CONSENSUS AND RELATED PROBLEMS IN DISTRIBUTED SYSTEM
Consensus And Related Problems In Distributed System.CONSENSUS RELATED PROBLEM IN HINDI Consensus and related problems problem in distributed computing multi-agent systems. Consensus problem in Distributed Systems Consensus in Distributed Systems the process of consensus in distributed systems. Consensus protocols in distributed systems interactive consistency problem in distributed system distributed consensus.
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Consensus and Related Problems in Distributed Systems
1. What is Consensus in Distributed Systems?
Consensus in distributed systems refers to the process by which multiple nodes (computers) agree on a single, consistent state or value despite failures and network issues. It is crucial for ensuring data consistency, fault tolerance, and synchronization in distributed applications like blockchain, cloud computing, and databases.
2. Challenges in Achieving Consensus
- Network Failures: Delays, packet loss, and partitions make communication unreliable.
- Fault Tolerance: Some nodes may crash or behave maliciously (Byzantine faults).
- Synchronization Issues: Nodes may process information at different speeds.
- Scalability: As the system grows, maintaining consensus becomes harder.
- Concurrency Conflicts: Multiple nodes might propose different values at the same time.
3. Popular Consensus Algorithms
- Paxos: A classic fault-tolerant consensus algorithm used in distributed databases.
- Raft: A more understandable alternative to Paxos, used in Kubernetes and etcd.
- Byzantine Fault Tolerance (BFT): Designed for systems where some nodes may act maliciously (e.g., Practical BFT in blockchain).
- Proof-of-Work (PoW) & Proof-of-Stake (PoS): Used in blockchain networks like Bitcoin and Ethereum.
4. Related Problems in Consensus
- Leader Election: Selecting a single leader node in a distributed network.
- Atomic Broadcast: Ensuring that all nodes receive and process the same messages in the same order.
- Split Brain Problem: When a network partition causes two sets of nodes to operate independently, leading to data inconsistency.
- CAP Theorem: A distributed system can only provide two out of three: Consistency, Availability, and Partition Tolerance.
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