Nobl9 Debuts Service Level Analyzer for Reliability Management
Understanding how IT applications are meeting service-level agreements (SLAs) is a critical part of helping organizations define and manage service-level objectives (SLOs).
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Service-level observability vendor Nobl9 is aiming to help its users better automate performance and reliability goals with the launch of the company's new Server Level Analyzer.
Nobl9 developed its core technology to help organizations with service-level objectives (SLOs), which define a desired target for IT operations and applications in terms of how a service should perform. With the new Service Level Analyzer technology, Nobl9 is taking its platform a step deeper by enabling IT operations professionals to get metrics and data from more than two dozen observability tools, including Splunk, Datadog, Prometheus, Dynatrace, New Relic, Google Cloud Monitoring, and Amazon Cloud Watch, among others.
According to Nobl9, the Service Level Analyzer gives organizations multiple new capabilities, including the ability to:
Generate significant objectives with just a few clicks by utilizing actual incident data.
Conduct rapid "what-if" analysis to assess the effects of various targets and thresholds on error budgets.
Efficiently enhance their comprehension of how customer experience is affected by incidents.
"It's an entirely new capability in Nobl9 to model different scenarios and see how much error budget would have been consumed under different sets of assumptions," Brian Singer, co-founder and chief product officer at Nobl9, told ITPro Today. "This fills an important gap in the market — companies can struggle getting widespread adoption of SLOs because it can be hard to dial in the right settings when getting started."
How the Nobl9 Service Level Agreement Analyzer Works
For any type of IT service, there is often a service-level agreement (SLA) that defines what level of service and performance the end user should expect to receive.
Singer noted that an SLA is typically a contract between two organizations specifying a certain level of guaranteed availability for a service, with penalties if that is not met. Nobl9's Service Level Analyzer helps organizations meet their SLAs.
Service Level Analyzer takes the historical data from a given service and models out whether that service would be able to meet the SLA, he said.
In order to meet an SLA, an engineering team must plan for better availability than what the SLA specifies; Service Level Analyzer gives the team the analytics required to specify the right SLO that will let them meet a given SLA, according to Singer. The Nobl9 analyzer can also help organizations provide different tiers of SLAs, so they can potentially charge more for a higher level of service.
Getting all the metrics from the different observability tools in order to analyze an SLA was particularly challenging for Nobl9.
"We had to build a fairly sophisticated engine to query the data from these different sources, being especially careful not to hit API rate limits or cause other problems with these external sources," Singer said. "Normalizing the data is a challenge as well because each data source treats things slightly differently. For example, the granularity of data can change, and the amount of data in a given query can be different."
The Intersection of SLAs and SLOs
There is an important distinction between what SLAs and SLOs provide, with IT operations needing to consider both sets of metrics.
A service-level objective can help an engineering team meet a service-level agreement they contractually have to hit because SLOs provide an early warning mechanism, giving teams time to remediate issues that might cause the SLA to be breached, Singer said. SLOs also provide transparency around the reasons an SLA might have been breached in the past, he added.
"SLAs are usually set to be minimums since they are tied to financial penalties, but it's very likely that customer expectations for reliability won't be met just by not breaching an SLA," Singer said. "That's where SLOs can be very helpful. They allow an engineering team to react based on delivering a satisfactory customer experience, not just the minimum required to not get fined."
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