems.

5. Distributed snapshot algorithms

   • Capture a consistent global state of a distributed system at a specific point in time.
   • Algorithms like Chandy-Lamport allow for non-blocking snapshots, enabling ongoing operations during the process.
   • Useful for debugging, recovery, and maintaining consistency in distributed applications.

6. Gossip protocols

   • Facilitate information dissemination among nodes in a decentralized manner, mimicking social gossip.
   • Nodes periodically exchange state information, ensuring eventual consistency across the system.
   • Effective for large-scale systems where centralized coordination is impractical.

7. Distributed hash tables (DHTs)

   • Provide a decentralized method for storing and retrieving key-value pairs across distributed nodes.
   • Algorithms like Chord and Kademlia enable efficient lookups and data distribution.
   • DHTs are foundational for peer-to-peer networks and scalable distributed applications.

8. Byzantine fault tolerance algorithms

   • Address the challenges posed by nodes that may act maliciously or unpredictably.
   • Algorithms like PBFT (Practical Byzantine Fault Tolerance) ensure system reliability despite faulty nodes.
   • Critical for applications requiring high security and trust, such as blockchain technologies.

9. Distributed spanning tree algorithms

   • Construct a spanning tree across a distributed network to facilitate efficient communication.
   • Algorithms like Spanning Tree Protocol (STP) help prevent loops and ensure optimal routing.
   • Essential for network management and resource allocation in distributed systems.

10. Distributed graph algorithms

    • Enable processing and analysis of graph structures across distributed nodes.
    • Algorithms like PageRank and Minimum Spanning Tree can be adapted for distributed environments.
    • Important for applications in social networks, web search, and network analysis.