Solutions for Minimizing Server Downtime

Chris Adams

As we've seen, hardware is at the root of a large proportion of data center outages, and the costs and consequences are often exacerbated when VMs are affected. The best answer, therefore, is for IT pros to get back to basics.

Start with Part 1: Complacency Kills Uptime in Virtualized Environments

Just as drivers wearing seatbelts should still use turn signals (even though many don't), data center managers should continue to take the usual precautions to protect against equipment-related outages. Put simply:

Attend to the hardware

In the rush to implement the latest technologies, don't overlook the fundamentals, such as routine server maintenance, UPS tests and upgrades, and facility checks for hotspots, air flow problems, and other issues.

Integrate monitoring and response

Only about half of IT organizations rely on their monitoring tool or ticketing system to activate a response team. This is a lost opportunity for accelerating break/fix. So is the failure to utilize newer AI-driven hardware monitoring technologies which are becoming highly accessible.

Have parts on standby

It's no good to go searching for spares after a hardware failure occurs. Spare parts should be on site for mission critical systems or available for quick delivery in other cases.

Invest in expertise

Having the right people with the right skills is essential. Unfortunately, today's tight IT labor market is making it difficult to find and afford talent. Data center managers should consider whether they have the budget to build comprehensive engineering capabilities or if they are better off sourcing it from a partner.

It can be hard to manage these tasks in addition to the many responsibilities that have been piled on data center personnel over the past decade. In many cases, the easiest and most affordable option is to hand off the bulk of the hardware "to do" list to a third-party provider specializing in IT support. That way someone else can effectively address the risk associated with hardware through 24/7 monitoring, spares management, and immediate Level 3 support while the business gets back to business.

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Solutions for Minimizing Server Downtime

Chris Adams

As we've seen, hardware is at the root of a large proportion of data center outages, and the costs and consequences are often exacerbated when VMs are affected. The best answer, therefore, is for IT pros to get back to basics.

Start with Part 1: Complacency Kills Uptime in Virtualized Environments

Just as drivers wearing seatbelts should still use turn signals (even though many don't), data center managers should continue to take the usual precautions to protect against equipment-related outages. Put simply:

Attend to the hardware

In the rush to implement the latest technologies, don't overlook the fundamentals, such as routine server maintenance, UPS tests and upgrades, and facility checks for hotspots, air flow problems, and other issues.

Integrate monitoring and response

Only about half of IT organizations rely on their monitoring tool or ticketing system to activate a response team. This is a lost opportunity for accelerating break/fix. So is the failure to utilize newer AI-driven hardware monitoring technologies which are becoming highly accessible.

Have parts on standby

It's no good to go searching for spares after a hardware failure occurs. Spare parts should be on site for mission critical systems or available for quick delivery in other cases.

Invest in expertise

Having the right people with the right skills is essential. Unfortunately, today's tight IT labor market is making it difficult to find and afford talent. Data center managers should consider whether they have the budget to build comprehensive engineering capabilities or if they are better off sourcing it from a partner.

It can be hard to manage these tasks in addition to the many responsibilities that have been piled on data center personnel over the past decade. In many cases, the easiest and most affordable option is to hand off the bulk of the hardware "to do" list to a third-party provider specializing in IT support. That way someone else can effectively address the risk associated with hardware through 24/7 monitoring, spares management, and immediate Level 3 support while the business gets back to business.

Related Links

Hot Topics

The Latest

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Why FinOps Rewrote Its Mission and What It Signals for Technology Management

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

Explainability Is the New Battleground in AI-Powered Observability

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

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AI Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...