Blockchain Consensus Mechanisms and Scalability


A visual representation of blockchain technology, illustrating interconnected blocks and Blockchain Consensus Mechanisms.

Blockchains have introduced trust minimization to use cases like monetary policy (e.g., Bitcoin) and digital asset trading (e.g., DEXs). However, they face challenges in matching the speed and cost efficiency of traditional systems, leading to high transaction costs and doubts about their ability to support real-time, high-value use cases.

Scalability, often linked to transaction speed, is both the crypto industry’s biggest challenge and opportunity. Current cryptocurrency transactions are slower than traditional payment methods, but ongoing developments in blockchain consensus mechanisms aim to overcome this bottleneck.

Scalability is crucial for smart contracts to become the backbone of industries like finance, supply chain, and gaming. Different consensus mechanisms in blockchain, including privacy-preserving ones, are being explored to enhance transaction speed and cost efficiency.

This post highlights blockchain scalability by contrasting it with traditional computing and examining the tradeoffs of scaling the execution, storage, and consensus layers. We’ll select three blockchain consensus mechanisms to demonstrate their role in improving scalability.

Understanding Blockchains vs. Traditional Computing

To appreciate the challenges of scaling blockchains, it’s crucial to grasp why blockchain computing differs fundamentally from traditional computing. Blockchains are particularly valuable for several reasons:

  1. Deterministic computation: The predefined logic in blockchain smart contracts executes exactly as intended, ensuring a high level of certainty.
  2. Credible neutrality: Without a central administrator or special privileges, blockchains enable anyone to submit transactions without fear of censorship or bias.
  3. End-user verification: Blockchain ledgers and the supporting code are open for anyone worldwide to audit, providing transparency and security.

At their core, blockchains manage a ledger of data, representing anything from asset ownership to contract states. This data management is facilitated by two main participant groups: block producers and full nodes.

Block producers handle unconfirmed transactions, validate them, and organize them into blocks. Depending on the consensus mechanisms in blockchain, these producers are called miners in Proof-of-Work (PoW) systems or validators in Proof-of-Stake (PoS) systems. Both PoW and PoS act as blockchain consensus mechanisms to ensure the network’s security and resilience against censorship.

Once block producers submit blocks, full nodes independently verify and either accept or reject these blocks. Full nodes, which include end-users, exchanges, and RPC providers, play a critical role in maintaining the integrity of the blockchain by rejecting any invalid blocks. This ensures that even if the majority of block producers are compromised, the ledger remains secure as long as a sufficient number of honest full nodes exist.

To address scalability, it’s important to consider different consensus mechanisms in blockchain, including innovative approaches like privacy-preserving blockchain consensus mechanisms. These mechanisms aim to balance scalability with security and privacy. In our exploration, we will select three blockchain consensus mechanisms to illustrate their impact on blockchain performance and scalability.

The Scalability Challenge in Traditional Blockchain Models

Traditional blockchain models face a critical limitation: achieving scalability often requires trade-offs with decentralization, security, or both. For example, a network that is scalable and decentralized needs to incentivize many participants to maintain high security. On the other hand, a network that is both scalable and secure tends to increase the cost of running a node, which can reduce decentralization. Lastly, networks that are decentralized and secure usually keep node requirements low and the cost of attacks high but face significant scalability bottlenecks.

In contrast, traditional computing environments do not prioritize decentralization. They aim for efficiency and are typically managed by centralized, for-profit entities. This centralization naturally lowers costs and increases speed because the system doesn’t need to ensure independently verifiable computation by end-users.

The trust model in traditional computing relies on brand reputation and legal agreements. In blockchain networks, however, trust is based on cryptography and game theory, offering independent verifiability and often supporting user participation. This distinction highlights why the trust model of traditional computing cannot align with blockchain networks, which are designed to avoid chokepoints like external influence, single points of failure, and non-auditable processes.

This brings us to the essence of blockchain scalability: how can blockchains achieve the speed and cost efficiency of traditional computing environments while preserving the strong trust-minimization properties of security and decentralization? The answer lies in exploring different consensus mechanisms in blockchain. Specifically, we need to evaluate how blockchain consensus mechanisms can be designed to improve scalability without compromising security and decentralization.

Emerging solutions, including privacy-preserving blockchain consensus mechanisms, offer promising pathways. In this context, we’ll select three blockchain consensus mechanisms to analyze their potential in achieving a scalable, secure, and decentralized blockchain environment.

Three Key Properties of Blockchain Scaling

Blockchain scaling can be categorized into three key areas: execution, storage, and consensus. Each property addresses a core problem in blockchain performance, and scaling one often affects the others.

Blockchain Execution

Blockchain execution refers to the computation needed to process transactions and perform state changes. This includes verifying transaction validity, such as checking signatures and token balances, and executing the on-chain logic required for state updates. State changes occur when full nodes update their ledger to reflect new token transfers, smart contract modifications, or data storage.

