In this blog, we look at existing approaches to evaluating blockchain performance from the standpoint of empirical analysis. Different solutions, such as benchmarking, monitoring measurements, self-designed experiments, and simulation, are specifically reviewed and compared. These approaches are typically used in conjunction to provide additional evidence for blockchain performance management.
Blockbench is the first benchmark framework for assessing private blockchain performance metrics, such as throughput, latency, scalability, and fault tolerance. For the time being, it supports measurement on four major private blockchain platforms: Ethereum, Parity, HLF, and Quorum.
It does, however, claim to support the evaluation of any private blockchain by extending the workload and blockchain adaptors accordingly.
Blockbench’s design identifies four abstraction layers in the blockchain: consensus, data model, execution engine, and application, from the bottom (low level) to the top (high level).
The consensus layer establishes the rule of agreement and gets agreement from all network participants on the block content so that it can add to the blockchain. The data model specifies the data structure, content, and operations that can perform on blockchain data. The execution engine includes runtime environment resources such as the EVM and Docker, which aid in executing blockchain codes. The application layer contains all blockchain applications, including smart contracts and DApps.
As input, blockchain benchmarking typically requires a standardized environment and a well-documented workload. However, for public blockchain systems, it is impossible to have excellent control over the actual workload and consensus participants, making benchmarking more difficult. There are two potential solutions for evaluating public blockchains.
The first solution is to create a private version of the associated test network and use the previously mentioned benchmarks to evaluate blockchain under artificially designed workloads. This may cause the creation of new adapters for either the workload or the blockchain network. Because the tested private version of blockchain may encounter scaling issues when implemented publicly, this approach should consider blockchain scalability. As a result, compared to the real public network, the tested result may show higher values of performance metrics.
The second solution is to monitor and evaluate the performance of the live public system under realistic workload conditions. Zheng et al. proposed a detailed, real-time performance monitoring framework based on logs. Compared to its counterpart solution via a remote procedure call, it has less overhead, more details, and better scalability (RPC).
This section examines DLT performance evaluation through empirical analysis based on self-designed experiments. Even though empirical analysis cannot provide standardized test results like benchmarking, it is very flexible in parameterization. It can use to identify potential bottlenecks and pave the way for additional performance gains.
Experiment-based approaches to evaluating distributed ledger systems such as Hyperledger, Ethereum, and DAG-based ledgers have been widely used. The performance of various private blockchain platforms and different versions of a specific blockchain can be compared by running tests in a well-controlled test environment. Furthermore, some studies examined the detailed performance of varying encryption and hash algorithms from the data layer in the blockchain abstraction model.
All evaluation solutions mentioned above (i.e., benchmarking, monitoring, and experimental analysis) require the systems to be available, whether private or public blockchains. However, the system under consideration is not always accessible.
For example, if a company needs to choose between two blockchain platforms in development based on their performance, none of the previously discussed solutions are workable. Building a real blockchain network for testing is typically time and resource intensive. This leads us to investigate another method of evaluation, namely simulation.
A blockchain simulator can simulate network node behavior in reaching consensus, providing performance comparable to a proper system. A blockchain simulator typically allows users to tune the system parameters to run different settings for comparison. This section will look at the role of simulation in the blockchain world.
The blockchain performance management cycle is essential for all employees in an organization because it is associated with the recognition, salary increases, and the next steps for growth. It is a significant activity in which senior leaders, line managers, and Human Resource teamwork ensure that people are recognized and rewarded for ethical contributions to the organization’s goals and values. SmartOSC offers full-service blockchain development solutions, contact us if you need them.
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