Graph Based Root Cause Analysis in Cloud Data Center

2020 ◽  
Author(s):  
Divyaansh Dandona ◽  
Mevlut Demir ◽  
John J. Prevost
Keyword(s):  
Data Center ◽  
Root Cause Analysis ◽  
Cloud Data Center ◽  
Cause Analysis ◽  
Cloud Data ◽  
Root Cause

scholarly journals Telemetry Based Anomaly Detection and Correlation in Data Center

2020 ◽  
Vol 9 (1) ◽  
pp. 2146-2148
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Anomaly Detection ◽  
Monitoring System ◽  
Data Center ◽  
Root Cause Analysis ◽  
High Accuracy ◽  
Cloud Platform ◽  
Cause Analysis ◽  
Root Cause

Data center is a complex amalgamation of servers where there are thousands of services, storage, networking, routers, switches and softwares providing services 24x7 to customers. Services provided can range from websites, storage, cloud platform, Email marketing etc. A team is established to detach the anomaly generated from the monitoring system. Anomaly or issues in servers cause high downtime of service. Detecting these anomalies with high accuracy and performing Root cause analysis has been a major issue. The team often remediates the symptom than the anomaly. With the use of Artificial Neural networks a trained model can provide solutions with high accuracy and scalablility which result in higher uptime and reduced MTTR for customers.

scholarly journals Incident Management for Explainable and Automated Root Cause Analysis in Cloud Data Centers    

2021 ◽  
Vol 27 (11) ◽  
pp. 1152-1173
Author(s):  
Arnak Poghosyan ◽  
Ashot Harutyunyan ◽  
Naira Grigoryan ◽  
Nicholas Kushmerick
Keyword(s):  
Rule Learning ◽  
Root Cause Analysis ◽  
Rule Induction ◽  
Incident Management ◽  
Data Sets ◽  
Classification Problems ◽  
Cause Analysis ◽  
Cloud Data ◽  
Root Cause ◽  
High Level

Effective root cause analysis (RCA) of performance issues in modern cloud environ- ments remains a hard problem. Traditional RCA tracks complex issues by their signatures known as problem incidents. Common approaches to incident discovery rely mainly on expertise of users who define environment-specific set of alerts and >target detection of problems through their occurrence in the monitoring system. Adequately modeling of all possible problem patterns for nowadays extremely sophisticated data center applications is a very complex task. It may result in alert/event storms including large numbers of non-indicative precautions. Thus, the crucial task for the incident-based RCA is reduction of redundant recommendations by prioritizing those events subject to importance/impact criteria or by deriving their meaningful groupings into separable situations. In this paper, we consider automation of incident discovery based on rule induction algorithms that retrieve conditions directly from monitoring datasets without consuming the sys- tem events. Rule-learning algorithms are very flexible and powerful for many regression and classification problems, with high-level explainability. Since annotated or labeled data sets are mostly unavailable in this area of technology, we discuss data self-labelling principles which allow transforming originally unsupervised learning tasks into classification problems with further application of rule induction methods to incident detection.

Root Cause analysis of SG tube ODSCC indications within the tube sheets of NPP biblis unit A

2011 ◽  
pp. 78-86
Author(s):  
R. Kilian ◽  
J. Beck ◽  
H. Lang ◽  
V. Schneider ◽  
T. Schönherr ◽  
...  
Keyword(s):  
Root Cause Analysis ◽  
Cause Analysis ◽  
Root Cause

Rule Construction and its Execution for Root Cause Analysis

2012 ◽  
Vol 132 (10) ◽  
pp. 1689-1697
Author(s):  
Yutaka Kudo ◽  
Tomohiro Morimura ◽  
Kiminori Sugauchi ◽  
Tetsuya Masuishi ◽  
Norihisa Komoda
Keyword(s):  
Root Cause Analysis ◽  
Cause Analysis ◽  
Root Cause

Preference Based Resource Allocation In Cloud Data Center

2018 ◽  
Vol 6 (2) ◽  
pp. 287-292
Author(s):  
M.R. Dave ◽  
◽  
H.B. Patel ◽  
B. Shrimali ◽  
◽  
...  
Keyword(s):  
Resource Allocation ◽  
Data Center ◽  
Cloud Data Center ◽  
Cloud Data

Root Cause Analysis Techniques Using Picosecond Time Resolved LADA

2014 ◽  
Author(s):  
Dan Bodoh ◽  
Kent Erington ◽  
Kris Dickson ◽  
George Lange ◽  
Carey Wu ◽  
...  
Keyword(s):  
Integrated Circuits ◽  
Picosecond Laser ◽  
Root Cause Analysis ◽  
Fault Isolation ◽  
Time Resolved ◽  
Cause Analysis ◽  
Root Cause ◽  
Design And Manufacturing ◽  
Multiple Interactions ◽  
Established Technique

Abstract Laser-assisted device alteration (LADA) is an established technique used to identify critical speed paths in integrated circuits. LADA can reveal the physical location of a speed path, but not the timing of the speed path. This paper describes the root cause analysis benefits of 1064nm time resolved LADA (TR-LADA) with a picosecond laser. It shows several examples of how picosecond TR-LADA has complemented the existing fault isolation toolset and has allowed for quicker resolution of design and manufacturing issues. The paper explains how TR-LADA increases the LADA localization resolution by eliminating the well interaction, provides the timing of the event detected by LADA, indicates the propagation direction of the critical signals detected by LADA, allows the analyst to infer the logic values of the critical signals, and separates multiple interactions occurring at the same site for better understanding of the critical signals.

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