Scalability in blockchain execution is often measured in transactions per second (TPS), but fundamentally, it involves increasing the number of computations per second. Since transactions vary in complexity and cost, the challenge lies in processing more computations without significantly raising the hardware demands on full nodes.

To address these challenges, we explore various blockchain consensus mechanisms that aim to enhance execution scalability. By selecting and analyzing three blockchain consensus mechanisms, we can better understand their role in enabling higher computation rates. These different consensus mechanisms in blockchain are crucial for balancing scalability with decentralization and security.

Additionally, privacy-preserving blockchain consensus mechanisms are being developed to improve execution scalability while maintaining user privacy and security. These innovations ensure that as blockchains scale, they continue to provide trust-minimization and verifiability without overly burdening full nodes.

Blockchain Storage and Consensus: Key Challenges in Scaling

Blockchain Storage

Blockchain storage encompasses the data full nodes must maintain, consisting of two primary forms:

  1. Historical Data: This includes all past transactions and block data, such as transaction details (origin and destination addresses, amounts, signatures) and block metadata (Merkle root, nonce, previous block hash). Although this data doesn’t require quick access, at least one honest node must make it available for download.
  2. Global State: This refers to the current snapshot of all data accessible to smart contracts, like account balances and smart contract variables. The global state acts as the blockchain’s database, crucial for validating new transactions and state changes. It is typically stored in structures like Merkle trees, allowing quick access and modification by full nodes.

Full nodes rely on historical data to sync initially and on the global state to validate new blocks and execute state changes. As blockchain storage grows, computational demands increase, potentially leading to state bloat, where syncing and running full nodes become more challenging. This issue highlights the need for scalable solutions that allow blockchains to handle more data without escalating storage requirements for nodes.

Blockchain Consensus

Blockchain consensus mechanisms are fundamental to ensuring that nodes in a decentralized network agree on the blockchain’s current state. These mechanisms, like Proof-of-Work (PoW) and Proof-of-Stake (PoS), aim to achieve an honest majority despite potential malicious actors, ensuring transactions are final and irreversible.

Scaling the consensus layer involves finding ways to reach finality faster and more cost-effectively while maintaining trust minimization. Exploring different consensus mechanisms in blockchain helps address this challenge. For instance, privacy-preserving blockchain consensus mechanisms offer innovative approaches to maintain user privacy while scaling the network. In this context, we can select three blockchain consensus mechanisms to analyze their effectiveness in enhancing scalability and efficiency.

Scaling the Execution Layer: Approaches and Tradeoffs

Below are five approaches currently being implemented to scale the execution layer of blockchains, each with its own advantages and tradeoffs. In practice, some of these strategies are combined to enhance execution capacity.

Vertical Scaling of Validator Hardware Requirements

One way to scale blockchain execution is by increasing the hardware capabilities of block producers. By requiring more powerful hardware, each validator can execute more computations per second.

Advantages: A decentralized network of high-computing validators supports larger blocks, faster block times, and lower transaction costs, while preserving on-chain composability between smart contracts. This scaling method also supports high-frequency trading, gaming, and other latency-sensitive applications, offering better trust minimization compared to traditional computing models.

Tradeoffs: The increased cost of running validators can hinder network decentralization. As validator hardware becomes more expensive, fewer participants may be able to afford the infrastructure needed to maintain the network. Over time, this could lead to centralization, undermining the core principles of blockchain. Higher node costs also make it more difficult for end-users to verify on-chain activity directly, which affects trust minimization.

Horizontal Scaling via Multi-Chain Ecosystems

Horizontal scaling involves using multiple independent blockchains or sidechains within a single ecosystem. This distributes transaction computation across many separate blockchains, each with its own set of block producers and execution capacity.

Advantages: Multi-chain ecosystems allow individual blockchains to have customized features like node hardware specifications, privacy settings, gas token usage, and virtual machine (VM) configurations. This flexibility often leads to the development of dApp chains, where each blockchain specializes in supporting specific decentralized applications (dApps). Multi-chain ecosystems also isolate security risks—one chain’s security design doesn’t necessarily affect other chains in the ecosystem.

Tradeoffs: Each blockchain in the ecosystem needs to establish its own security model using a native token, often inflationary in the early stages. As the ecosystem matures, it may be challenging to transition to a more sustainable economic model driven by on-chain user fees. Moreover, multi-chain systems face composability challenges, as dApps and tokens may not always interact seamlessly across different blockchains.

Horizontal Scaling via Execution Sharding

Execution sharding is a form of scaling where a single blockchain supports parallel execution across multiple shards. Each shard functions as a separate blockchain, while a main chain coordinates and synchronizes the shards.

In execution sharding, validators are randomly assigned to different shards, and nodes only execute transactions on one shard at a time. The risk of corrupting any single shard is minimized through this rotation.

