Fixing and Preventing Application Outages: 5 Questions That Just Might Save Your Network

Akhil Sahai

Why do outages of mission-critical applications still happen? Aren't there multiple solutions that can alert IT teams to problems? Yes – and that can be part of the problem. One alarm goes off, and then another and another until the team is quickly overwhelmed. By the time an incident like this is brought under control, the company may have lost on average as much as $750,000 for a 90-minute outage, according to the Ponemon report, Cost of Data Center Outages, in addition to loss of face and damage to brand value.

What typically happens next is that forensic experts go through multiple product consoles and logs to identify the cause of an incident, and the blame gets passed around as time ticks away. But deep machine learning-based, root-cause analytics and predictive analytics technologies are helping organizations dramatically prevent such incidents and reduce mean time to repair.

In a digital-first world, teams must manage unparalleled amounts of data while predicting and preventing outages, in real time, while maintaining and delivering agile, reliable applications. The problem is that most organizations must tap several different siloed vendor tools to assist in the monitoring, identifying, mitigation and remediation of incidents and hope that they speak to each other, which traditionally hasn't happened.

IT infrastructure keeps changing from physical to hybrid and multi-cloud environments, and new architectures keep arising. Consequently, it is becoming impossible for IT administrators to keep up with the multitude of objects, with thousands of metrics generating data in near-real time.

Applications in today's environments need to be reliable and secure within these high performance environments, so new approaches must be employed to provide intelligence. Automated, self-learning solutions that analyze and provide insight into applications and infrastructure topologies are essential in this transformation.

The New Language of Monitoring Tools

Vendors are throwing around phrases like "big data" and "machine learning" because organizations understand that these features can help them tackle the complex needs of application performance. But what do they really mean?

Machine Learning: Machine learning is self-learning, supervised or unsupervised algorithms that can be based on neural networks, statistics or Digital Signal Processing et al.

Big Data Architecture: A framework for managing masses of structured and unstructured data in an automated, highly scalable way using open source technologies.

Domain Knowledge: Questions about what happened, what caused it, how to remediate it and prevent it from happening again – the domain knowledge in TechOps and DevOps helps answer them.

5 Questions About Your Monitoring Solution

Ask these 5 questions before moving forward with a monitoring solution that can address application outages:

1. Immediate Intelligence: Does the solution identify in real time, those alarms that need immediate attention?

2. Scalability: Does the solution scale and is it able to handle millions of objects?

3. Automation: Does it quickly pinpoint the root cause of the problem and identify how to fix it, rather than relying on expensive domain experts?

4. Communal Wisdom: Do you have access to tribal knowledge such as vendor knowledge bases, discussion forums and the latest state-of-the-art technologies in order to help teams remediate incidents quickly and efficiently?

5. Prevention: Historically, monitoring tools send alerts only after a problem has already occurred or when the rules and set thresholds are violated, but the key to preventing outages is to predict issues in advance. Does the solution provide alerts to anomalous trends or potentially dangerous issues before they impact your application?

Yesterday's siloed IT management tools tend not to communicate with each other, causing confusion and over-alerting. This makes it difficult, if not impossible, for IT teams to determine what's worth investigating and what's not – paving the way for service disruptions and breaches. Fortunately, today's solutions offer real-time insight and recommendations for remediation, as well as analytics that can predict trouble so you can stop problems before they start.

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Fixing and Preventing Application Outages: 5 Questions That Just Might Save Your Network

Akhil Sahai

Why do outages of mission-critical applications still happen? Aren't there multiple solutions that can alert IT teams to problems? Yes – and that can be part of the problem. One alarm goes off, and then another and another until the team is quickly overwhelmed. By the time an incident like this is brought under control, the company may have lost on average as much as $750,000 for a 90-minute outage, according to the Ponemon report, Cost of Data Center Outages, in addition to loss of face and damage to brand value.

What typically happens next is that forensic experts go through multiple product consoles and logs to identify the cause of an incident, and the blame gets passed around as time ticks away. But deep machine learning-based, root-cause analytics and predictive analytics technologies are helping organizations dramatically prevent such incidents and reduce mean time to repair.

In a digital-first world, teams must manage unparalleled amounts of data while predicting and preventing outages, in real time, while maintaining and delivering agile, reliable applications. The problem is that most organizations must tap several different siloed vendor tools to assist in the monitoring, identifying, mitigation and remediation of incidents and hope that they speak to each other, which traditionally hasn't happened.

IT infrastructure keeps changing from physical to hybrid and multi-cloud environments, and new architectures keep arising. Consequently, it is becoming impossible for IT administrators to keep up with the multitude of objects, with thousands of metrics generating data in near-real time.

Applications in today's environments need to be reliable and secure within these high performance environments, so new approaches must be employed to provide intelligence. Automated, self-learning solutions that analyze and provide insight into applications and infrastructure topologies are essential in this transformation.

The New Language of Monitoring Tools

Vendors are throwing around phrases like "big data" and "machine learning" because organizations understand that these features can help them tackle the complex needs of application performance. But what do they really mean?

Machine Learning: Machine learning is self-learning, supervised or unsupervised algorithms that can be based on neural networks, statistics or Digital Signal Processing et al.

Big Data Architecture: A framework for managing masses of structured and unstructured data in an automated, highly scalable way using open source technologies.

Domain Knowledge: Questions about what happened, what caused it, how to remediate it and prevent it from happening again – the domain knowledge in TechOps and DevOps helps answer them.

5 Questions About Your Monitoring Solution

Ask these 5 questions before moving forward with a monitoring solution that can address application outages:

1. Immediate Intelligence: Does the solution identify in real time, those alarms that need immediate attention?

2. Scalability: Does the solution scale and is it able to handle millions of objects?

3. Automation: Does it quickly pinpoint the root cause of the problem and identify how to fix it, rather than relying on expensive domain experts?

4. Communal Wisdom: Do you have access to tribal knowledge such as vendor knowledge bases, discussion forums and the latest state-of-the-art technologies in order to help teams remediate incidents quickly and efficiently?

5. Prevention: Historically, monitoring tools send alerts only after a problem has already occurred or when the rules and set thresholds are violated, but the key to preventing outages is to predict issues in advance. Does the solution provide alerts to anomalous trends or potentially dangerous issues before they impact your application?

Yesterday's siloed IT management tools tend not to communicate with each other, causing confusion and over-alerting. This makes it difficult, if not impossible, for IT teams to determine what's worth investigating and what's not – paving the way for service disruptions and breaches. Fortunately, today's solutions offer real-time insight and recommendations for remediation, as well as analytics that can predict trouble so you can stop problems before they start.

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Keys to Building a Partner Ecosystem That Scales

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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