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Conversational channels are changing the landscape of hybrid cloud servicemanagement. These channels are becoming important avenues for Site ReliabilityEngineers (SREs) %Subject Matter Experts (SME) to collaboratively work togetherto resolve an incident or issue. Identifying segmented conversations andextracting key insights or artefacts from them can help engineers to improvethe efficiency of the incident remediation process by using informationretrieval mechanisms for similar incidents. However, it has been empiricallyobserved that due to the semi-formal behavior of such conversations (humanlanguage) they are very unique in nature and also contain lot ofdomain-specific terms. This makes it difficult to use the standard naturallanguage processing frameworks directly, which are popularly used in standardNLP tasks. %It is important to identify the correct keywords and artefacts likesymptoms, issue etc., present in the conversation chats. In this paper, webuild a framework that taps into the conversational channels and uses variouslearning methods to (a) understand and extract key artefacts from conversationslike diagnostic steps and resolution actions taken, and (b) present an approachto identify past conversations about similar issues. Experimental results onour dataset show the efficacy of our proposed method.

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