Dynamic Peer-Based Node Health Monitoring in Distributed Systems
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Solution Overview
Problem
Existing methods for determining node health in distributed computing systems rely on static threshold limits, which may not accurately reflect a node's performance, as a node can be within limits but underperform compared to peers.
Innovation Solution
The system compares health parameters of each node to those of its peers, using fault monitors and coordinators to assess relative performance and initiate remedial actions based on configuration value adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static threshold limits are used to determine node health, then the monitoring system is simple to implement, but it cannot accurately reflect node performance under varying runtime conditions
Solution Approach 1:
The patent transitions from static threshold limits to dynamic peer-based health parameter comparison. Instead of using fixed thresholds, the system continuously compares each node's health parameters against its peers' parameters, allowing the health assessment criteria to adapt dynamically to runtime conditions and peer performance variations.
Solution Approach 2:
The system implements feedback by having nodes continuously report their health parameters to the fault monitor, which then compares these parameters against peer nodes and provides feedback about relative performance. This feedback loop enables the system to identify underperforming nodes and trigger remedial actions based on comparative analysis rather than static thresholds.
2Reliability
If peer comparison is used to assess node health, then node health assessment accuracy is improved, but the monitoring system complexity increases
Solution Approach 1:
The fault monitor component performs multiple functions: collecting health parameters from all nodes, comparing parameters against peers, determining relative health status, and initiating remedial actions. This multi-functionality consolidates complexity into a single component rather than distributing it across multiple specialized systems.
Solution Approach 2:
The patent introduces a fault monitor as an intermediary component that mediates between individual nodes and the overall system health assessment. Rather than nodes directly comparing themselves or requiring complex distributed consensus, the fault monitor serves as a central coordinator that simplifies the peer comparison process while improving reliability.
3Productivity
If continuous monitoring and remedial action are implemented, then cluster operation optimality is maintained, but the system requires more resources and complexity
Solution Approach 1:
The system implements self-service through automated remedial actions. When the fault monitor identifies an underperforming node, it automatically adjusts configuration parameters or triggers recovery procedures without requiring manual intervention. This self-service capability maintains cluster efficiency while minimizing the operational complexity burden.
Solution Approach 2:
The system performs preliminary actions by continuously monitoring health parameters and comparing them against peers before significant performance degradation occurs. The fault monitor proactively identifies potential issues and initiates remedial actions in advance, preventing complete node failure and maintaining cluster productivity without requiring complex reactive recovery systems.
Data Source
AI summary
Techniques are described for identifying unhealthy nodes in a multi-node system. One or more parameters of each node is monitored, then compared with the values for the same parameter running on other nodes in the multi-node system. Based on the comparison, a determination is made whether a node is healthy. If the multi-node system comprises one or more nodes with differing capabilities, an adjustment is performed to account for the differing capabilities of each respective node. Further provided are methods of taking remedial action upon a determination that a node is unhealthy. A tuner is used to modify values of health parameters until the node is performing similarly to its peers.