Endorser Node Selection via Transaction State and Load Detection
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Solution Overview
Problem
Current blockchain systems face inefficiencies in identifying optimal endorser nodes for transaction endorsement, leading to wasted computing and storage costs due to nodes being at different relative states and varying processing loads, which can result in transaction failures and bottlenecks.
Innovation Solution
A system that determines the current transaction state and load of endorser nodes based on transaction history and performance data, providing a ranked list of optimal nodes for clients to improve endorsement collection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If nodes are selected for endorsement without considering their current state and load, then the endorsement collection process is simple, but transaction failures increase and computing costs are wasted
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing the current state and load information of endorser nodes in the data block before transaction endorsement is needed. This allows the client to directly access this pre-computed information to select optimal endorsers without performing complex real-time analysis, thus improving reliability while controlling complexity.
Solution Approach 2:
The data block acts as an intermediary that stores and transmits endorser node state and load information between the blockchain and the client. This intermediary structure enables the client to access comprehensive node information without directly querying each node, simplifying the selection process while ensuring reliable endorsement.
2Reliability
If all endorser nodes are contacted for endorsement, then comprehensive coverage is achieved, but computing costs and overhead increase
Solution Approach 1:
The system applies local quality by evaluating and selecting endorser nodes based on their individual current state and load characteristics. Instead of treating all nodes uniformly, the system identifies and contacts only those nodes with favorable states (lower load, appropriate capacity) for endorsement, thereby reducing computing costs while maintaining adequate coverage through localized optimization.
Solution Approach 2:
The system changes the selection parameters from static node identification to dynamic state-based selection. By using current state and load information stored in the data block, the system adjusts which nodes are contacted for endorsement based on real-time conditions, reducing unnecessary computing overhead while ensuring comprehensive endorsement where needed.
3Adaptability or versatility
If nodes at different relative states are used for endorsement, then network diversity is improved, but transaction failure rate increases due to node lag
Solution Approach 1:
The system implements feedback mechanisms by incorporating current state and load information of endorser nodes into the data block, which is then accessible to clients for selection. This feedback loop ensures that nodes are selected based on their actual current state rather than assumed state, preventing transactions from failing due to node lag while maintaining diversity in the endorsement set.
Data Source
AI summary
An example operation may include one or more of receiving a data block from a blockchain node, determining a current transaction state of each of a plurality of endorser nodes based on transaction history stored within read and write sets of the data block, determining a current load of each of the plurality of endorser nodes based on performance data stored within the data block, and outputting information about the current transaction state and the current load of each of the plurality of endorser nodes via a user interface.