Carbon to Diamond: An Incident Remediation Assistant System From Site Reliability Engineers’ Conversations in Hybrid Cloud Operations

Authors

  • Suranjana Samanta IBM Research India
  • Ajay Gupta IBM Research India
  • Prateeti Mohapatra IBM Research India
  • Amar Prakash Azad IBM Research India

Keywords:

Artefact Extraction, Similar Issue Retrieval, Word Embedding, Conversation Representation

Abstract

Conversational channels are changing the landscape of hybrid cloud service management. These channels are becoming important avenues for Site Reliability Engineers (SREs) %Subject Matter Experts (SME) to collaboratively work together to resolve an incident or issue. Identifying segmented conversations and extracting key insights or artefacts from them can help engineers to improve the efficiency of the incident remediation process by using information retrieval mechanisms for similar incidents. However, it has been empirically observed that due to the semi-formal behavior of such conversations (human language) the conversations are very unique in nature and also contain domain-specific terms. %It is important to identify the correct keywords and artefacts like symptoms, issue etc., present in the conversation chats. In this paper, we build a framework that taps into the conversational channels and uses various learning methods to (1) understand and extract key artefacts from conversations like diagnostic steps and resolution actions taken and (2) present an approach to identify past conversations about similar issues. Experimental results on our dataset show the efficacy of the methods used in our proposed system.

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Published

2021-05-18

How to Cite

Samanta, S., Gupta, A., Mohapatra, P., & Azad, A. P. (2021). Carbon to Diamond: An Incident Remediation Assistant System From Site Reliability Engineers’ Conversations in Hybrid Cloud Operations. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15401-15407. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17809

Issue

Section

IAAI Technical Track on Emerging Applications of AI