Hybrid Infrastructure Monitoring

Digital transformation is continuously increasing the dependence on cloud-based services and architectures for an enterprise’s business-critical application delivery. The underlying infrastructure is becoming increasingly virtualized, distributed, heterogeneous, and multi-domain (where traffic crosses domains with different ownerships). Data and resources required for applications and service delivery are distributed across multiple sites (e.g., clouds, data centers, etc.) managed by multiple entities. 

Users, applications, and data are widely dispersed, and most of the time, they are not in direct control of the enterprises. IT operations teams are juggling with multiple domains/layers from LAN, WAN, Peering Connects, Internet Exchange points (IXC), Public/Private Cloud, SaaS, On- and Off-prem, Third-Party Partners, etc. to every device – physical (laptop, server, router, switch) to virtual (VMs, containers, etc.). Hybrid networking and infrastructures are widely deployed and well understood, but are they being efficiently managed? 

Ensuring that the end-to-end hybrid infrastructure delivers the performance demanded by users and applications, at present and in the future, is an on-going challenge for IT operations teams. They are exploring and utilizing various monitoring solutions such as hybrid cloud monitoring, multi-cloud monitoring, etc., and emphasizing the need for using ML and  AI  infrastructure management.

Solution

With no direct control of the infrastructure where the application or the data resides, IT teams struggle with their visibility stopping after a certain point due to lack of visibility beyond that point or simply because of not having control beyond that point. In addition, network paths taken by application flows are numerous and are sometimes chosen automatically, leading to performance and cost implications. The dynamic nature of application-hosting infrastructures in the cloud, with resources (e.g., containers, pods, etc.) being dynamically dimensioned (and spun up or down), creates a network topology mapping, possibly with a high level of dynamism. Continuous visibility in such dynamic and hybrid infrastructures is only possible with automation in the observability process through the innovative use of  Machine Learning (ML) and Artificial Intelligence (AI) for infrastructure management.

Ennetix xVisor does not require a predefined network or application topology map to get started and neither does it need manual address ranges to start the discovery process of hybrid infrastructures of application delivery. At the root of our discovery architecture is “Application Semantics,” i.e., when users are interacting with an application, how does the entire interaction play out? Continuous monitoring of user-app interactions forms the basis to build the topology map in xVisor, and then the data-collection process through flows, pcaps, probes, etc. starts!

Conclusion

xVisor is truly automated for hybrid infrastructure monitoring (I.e., hybrid cloud monitoring, multi-cloud monitoring, etc.) from day zero, using  ML and AI for infrastructure management, with dashboards that populate in a matter of minutes. No hassle of setting manual thresholds!

Learn More:- https://ennetix.com