Distributed Ledger Health Monitoring for Consensus-Based Service Triage
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Solution Overview
Problem
Existing systems struggle to efficiently and automatically identify and prioritize the remediation of application services experiencing reduced functionality in a microservices architecture, particularly in large computing environments like datacenters, without manual intervention.
Innovation Solution
A distributed ledger technology-based system is used to verify the health status of application services through a consensus mechanism among interconnected nodes, recording health indicators and criticality metadata in a distributed ledger, and automatically generating support tickets based on the consensus threshold, enabling automated prioritization and broadcasting of remediation efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data collection and analysis is used to identify impaired application services, then system complexity is reduced, but productivity and response time deteriorate
Solution Approach 1:
The system enables self-service by allowing application services to self-report their health status and having the distributed ledger system automatically verify and process these reports. The consensus mechanism and automated support ticket generation eliminate the need for manual data collection and analysis, significantly improving productivity while the automation handles the complexity internally
Solution Approach 2:
The distributed ledger acts as an intermediary between application services and the support ticket system. It receives health indicator reports from services, verifies them through consensus among nodes, and automatically generates support tickets when issues are confirmed. This intermediary layer automates the identification process while managing system complexity through standardized protocols
2Measurement precision
If distributed ledger with consensus mechanism is implemented, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The consensus mechanism implements feedback by having multiple nodes verify health indicator reports before accepting them as valid. Each node independently evaluates the reported status and provides feedback through voting. Only when the consensus threshold is met is the health status change recorded, ensuring high measurement precision through collective verification
Solution Approach 2:
The system segments the verification process into independent nodes that each perform the same validation function. Instead of a single complex verification system, multiple simpler nodes work in parallel, each contributing to the consensus. This segmentation improves reliability through distributed verification while making the overall system more manageable
3Productivity
If automated support ticket generation is implemented, then productivity improves, but device complexity increases
Solution Approach 1:
The system performs preliminary action by pre-defining consensus thresholds and support ticket generation rules in the distributed ledger. When health indicators are reported, the system automatically compares them against pre-set criteria and generates support tickets without requiring complex real-time decision logic. This preliminary configuration enables efficient automation while reducing runtime complexity
Data Source
AI summary
This disclosure describes techniques for application health monitoring using distributed ledger technology in a computing system that includes a plurality of nodes providing application services. For example, the techniques include obtaining health indicators of a particular application service by a computing system. The computing system causes a consensus system that includes a particular node executing the particular application service to vote and verify the status of the node. Based on the verification of the status of the particular node, the consensus system writes an entry to a distributed ledger regarding the status of the particular node. The computing system reads the entry of the distributed ledger and generates a ticket based on the entry. The computing system adds the ticket to a network queue for broadcasting within the computing system.