Hyperledger Blockchain Throughput Optimization via Dynamic Block Size and vCPU Allocation
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Solution Overview
Problem
Blockchain networks face challenges in maintaining target latency and throughput due to limitations in block size, vCPU allocation, channel management, and endorsement policies, which can lead to increased latency and reduced reliability.
Innovation Solution
A method that adjusts block size, number of vCPUs, and endorsement policy based on measured latency and throughput requirements, using chain code to automatically optimize these parameters and maintain desired network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the block size is increased to improve throughput, then the network can process more transactions per block, but the latency increases as it takes longer to fill and validate larger blocks
Solution Approach 1:
The patent implements dynamic adjustment of block size based on real-time network conditions. The system monitors transaction volume and network load, then automatically adjusts the block size parameter to optimize the balance between throughput and latency. This dynamic adaptation allows the network to scale block size during high-demand periods while maintaining smaller blocks during low-demand periods, resolving the static trade-off between these two parameters.
Solution Approach 2:
The system changes the block size parameter dynamically based on measured network performance metrics. By monitoring transaction arrival rates, block validation times, and network load, the system adjusts the block size parameter in real-time to achieve target latency and throughput values, transforming the fixed parameter into a variable that adapts to changing network conditions.
2Productivity
If the number of vCPUs is increased to improve processing capacity, then more transactions can be validated simultaneously, but the device complexity and resource requirements increase
Solution Approach 1:
The patent implements dynamic allocation of vCPUs based on real-time network load and transaction volume. The system monitors the number of pending transactions, block validation queue depth, and current CPU utilization, then automatically adjusts the number of active vCPUs. This allows the network to scale processing capacity during high-load periods while reducing resource consumption during low-demand periods, resolving the trade-off between processing capacity and resource requirements.
3Reliability
If the endorsement policy is made more strict to improve security, then fewer unauthorized transactions are approved, but the latency increases due to additional verification steps
Solution Approach 1:
The patent implements a tiered endorsement policy where transactions are subjected to different levels of verification based on their risk profile and transaction type. High-value or suspicious transactions receive full endorsement verification from multiple peers, while low-value routine transactions receive expedited processing with reduced verification requirements. This partial application of strict endorsement policies maintains security for critical transactions while reducing latency for routine operations.
Solution Approach 2:
The system applies different endorsement policy strictness levels to different transaction types, channels, or organizational contexts. Critical financial transactions undergo rigorous multi-peer endorsement verification, while routine transactions such as token transfers or standard smart contract executions receive streamlined verification. This localized quality approach ensures high security where needed while maintaining overall network throughput and latency performance.
4Productivity
If the number of channels is increased to improve network scalability, then more organizations can participate simultaneously, but the device complexity and management overhead increase
Solution Approach 1:
The patent implements automated channel segmentation and management where the network is divided into multiple isolated channels, each serving specific organizations or transaction types. The system automatically creates, configures, and manages these channels based on organizational requirements, reducing the management burden on individual participants. Each channel operates independently with its own consensus and endorsement policies, allowing scalable growth without increasing overall network complexity for individual nodes.
Solution Approach 2:
The system implements self-service channel provisioning where organizations can automatically create and configure their own channels based on predefined templates and policies. The network infrastructure automatically allocates resources, configures peer connections, and sets up endorsement policies without requiring manual intervention from network administrators. This self-service approach enables scalable channel creation while minimizing management overhead for both individual organizations and the overall network.
Data Source
AI summary
In a hyper ledger-based blockchain network system, in order to adjust latency and throughput required by a specific hyper ledger-based network, by using a block size, an endorsement policy, the number of channels, and the number of vCPUs allocation, the latency and the throughput desired by a user are maintained.