Abstract:
Micro-service architecture is a promising paradigm to develop, deploy and maintain applications using independent and autonomous cloud services. Nowadays, increasingly ap...Show MoreMetadata
Abstract:
Micro-service architecture is a promising paradigm to develop, deploy and maintain applications using independent and autonomous cloud services. Nowadays, increasingly applications are embracing this model. However, it is difficult and time-consuming to diagnose and identify the actual root cause when anomalies occurs in micro-service architecture due to various factors. This paper introduces a novel framework for anomaly investigation and root cause identification in micro-service architecture. The novelty in our work lies on: (1) Different from existing solutions, in our framework, we propose a frequent pattern mining algorithm on anomaly correlation graph, named FacGraph, to discover root cause services. (2) We leverage breadth first ordered string (BFOS) to reduce the time-consumption of the frequent graph mining (FSM) (3) We further develop a distributed version of FacGraph to improve its paralleled computing efficiency. We evaluate our framework in real production environment IBM Bluemix. Result demonstrate that FacGraph outperforms other methods in diagnosis accuracy and offers a fast identification of root cause service when an anomaly occurs.
Published in: 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC)
Date of Conference: 17-19 November 2018
Date Added to IEEE Xplore: 13 May 2019
ISBN Information: