Challenges
To manage today’s cloud-centric, distributed, and multi-domain application-delivery infrastructures, enterprises resort to using a collection of point products, each providing a piece of the total picture of observability of “client-to-cloud”. Further more, the data provided by these tools are fragmented, and need to be pieced together and correlated using manual methods or home-brewed software that each enterprise has to build under IT operations workflow, in order to create a holistic view of end-to-end digital performance.
The more the data, the more the dimensions for looking at a problem to performing faster Root-Cause Analysis (RCA). Every transaction between the client and the application server through every network/infrastructure node in the application-delivery path needs to be collaborated with comprehensive datasets for effective RCA, client-network-cloud performance management, and subsequent remediations.
Seeing some aberrations and raising a flag and/or alert is not sufficient anymore. The expectation is that steps need to be taken to generate actionable insights on digital performance in the infrastructure and cloud performance management. Insight- and analytics-driven chain of actions to automate remediation is the only way for faster MTTR and reducing loss of business revenues from infrastructure, services or network downtime.
In the world of AI/ML, data is key, and the more the data the more the accuracy of the models. AIOps, in particular, has been a challenge for “domain-agnostic” generic solutions that do not collect their primary data, and as such, they are only as good as the data they are provided. Effective AIOps solutions should be able to collect and continuously analyze real-time performance data from various sources, cross-platform solutions, and domains/layers as a first step in the automated IT operations workflow and remediations process.
Solution
Too many alerts/events generated by a plethora of management tools create information overload and lead to triage dysfunction. The AIOps journey should start with extensive and comprehensive data collection (from cross-platform solutions) under the same platform to reduce observability gap and faster RCA, with the added side benefit of noise reduction.
Ennetix xVisor provides various “Primary” data collection methods from application/network/infrastructure nodes that include (but are not limited to) Flows, Wire data, Probes, Metrics, Syslog, API Logs, Traces, Transactions, Device data, SNMP, Configs, Meta data, etc. in a programmatic manner. For reporting and remediation integrations, xVisor includes API-driven collaborations with other SIEM, NOC, SOC, NAC, FW/SOAR, ITIL, ITSM, CMDB, etc. platforms.
xVisor also allows placement of appropriate steps for having human oversight and validation and for recording of workflows for compliance and audits. xVisor can differentiate between “notify only” vs. automated actions, based on intent-based policies set by administrators.
Conclusion
Self- healing in dynamic application-delivery infrastructures is becoming a reality, and how far Ennetix customers want to take it now is truly in their control! Our role is to be ready when they are turning on the “switch” to collaborate across cross-platform solutions.
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