Distributed Systems Consensus Algorithms PDF

Distributed Systems Consensus Algorithms Coordination and Agreement in Distributed Systems Consensus Algorithms Distributed Mutual Exclusion (DMX) Time and Global States Consensus Algorithms Consensus algorithms are fundamental components in distributed computing systems designed to...

Distributed Systems Consensus Algorithms Coordination and Agreement in Distributed Systems Consensus Algorithms Distributed Mutual Exclusion (DMX) Time and Global States Consensus Algorithms Consensus algorithms are fundamental components in distributed computing systems designed to enable a group of interconnected nodes to reach an agreement on a shared value or decision despite potential failures, variations in processing speeds, and communication delays. The primary objective is to ensure that all nodes converge to a consistent state, either by agreeing on a common value or reaching a majority decision. These algorithms play a critical role in maintaining the integrity, reliability, and coherence of distributed systems, addressing challenges related to fault tolerance, network partitions, and ensuring consistent behavior across nodes to achieve a unified outcome in the presence of uncertainty and variability. Consensus Algorithms Consensus Aspects State of the System: Consensus is necessary for agreeing on the current state of the distributed system. This includes agreeing on the values of variables, states of data structures, or configurations across all nodes. Data Replication: Consensus is crucial for maintaining consistency among replicas of data distributed across different nodes. It ensures that all replicas are updated in a coordinated manner to reflect the correct state of the system. Distributed Transactions: When multiple nodes participate in a distributed transaction, consensus is required to agree on whether the transaction should be committed or rolled back. This prevents inconsistencies that could arise from partial or conflicting updates. Leader Election: In systems with multiple nodes, especially those employing a master-slave or leader-follower architecture, consensus is necessary for electing a leader. The leader is responsible for coordinating and managing the system's activities. Consensus Algorithms Consensus Aspects Configuration Changes: Changes to the configuration of a distributed system, such as adding or removing nodes, require consensus to ensure that all nodes agree on the new configuration. This is important for maintaining a coherent and well-functioning system. Clock Synchronization: Consensus is often used to synchronize clocks across distributed nodes to ensure a consistent view of time. This is crucial for ordering events and transactions in a meaningful way. Membership and Access Control: Determining the membership of the distributed system and agreeing on access control policies require consensus to avoid unauthorized access and to ensure that all nodes are aware of the system's participants. Consensus Algorithms Consensus algorithms: Paxos Algorithm Raft Algorithm Byzantine Fault Tolerance (BFT) Consensus Algorithms Paxos Algorithm It was introduced by Leslie Lamport in 1989 and has since become a fundamental building block for distributed systems. involving a sequence of phases to reach agreement among a group of nodes. Prepare phase initializes the agreement process, Promise phase ensures that nodes agree on a common proposal number Accept phase confirms the acceptance of a proposed value Learn phase finalizes the agreement. Consensus Algorithms Paxos Algorithm Paxos has certain complexities, such as the potential for deadlock and difficulties in implementation. Its robustness and ability to handle failures make it a foundational algorithm in distributed consensus, influencing subsequent developments and improvements in this field. Paxos is safe (agreement is achieved) and alive (progress is made) even in the face of network partition and node failures. Consensus Algorithms Paxos Algorithm A common use case for Paxos is in distributed databases for ensuring replication consistency. Consider a scenario where a distributed database is deployed across multiple nodes for fault tolerance and high availability. Paxos can be employed to achieve consensus on write operations, ensuring that data updates are replicated consistently across all nodes. In the face of node failures or network partition, Paxos helps maintain the agreed-upon state of the database, ensuring that each node has an identical copy of the data. Consensus Algorithms Paxos Algorithm Paxos is complex as it undergoes multiphases. Hence, Next algorithms are effectively more used. A network partition refers to the situation in a distributed system where communication between certain segments of nodes is disrupted, leading to isolated subgroups that cannot exchange messages or coordinate effectively.. Consensus Algorithms Paxos Algorithm https://www.scylladb.com/glossary/paxos-consensus-algorithm/ Consensus Algorithms Raft Algorithm Introduced by Diego Ongaro and John Ousterhout in 2013 as a response to the complexity of Paxos. Simplifies the consensus problem by breaking it down into three key components: leader election, log replication, and safety. Raft operates with a leader node that manages the consensus process. During leader election, nodes compete to become the leader, and once a leader is elected, it manages the replication of the log across all nodes. Consensus Algorithms Raft Algorithm Leader Election: The first step in the Raft algorithm is electing a leader. Nodes in the system use a randomized timer to periodically send out heartbeats to other