From Early Algorithms to Today After the rise of various cryptocurrencies such as bitcoins and Ethereum followed by the launch of other cryptocurrencies, blockchain technology has gained fame in recent times. However, the evolution of blockchain technology was a process starting with understanding and defining the problem with the centralized systems to reaching the search for solutions to each of the aforementioned challenges. The evolution of Consensus algorithms that allow for several disengaged nodes in the network to converge at the state of the blockchain are an important aspect of blockchain technology. Various consensus algorithms had to be developed the counter the existing problems.
A few examples of consensus algorithms are proof-of-work, proof-of-stake, and hybrid—which are employed in blockchain systems. Other problems encountered in the development of the technology were security, decentralization, and energy efficiency, which should be considered when selecting a consensus algorithm.
Table of Contents
Some major events in the development of the consensus algorithm
1. The two general problems:
The question of how two generals can agree on an organized attack despite faulty communication. This is a thought experiment by E.A. Akkoyunlu and a few others published in the year 1980. This demonstrated the challenges of coming to a consensus in unstable distributed environments.
2. Byzantine Generals Problem:
This problem was introduced by Leslie Lam Port, Robert Shasta, and Marshall Please in the year 1982 which led to a solution in securing a decentralized system even when there were few malicious participants.
3. Fischer, Lynch, and Paterson Impossibility:
The Fischer, Lynch, and Paterson impossibility also commonly known as FLP impossibility demonstrates that it becomes impossible to come up with a guaranteed consensus in an asynchronous system even when there is a single malicious node. This highlighted a practical problem over theoretical problems and led to the formation of new protocols. Fischer, Lynch, and Paterson introduced this in 1985.
4. Panos Algorithm:
Panos algorithm was introduced by Leslie Lam Port in 1989 in commercial database systems. Panos is a series of protocols that helps to solve consensus problems with networks that contain faulty or unreliable processors. A number of the algorithms ensure consensus with Panos even in cases where some nodes give a faulty impression. It is one of the consensus protocols that are most commonly employed, including in databases.
5. VSR:
This was one of the earliest protocols for keeping such systems consistent and replicated DBS up. An essential forerunner of later consensus algorithms was View stamped Replication VSR. OK and Lisbon introduced this into it in 1988
6. Raft:
Diego Ontario and John Oosterhout invented Raft, a consensus algorithm in 2012. It has similar concepts but aims to be easier to understand, to be easier to use. Conceptually similar to Panos, but designed to be easier to understand and use, but was intentionally designed to be easier to understand and use. It has become more well-liked when developing distributed databases and systems.
7. Chubby:
Chubby is a distributed lock service that Google developed. It uses Panos to achieve consistency amongst replicated nodes to achieve consensus.
8. Zookeeper:
Like Chubby, Apache ZooKeeper is a distributed coordination service that uses consensus protocols, specifically the Lab protocol, to handle configuration data and offer distributed synchronization. Zookeeper was introduced in the year 2008.
9. Bitcoin:
Under the pseudonym Satoshi Nakamoto an anonymous person or group introduced Bitcoin, a new type of decentralized consensus mechanism Proof of Work (PoW). Because it allows users to reach agreements without depending on a central authority, this consensus algorithm is revolutionary for distributed networks. Users finish a difficult computation to make PoW.
10. Proof of Stake (POS):
POS unlike PoW selects validators based on the amount of stake they have in the network, or simply the amount of cryptocurrency they own. With its switch from PoW in 2022, Ethereum is one of the blockchain projects that uses POS, which is more energy-efficient than PoW.
11. Practical Byzantine Fault Tolerance (PBFT):
A consensus algorithm called PBFT is made to withstand Byzantine faults or malicious or malfunctioning nodes. It became popular in enterprise blockchain programs like Hyperledger Fabric
12. Delegated Proof of Stake (DUOS):
As one of the POS types, DUOS allows token holders to select delegates who add the blocks and sign transactions. DUOS is used by blockchain initiatives like EOS and Steam.
Some recent Evolution of Consensus
1. Hybrid Consensus:
Some present-day distributed computing systems use a mix of PoW and POS some examples include Ethereum before transitioning fully to POS. The goals of these hybrid models are decentralization, scalability, and security optimization.
2. Sharing and Layer 2 Solutions:
Many of the modern consensus algorithms, therefore, are targeting Layer 2 solutions and sharing – a process of cutting the blockchain into smaller, easier-to-scale pieces – as a way of increasing throughput while maintaining decentralization.
3. Asynchronous Byzantine Fault Tolerance (AFT):
AFT is a kind of consensus algorithm that is used in projects such as the Federal Hash graph. It enables systems to reach consensus without synchronous communications, which increases their resilience to specific kinds of network failures.
Some famous cryptocurrencies and the c
Applications
1. Databases and Cloud Storage
- Use Case: Keeping data together in the same data center or on multiple servers.
- Consensus Algorithms: Panos, Raft
2. Fault-Tolerant Systems
· Use Case: guaranteeing that, even if some nodes in the distributed system crash, it’ll be able to continue to operate.
· Consensus Algorithms: Panos, Byzantine Fault
3. IoT (Internet of Things)
· Use Case: Safe, reliable sharing and decision-making, that does not violate user privacy while in transit is also sidestepped to ensure true consensus among IoT devices.
· Consensus Algorithms: Proof of Space
4. Supply Chain Management
· Use Case: Authenticity verification and supply chain good tracking.
· Consensus Algorithms: Traditionally practical Byzantine Fault Tolerance (PBFT), Proof of Authority (POA)
5. Voting Systems
· Use Case: A group effort to verify and secure votes in vote-casting electronic voting systems.
· Consensus Algorithms: Delegated Proof of Stake (Duos), BFT, Proof of Stake (POS).
6. Version Control and Collaborative Editing
· Use Case: Keep documents or files in real-time among different users making the same changes.
· Consensus Algorithms: Panos, Raft
7. Smart Grids
· Use Case: Coordination and management of energy flow between energy producers and consumers in decentralized power grids.
· Consensus Algorithms: Byzantine Fault Tolerance (BFT), Proof of Stake (POS)
8. CDNs
· Use Case: To synchronize and distribute content among geographically dispersed servers. Consensus Algorithms: Panos, Raft, POA
9. Financial Systems
· Use Case: Distributed ledger applications for secure and transparent financial transaction systems.
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Conclusion
Consensus algorithms have always evolved because of the necessity of dependable and effective coordination between dispersed systems. The consensus mechanisms have started from the theoretical models like the Byzantine Generals Problem and moved to the modern blockchain solutions and are critical for challenging decentralized networks to provide security, fault tolerance, and consistency.