VMware has updated its Wavefront monitoring service to include support for distributed tracing along with anomaly detection capabilities enabled by machine learning algorithms.
Stela Udovicic, senior director for product marketing for Wavefront by VMware, said the addition of the distributed tracing support, now available as part of a public beta, means that a single monitoring service now can provide metrics, histograms and the tracing function required to provide a holistic view of the IT environment. Those capabilities eliminate the need to employ multiple services to achieve the same goal, she noted.
In contrast, Wavefront makes it easier to search for traces containing a specific service or application programming interface (API) to determine which ones have errors or long response time. Additionally, DevOps teams can filter the trace search to show traces containing spans from a given cluster or shard.
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At the same time, VMware is adding an AI Genie capability that promises to make it easier to troubleshoot existing needs and identify future application and IT infrastructure requirements, said Udovicic. That data is collected in real time and presented in a way that allows even the most novice users to visualize using analytics tools built into the Wavefront services, added Udovicic.
Wavefront is based on an agentless architecture that in part makes it possible for the service to process millions of metrics, histograms, and traces per second. That capability is especially critical in IT environments that have embraced microservices based on containers that tend to expand across an enterprise IT environment, said Udovicic. Wavefront already supports Kubernetes clusters.
In general, Udovicic noted that developers tend to embrace a variety of open source approaches to monitoring applications as they build their applications. But a SaaS application such as Wavefront makes it easier for enterprise IT organizations to apply to monitor across a broad range of applications. To facilitate that transition, Wavefront provides an OpenTracing/OpenCensus compliant solution that includes a drop-in replacement for the Zipkin and Jaeger distributed tracing software, which is at the core of many open source monitoring tools.
VMware is clearly betting that increased reliance on AI will push organizations toward relying more on cloud services. Machine learning algorithms require access to massive amounts of data before they can construct AI models capable of accurately predicting events. The cost of collecting and storing all that data, plus the expertise required to train the AI models, are beyond the capabilities of most DevOps teams.
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Most enterprise IT organizations have applied monitoring tools to only their most critical applications, mainly because of cost concerns. But as applications based on microservices proliferate across the enterprise, it’s becoming apparent that all the dependencies that will exist between various application services will need to be continuously monitored. The only issue now is determining how best to achieve that goal as part of an integrated set of DevOps processes.
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