Zenoss, a provider of software-defined intelligent application and service monitoring, has launched its multi-cloud serverless monitoring solution. The new solution would unify the monitoring of serverless functions across the ‘Big Three’ public cloud providers – Google Cloud Platform (GCP), Amazon Web Services (AWS Cloud) and Microsoft Azure.
Quite some organizations would struggle to rationalize serverless function performance data, according to Zenoss. This would be exacerbated in multi-cloud environments. With a more precise understanding of serverless function performance, organizations are able to rapidly tune their serverless environments, stated Zenoss.
The new monitoring solution, Zenoss multi-cloud serverless monitoring, was architected to address this specific challenge. It would provide “easy” tracking of serverless function performance and behavior across multi-cloud environments. It would include “robust” analytics, including metrics such as top serverless functions by invocation count, a stack ranking of serverless functions with longest runtime durations, and serverless functions with the highest error rates. It would also include visualizations enabling customers to “easily” pinpoint serverless functions that are impacting business services, regardless of cloud provider.
Informed Business Decisions
“For modern IT organizations leveraging public cloud platforms, it is no longer just about achieving the highest performance possible – it’s about optimizing performance,” said Ani Gujrathi, Chief Technical Officer (CTO) at Zenoss. “The Zenoss multi-cloud monitoring innovations are continuing to deliver the most precise insights across all major cloud platforms and empowering our customers to make more informed business decisions.”
Zenoss multi-cloud serverless monitoring is available now to both new and existing customers as part of the standard Zenoss offering, at no additional charge.
Zenoss Cloud is a SaaS-based intelligent application and service monitoring platform that streams and normalizes all machine data. This would enable the emergence of context for preventing service disruptions in complex multi-cloud environments. Zenoss collects all types of machine data, including metrics, dependency data, events, streaming data and logs – to build real-time IT service models that train machine learning algorithms to deliver “robust” AIOps analytics capabilities.