Al capabilities are riding a big wave in the business cloud computing environment which enables organizations to be more cost-efficient and better automation. The wider enterprise cloud adoption and the emergence of AI, ML technologies are allowing companies to use intelligent software automation rather than simple script-based IT automation.
With cloud adoption, several new operational dimensions have emerged, including variable usage costs, API-driven infrastructure provisioning, no centralized security, and dynamic resources. Along with the agility and flexibility, the cloud operations bring some challenges, including security compliance issues, alert fatigues, and analytics-driven monitoring requirements. Adopting AI-based cloud operations (AIOps for CloudOps) enables cloud-based IT teams to address their cloud management challenges.
Traditional IT operations during the cloud era is a complex task and not effective in terms of achieving the service level objectives (SLO). The solution to this problem is an AI-based CloudOps, which helps IT teams to have better analytics, event reduction, and faster decision-making capabilities. The observe part of the CloudOp builds on top of the log analytics and KPI analytics. Then it uses the multivariate KPI anomaly detection and cross-domain log anomaly detection to derive more meaningful events. Based on this, the contextual root cause analysis and automated incident recognition can log more meaningful tickets.
The engage is an AIOps function that continuously enables the IT support team to have a better IT user engagement and IT service management process. The AI-based CloudOps enables an automated way to resolve the anomalies and root cause events. The AI for CloudOps helps the organization with L1 Virtual agents automate the L1 helpdesk function (shift further on L1 activities), automated ticket creation from multiple sources like email, tools, voice interaction, and engagement. Apart from this, it enhances the core IT service processes like ticket lifecycle management, knowledge management with knowledge graph, and AI-driven SLA management. The AI for CloudOps also provides more in-depth insights, including IT Service Analytics, SLA Analytics, Cloud Service Agent performance Analytics, Change Risk analytics.
The act is the automation phase. The AI for CloudOps helps in automating the request fulfilment, incident remediation, cloud service provisioning. The cloud-native application release automation, cloud infrastructure as a code deployment, and patch installation automation are possible with the AI-based CloudOps tools. By the implementation of AIOps based CloudOps into the organization, IT operators can focus on their core tasks, which saves up operational cost and time.
To continuously manage the working of the entire business organization is strenuous. CloudOps governance models are the perfect key for the solution. The governance aspect of CloudOps provides a single pane of glass for the entire cloud operations through a seamless end to end visibility along with correlated business KPIs. This functionality helps the CIOs in analysing and managing cloud cost, usage, security, and governance in one place and detecting potential vulnerabilities that could put your cloud environment at risk. It also controls the usage and creates visibility across all used services to achieve maximum cost-effectiveness. In the end, proper governance helps the IT organization to focus more on its business value.