Advantages: Since all execution shards pull from the same pool of validators, new shards don’t require additional security measures. With a large enough pool of validators, each shard can achieve high security. Execution sharding avoids the need to increase node hardware requirements, as nodes handle only one shard’s transactions at a time. Shards can be configured to meet the unique needs of specific use cases.

Tradeoffs: Flexibility is limited as nodes must be capable of supporting computations across all shards. There’s also a practical limit to the number of shards a blockchain can accommodate due to the computational load on the main chain and the risk of insufficient nodes per shard. Furthermore, shared security models create potential vulnerabilities, as a weakness in one shard may affect all others.

Horizontal Scaling via Modularity: A New Approach

Another method for horizontal scaling involves modular blockchains, where different blockchain components—such as execution, data availability (DA), and consensus—are isolated into distinct layers. A popular approach within modular blockchain designs is the use of rollups, which move transaction computation and state off-chain while storing the transaction data on-chain. State changes computed off-chain are verified on-chain using zero-knowledge proofs (zk-rollups) for proactive validation, or fraud proofs for retrospective validation (optimistic rollups).

Advantages: Modular blockchains offer several benefits, including offloading transaction execution and state management to off-chain environments that are cheaper, leaner, and capable of higher throughput. Despite this off-chain execution, the security of the blockchain’s base layer is preserved since the consensus process—verifying the validity of off-chain computation—is conducted by an existing decentralized base layer (i.e., L1). This means that the computational bandwidth of the base layer blockchain is used more efficiently because full nodes don’t need to process every transaction but only verify succinct proofs and store limited transaction data.

Rollups also offer trust minimization features, such as escape hatches, which allow users to withdraw their funds and submit them to the base layer blockchain if the rollup network fails. Many modular systems reduce consensus costs over time by sharing verification costs among users, particularly with zk-rollups, where the costs of verifying proofs decrease as the network scales. Furthermore, modular blockchains can operate with a 1-of-n trust model, meaning only one honest node is required to ensure the accuracy and liveness of the computation.

Tradeoffs: While modular blockchains offer scalability, they may not be as fast or inexpensive as sidechains or standalone blockchains due to the reliance on a base layer blockchain’s limited and sometimes costly block space for security. Current modular designs also face upgradability risks, which may require governance intervention and could result in liquidity fragmentation. Additionally, composability issues may arise if some decentralized applications (dApps) remain on the base layer while others operate on off-chain execution layers. Finally, implementing a rollup or other modular blockchain designs is a more complex process compared to launching a new standalone blockchain.

Payment and State Channels: Enhancing Blockchain Scalability

Payment and state channels are innovative solutions for blockchain scaling, allowing users to lock cryptocurrency into a multisig smart contract and perform transactions off-chain. These transactions, which represent asset ownership transfers or state changes, require no on-chain activity until the channel is opened or closed. The multisig contract ensures the accurate settlement of the channel, with users cryptographically signing each interaction, paired with a nonce to verify the correct order of transactions.

Advantages: Payment and state channels provide real-time cryptocurrency transfers with zero cost and near-instant latency. These channels make micropayments viable, something that traditional blockchain networks cannot support due to high transaction fees. They also offer the ability to settle the cryptocurrency locked in the channel on-chain quickly if both parties agree, enhancing overall transaction speed.

Tradeoffs: A significant drawback of state/payment channels is that both parties involved must remain online to prevent using outdated messages for settlement on the blockchain. This often requires watchtowers to monitor the channel and safeguard user funds. Additionally, payment channels must be pre-funded with liquidity, which can result in capital inefficiency, especially for larger transactions.

Another challenge is the efficient routing of payments across a network of channels, which can lead to failed transfers or the creation of centralized hub-and-spoke models to ensure adequate liquidity and short payment routes. State and payment channels are typically most effective among a fixed set of participants and face difficulties when applied to dynamic or ever-changing groups. Additionally, ownership issues can arise when channels attempt to represent objects without a clear logical owner, such as liquidity pools on decentralized exchanges (DEXs).

Scaling Data Storage: Approaches and Considerations

As blockchain networks grow, scaling the storage layer becomes crucial to handle increasing amounts of data. Below are six different methods currently being employed to enhance blockchain storage capacity, often in combination for greater efficiency.

Vertical Scaling of Blockchain Nodes

Vertical scaling in blockchain storage involves enhancing the hardware requirements for full nodes, similar to its application in blockchain execution.

Advantages: Higher storage limits enable full nodes to store more extensive historical data and state information, offering large volumes of cheap storage. This direct storage capability simplifies access to on-chain data, as there are no additional storage layers or dependencies.

Tradeoffs: The increasing data storage demand over time raises the cost of running full nodes, threatening network decentralization. Reduced decentralization can lower the trust-minimized guarantees that data will remain available and accurate. Additionally, state bloat may slow down block execution, straining the network further.

