Outlier Node Detection in Cluster Computing
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Solution Overview
Problem
Existing cluster computing systems face performance issues due to degraded nodes, which are often identified too late by legacy diagnostic tools relying on absolute metrics, leading to suboptimal corrective actions and increased resource demands.
Innovation Solution
The implementation of techniques that use relative metrics to detect and remediate degraded nodes in clusters, focusing on service-level interactions and statistical outlier identification to reduce resource usage and improve performance by addressing node degradation proactively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If legacy diagnostic tools use a priori known parameters (CPU load, memory utilization, network I/O) to identify degraded nodes, then the detection process is simple and fast, but the detection accuracy is insufficient and nodes are identified too late
Solution Approach 1:
The patent transforms the detection approach from using static a priori parameters to using dynamic relative metrics that capture service-level interactions between nodes. Instead of monitoring absolute values like CPU load or memory utilization, the system measures relative performance deviations by comparing actual service completion times against expected times derived from historical data and peer node performance. This parameter transformation enables early detection of degraded nodes before they cause cluster-wide performance issues.
Solution Approach 2:
The patent replaces traditional mechanical monitoring approaches (direct measurement of hardware parameters) with a statistical field-based approach. Instead of physically monitoring each node's internal state, the system uses service-level observations and statistical models to infer node health. The detection mechanism substitutes direct hardware monitoring with indirect statistical inference based on service completion patterns and relative performance metrics.
2Power
If the number of nodes in the cluster increases to improve computing power, then the overall processing capability increases, but the likelihood of having at least one slow-performing node increases
Solution Approach 1:
The patent implements continuous feedback mechanisms where each node's service-level performance is monitored and compared against expected performance derived from historical data and peer nodes. The system collects service completion time observations, calculates relative performance metrics, and uses this feedback to identify nodes that are deviating from normal performance patterns. This feedback loop enables the cluster to adapt to performance variations and maintain reliability even as the number of nodes increases.
Solution Approach 2:
The patent segments the cluster monitoring function into distributed components, where each node independently monitors its own service-level performance and contributes observations to the collective detection process. Instead of a centralized monitoring system that would become a bottleneck, the segmentation approach allows parallel monitoring across all nodes, enabling the system to scale with cluster size while maintaining detection effectiveness.
3Productivity
If one slow-performing node is present in a parallelized computing group, then the entire pipeline runs only as fast as the slowest node, but identifying and removing the slow node increases detection complexity
Solution Approach 1:
The patent changes the detection parameters from absolute performance thresholds to relative performance deviations. Instead of asking whether a node exceeds a fixed speed threshold, the system measures how much slower a node is compared to its peers and historical performance. This relative measurement approach naturally highlights outlier nodes in parallel computing groups without requiring complex analysis of inter-node dependencies or pipeline dynamics.
Data Source
AI summary
Systems for cluster computing. A method for detection and remediation of degraded nodes in a cluster commences upon measuring operational aspects of the nodes in the cluster, then determining, based on the measurements and other factors, a suspect set of nodes comprising one or more suspect nodes from the nodes in the cluster that have measurements that are determined to be outliers with respect to remaining nodes that are determined not to be the outliers. A density-based spatial clustering analysis is performed over the suspect set and remediation actions are initiated when results of the density-based spatial clustering analysis identifies a suspect node as being a degraded node.