AIOps

Observability And AIOps: Why Convergence Is The Future To Improving Uptime

Nastel® Technologies
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On October 4, Facebook and its properties, Instagram and WhatsApp, were down for more than five hours due to configuration changes on routers in Facebook’s data centers. A five-hour outage is an eternity in our always-on digital economy, costing the company an estimated $65 million and 4.8% in stock valuation.

 

The high-profile Facebook outage is emblematic of just how digitally intermediated our economy is becoming, and the incident renews C-level focus on preventing similar service failures. After all, outages can disenchant customers, damage corporate brands and hurt sales and growth.

 

Many business executives are likely grappling with their own service assurance. If the system of one of the world’s largest tech companies can go down for an extended period, what does that mean for my company? How do I avoid costly service interruptions in an increasingly digital economy that’s unforgiving of downtime?

 

To be clear, you can never fully protect against outages happening, but you can significantly mitigate the damage they do. For that, modern companies need to move beyond observability platforms and AIOPs tools. They need the convergence of observability and AIOps. Here’s why:

 

Observability And AIOps: Better Together

Observability and AIOps measure different types of data, and as a result, these tools provide different types of indicators. Let’s dive into the benefits of each.

A fully observable system provides enough visibility into its operations that humans can understand the system’s internal state based on its external outputs. Observability tools monitor telemetry data — logs, traces and metrics — to understand the performance and behavior of distributed systems that are constantly changing. A change in telemetry data is a leading indicator of a system failure.

 

However, observability tools on their own aren’t enough. If you were on an SRE or DevOps team at Facebook on October 4, perhaps your observability tool would notify your team that there were problems in the system’s telemetry data. Although an incident notification is critically important, the tool probably stopped there. The data likely lacked actionable diagnostics, falling short of diagnosing the problem and suggesting a fix.

 

Enter AIOps, which is associated with alert data. AIOps tools collect and analyze data from across disparate data sources to give DevOps and SRE teams a holistic end-to-end view of what’s going on in a distributed IT environment. These tools expand the system’s visibility to surface significant events likely to cause an interruption in an organization’s apps and vital surfaces. In other words, AIOps typically provide lagging indicators, surfacing data and catching an operator’s attention after an incident has already occurred. The benefit is that AIOps tools leverage AI and ML to provide increasingly accurate mitigation strategies.

If you are on an SRE or DevOps team at Facebook, perhaps your AIOps tool surfaced an anomaly in the metrics data, sent you an alert and offered a fix. However, that was an end state, and precious time could have elapsed if you didn’t use observability tools.

 

Converging observability and AIOps can allow you to effectively anticipate, detect and resolve incidents in modern, intertwined systems and handle the explosive growth in data volumes. When teams use observability and AIOps in conjunction, they link metrics and alert data for a complete picture of and deep insights into complex systems. Observability offers early indicators, deep diagnosis and fast detection. AIOps tools provide end-to-end, comprehensive views of entire tech stacks for quick, accurate problem resolution.

 

How To Successfully Adopt Observability And AIOps Technology

Companies transitioning their monitoring tools to a unified observability and AIOps platform should approach this new technology deployment with care and expect some trepidation from IT teams that are charged with maintaining mission-critical services.

 

Here are practical ways to successfully deploy an intelligent observability solution that will keep pace with today’s IT challenges:

 

1. Lead from the top down. Executives at the CIO or VP level should provide strategic oversight of the implementation, demonstrate why this technology is worth investment and ease IT teams’ fears of being automated out of a job.

2. Educate your team. Provide initial education about AIOps and observability before the technology deployment and continuous training on your platform after implementation.

3. Try it out. Most vendors offer free trials of their platforms to get teams familiar with the technology before it is implemented across the entire production environment.

4. Start with a proof-of-concept (POC) project. Start small and identify just one or two applications and services that will be a testbed for your intelligent observability tool.

5. Reassess the results. Observe the kind of data your platform surfaces and review the POC project’s results to ensure they align with your goals and expectations.

6. Share your experiences. Communicate the results of the POC with IT teams and business stakeholders, tying incident resolutions to tangible business benefits.

 

The October Facebook outage reminded the world just how dynamic, complex and fragile underlying technologies like microservices and containers have become. A change in infrastructure, applications and services needs rapid response — so consider transitioning to intelligent observability. In today’s economy, downtime can equal big dollars.

 

This article originally appeared on forbes.com, to read the full article, click here.

Nastel Technologies, a global leader in integration infrastructure (i2) and transaction management for mission-critical applications, helps companies achieve flawless delivery of digital services.

 

Nastel delivers Integration Infrastructure Management (i2M), Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate. To answer business-centric questions and provide actionable guidance for decision-makers.
The Nastel Platform delivers:

  • Integration Infrastructure Management (i2M)
  • Predictive and Proactive anomaly detection that virtually eliminates war room scenarios and improves root cause analysis
  • Self-service for DevOps and CI: CD teams to achieve their speed to market goals
  • Advanced reporting and alerting for business, IT, compliance, and security purposes
  • Decision Support (DSS) for business and IT
  • Visualization of end-to-end user experiences through the entire application stack
  • Innovative Machine Learning AI to compare real-time to the historical record and discover and remediate events before they are critical
  • Large scale, high-performance complex event processing that delivers tracing, tracking, and stitching of all forms of machine data
  • And much more

Comments

  • Dave
    December 21, 2021
    Fascinating piece of research, but what is unclear from this summary of this study is if the AI is actually more or less reliable than human analysis. 5 radiologists is quite a small sample, and the range of accuracy of fake detection is so wide that the results of the AI actually fit inside that range. Are the volumes of tests enough to be statistically viable? And of course the real issue is that compromised images could take many forms, some could be complete real images replacing the image to be tested, while others could have artifacts added or removed. So is the issue AI or the ability to secure the way an image is managed from creation to analysis. That then becomes a integration infrastructure management (i2M) problem.
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