Data Sharding on Layer-1 Blockchains

Data sharding splits the storage of the blockchain ledger or its reconstruction data across multiple shards, lowering individual node storage requirements to a single shard or a few shards.

Advantages: This approach boosts storage capacity without raising individual node hardware requirements, supporting decentralization by enabling more users to run nodes. Data sharding also enhances rollups that store transaction data on the baselayer, as seen in systems like Danksharding, which optimize load-balancing and data inclusion through a merged fee market.

Tradeoffs: The main chain’s load can limit the number of shards a blockchain can support. Data availability sampling (DAS) is necessary to confirm that data was accessible when the block was created, without downloading all the data. Sharding also introduces communication overhead for transferring storage between nodes during rotations and demands a large node pool to ensure sufficient decentralization across all shards.

Compressed On-Chain Data Storage with Modular Blockchains

Modular blockchains store transaction data or state differences on-chain or off-chain after off-chain computation. Rollups frequently compress transaction data off-chain before storing it on-chain.

Advantages: Compressed on-chain storage offers the highest security for modular blockchains since all full nodes retain the data. It lowers storage costs on the layer-1 blockchain and, when paired with data sharding, provides rollups with a more efficient and scalable on-chain storage solution.

Tradeoffs: On-chain storage is costlier than off-chain options, which may limit modular blockchains’ scalability compared to less decentralized storage methods. Data compression might omit non-essential data for validation, potentially restricting detailed analyses of blockchain activity.

Off-Chain Data Storage in Modular Blockchain Designs

In modular blockchain architectures, off-chain storage methods help reduce on-chain storage demands. These designs include “validiums,” which utilize zero-knowledge proofs published on-chain while keeping data off-chain. Below are the four main approaches to off-chain data storage in modular blockchains:

Centralized Storage

Centralized storage involves keeping data off-chain on a centralized platform.

Advantages: This is the most cost-effective storage method.

Tradeoffs: Centralized storage is vulnerable to data withholding and security risks, such as data manipulation or the platform becoming inaccessible.

Permissioned Data Availability Committees (DACs)

Permissioned DACs manage off-chain data and provide on-chain attestations using a small committee of trusted nodes.

Advantages: They improve on centralized storage by slightly enhancing trust in data availability.

Tradeoffs: Similar to centralized storage, they still face issues related to centralization, albeit with somewhat improved security guarantees.

Permissionless Data Availability Committees (DACs)

Permissionless DACs offer off-chain data storage with on-chain proofs, driven by cryptoeconomic incentives to encourage honest behavior.

Advantages: This method is more secure than other off-chain solutions and cheaper than full on-chain storage.

Tradeoffs: It remains less secure than on-chain storage and hasn’t yet been implemented at scale with sustainable economics.

Volitions

Volitions allow users to choose whether their transaction data is stored on-chain or off-chain, maintaining a shared state root and consensus cost.

Advantages: Volitions are innovative as they offer flexible data availability solutions at the transaction level.

Tradeoffs: This method is more complex and has not yet been achieved in production.

Data Pruning

Data pruning enables blockchain nodes to discard historical data beyond a certain block height, often combined with Proof-of-Stake checkpoints where transactions in older blocks are considered final.

Advantages: Pruning reduces the data storage burden on nodes, as older validated data can be safely discarded. Nodes only need to validate future blocks, enhancing efficiency.

Tradeoffs: Pruning depends on third parties (e.g., exchanges, block explorers) to store historical data permanently. Despite this, only one honest third party needs to maintain the data for full nodes to recreate historical states. While Proof-of-Stake checkpoints reduce the relevance of this reliance, historical data remains crucial for on-chain analytics and block explorers.

Statelessness, State Expiry, and State Rent

These methods aim to limit the amount of state that full nodes need to store, focusing on state expiry, statelessness, or state rent mechanisms.

State Expiry

In state expiry designs, nodes prune state that hasn’t been accessed for a set period. However, expired state can be revived using merkle proofs known as “witnesses.”

Statelessness

Statelessness means full nodes don’t store the state but validate new blocks using witnesses.

  • Weak Statelessness: Only block producers store global state, while other nodes verify blocks without storing state.

State Rent

State rent requires users to pay for maintaining limited state storage. Unpaid state is recycled and made available to new users.

Advantages: These methods reduce the storage burden on individual nodes, addressing state bloat despite an expanding ledger or increasing on-chain transactions. They help maintain practical hardware requirements, ensuring long-term viability for end-user verification.

Tradeoffs: These are relatively novel approaches and shift away from the traditional model where users pay once to store their state on all nodes indefinitely. Transitioning to these models can be challenging, potentially breaking existing applications that assume perpetual state availability. Additionally, new state storage models could increase the cost of certain applications.