nodes. If a follower does not receive a heartbeat from the leader within a certain amount of time, it assumes that the leader has failed and initiates a new leader election. Consensus Algorithms Raft Algorithm https://www.linkedin.com/pulse/building-consensus-distributed-systems-power-paxos-raft-salik-tariq/ Consensus Algorithms Raft Algorithm https://www.linkedin.com/pulse/building-consensus-distributed-systems-power-paxos-raft-salik-tariq/ Consensus Algorithms Raft Algorithm Log Replication: Once a leader is elected, it can accept client requests and replicate them to the other nodes in the system. When a leader receives a client request, it appends the request to its log and sends append entries messages to the other nodes. These messages contain information about the log entry, including the index and term number. Consensus Algorithms Raft Algorithm Committing Entries: Once a log entry has been replicated to a majority of nodes in the system, the leader can send a commit message to all nodes, indicating that the entry has been committed. The other nodes will then apply the entry to their state machine and respond with an acknowledgement message. Consensus Algorithms Raft Algorithm Raft ensures safety by maintaining a consistent and ordered log. The simplicity of Raft makes it more accessible for developers to understand and implement, making it a popular choice in scenarios where ease of comprehension and manageability are prioritized. The clear separation of concerns and comprehensible state transitions make Raft an attractive option for building reliable and maintainable distributed systems. Consensus Algorithms Raft Algorithm https://www.geeksforgeeks.org/raft-consensus-algorithm/ Consensus Algorithms Raft Algorithm Raft ensures safety by maintaining a consistent and ordered log. The simplicity of Raft makes it more accessible for developers to understand and implement, making it a popular choice in scenarios where ease of comprehension and manageability are prioritized. The clear separation of concerns and comprehensible state transitions make Raft an attractive option for building reliable and maintainable distributed systems. Consensus Algorithms Byzantine Fault Tolerance (BFT): Introduced by Leslie Lamport. Tackles the challenge of ensuring consensus even in the presence of malicious or faulty nodes. Unlike traditional consensus algorithms, BFT addresses scenarios where nodes may exhibit arbitrary and Byzantine behaviors, providing a higher level of security and fault tolerance. Consensus Algorithms Byzantine Fault Tolerance (BFT): Byzantine agreement problems involve reaching consensus in the presence of potentially compromised nodes, a critical consideration in secure and mission-critical applications. Byzantine consensus algorithms, though computationally more expensive, offer a level of resilience that is important in scenarios where the integrity and security of the distributed system are considered concerns. Involve nodes exchanging cryptographic signatures to verify the authenticity of messages and voting on the validity of proposed values. Consensus Algorithms Byzantine Fault Tolerance (BFT): https://www.bitstamp.net/learn/blockchain/what-is-byzantine-fault-tolerance-bft/ Consensus Algorithms Byzantine Fault Tolerance (BFT): Byzantine fault tolerance is a measure of the ability of a distributed system to continue operating even if one or more of its components fails. What if a node, or group of nodes, decides to attack the network by transmitting information about false transactions in an attempt to steal Money The ability of the network to resist such an attack and continue operating uninterrupted is known as Byzantine fault tolerance. Consensus Algorithms Byzantine Fault Tolerance (BFT): Byzantine Fault Tolerance is particularly relevant in scenarios where the integrity and security of the distributed system are critical, such as in blockchain networks and systems dealing with sensitive data. Blockchain networks, such as Bitcoin and Ethereum, leverage Byzantine Fault Tolerance to ensure consensus in a trustless and decentralized environment. In a blockchain, nodes (or miners/ validators) validate and agree on the order of transactions even in the presence of malicious actors. Consensus Algorithms Byzantine Fault Tolerance (BFT): Byzantine Fault Tolerance (BFT) is a concept that can be applied to consensus algorithms, including both Proof of Work (PoW) Proof of Stake (PoS) Consensus Algorithms Byzantine Fault Tolerance (BFT): Proof of Work (PoW) Consensus PoW relies on miners solving complex mathematical puzzles to validate transactions and secure the network. This process demands significant computational power, resulting in high energy consumption, as seen in Bitcoin. Consensus Algorithms Byzantine Fault Tolerance (BFT): Proof of Stake (PoS) Consensus Selects validators to create new blocks and validate transactions based on their willing to stake. This approach is more energy-efficient, as validators don't need to perform intense computations. References https://www.scylladb.com/glossary/paxos-consensus-algorithm/ https://www.linkedin.com/pulse/building-consensus-distributed- systems-power-paxos-raft-salik-tariq/ https://www.geeksforgeeks.org/raft-consensus-algorithm/ https://www.bitstamp.net/learn/blockchain/what-is-byzantine-fault- tolerance-bft/ References https://www.blockchain-council.org/blockchain/proof-of-work-vs- proof-of-stake-beginners-guide/ https://www.investopedia.com/terms/p/proof-stake-pos.asp https://bitpay.com/blog/proof-of-work-vs-proof-of-stake/ https://www.techtarget.com/whatis/feature/Proof-of-work-vs-proof-of- stake-Whats-the-difference