Root Cause Analysis on Analog Circuit Using TR-LADA

Lua, Winson; Phoa, Angeline; Ranganathan, Gopinath; Girish, A S; Krishnan, Ravikumar Venkat

doi:10.1109/ipfa47161.2019.8984900

2019 IEEE 26th International Symposium on Physical and Failure Analysis of Integrated Circuits (IPFA) 2019

DOI: 10.1109/ipfa47161.2019.8984900

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Root Cause Analysis on Analog Circuit Using TR-LADA

Winson Lua¹,

Angeline Phoa²,

Gopinath Ranganathan³

et al.

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2022

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Root Cause Analysis of Anomalies Based on Graph Convolutional Neural Network

Li¹,

Tu²,

Ma³

2022

Int. J. Soft. Eng. Knowl. Eng.

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With the gradual increase of network complexity and network scale in the cloud environment, Root Cause Analysis (RCA) of node failures has become a systematic problem of great research significance. This paper proposes Graph-Attention-Sage (GASage) algorithm, which is a fault RCA algorithm and scheme. The algorithm solves the RCA by incorporating TOPK sampling and Attention-Aggregation with GraphSage algorithm in large-scale and complex microservice network environment. The GASage algorithm is based on graph convolutional neural network and graph attention mechanism, which profoundly combines the characteristics of network fault RCA problems. TOPK sampling mechanism is applied in GASage to select the neighboring nodes with the top [Formula: see text] highest correlation as the objectives to be aggregated. GASage adopts an attention mechanism when aggregated, which aggregates the features of the central node according to the weights of the adjacent nodes and the central node. The weight aggregation method can greatly improve the node representation effect in the RCA scenario. The empirical results of our experiments have demonstrated that the model learned with GASage can outperform other model with the same learning framework and achieves more than 10% improvement in precision.

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Root Cause Analysis of Anomalies Based on Graph Convolutional Neural Network

Li¹,

Tu²,

Ma³

2022

Int. J. Soft. Eng. Knowl. Eng.

View full text Add to dashboard Cite

show abstract

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Root Cause Analysis on Analog Circuit Using TR-LADA

Cited by 1 publication

References 2 publications

Root Cause Analysis of Anomalies Based on Graph Convolutional Neural Network

Root Cause Analysis of Anomalies Based on Graph Convolutional Neural Network

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