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Availability: Time Warp

Terry Critchley

"You can fool some of the people all of the time, and all of the people some of the time, but you cannot fool all of the people all of the time."
Abraham Lincoln

Some of the outage figures quoted by organizations look ludicrously small to me. Without casting aspersions on the veracity of these figures (or availability statistics), I do feel that some examination of them is needed.

The non-availability of a system is often quoted thus:

where MTTR is the Mean Time To Repair a particular outage.

”We recognized we'd run the wrong job and restarted correctly in just 3 minutes, thus our MTTR = 3 minutes."

If you substitute the word "recover" for "repair" in the above definition you will be closer to the truth. However, your database(s) are almost certainly on Planet Zog as far as consistency is concerned and the "repair" of that will often take much longer. The correct definition of MTTR should be "mean time to recover" and the equation then looks as above but with a new MTTR:

The last item in this equation I call the ramp up time, the time to get the show back on the road. This can be small but is often much larger than repair time, as shown in the diagram below. A decent Service Level Agreement (SLA) will opt for this definition of "fixed" for an issue and will include the ramp up time in the recovery time specification.

The recovery of a database or other data and metadata corrupted by human error or malware can take a considerable time to restore to the working status demanded by the end users.

This is borne out by several “Never Again” cases outlined in the Availability Digest (under the heading: Never Again) where financial bodies — banks, stock dealings — have repaired faults but taken many hours to recover normal working conditions again. ”The system was repaired at 11am and trading commenced normally at 2:30pm" is a typical (hypothetical) report on such situations.

The final point to make is that there are several viewpoints of an “outage” or period of "downtime", depending on your place in an organization. The end user's view will be that the outage lasts as long as he/she is prevented from using IT to do the job they are supposed to do. The server specialist's view might be that the outage of his hardware was a mere minute or two before it was fixed whereas the network person will say" “what's all the fuss about; everything on the network is working fine?”

It all depends on your viewpoint and I know what viewpoint the company CEO, the users and the board will take. Do you?

Dr. Terry Critchley is the Author of “High Availability IT Services” ISBN 9781482255904 (CRC Press).

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Availability: Time Warp

Terry Critchley

"You can fool some of the people all of the time, and all of the people some of the time, but you cannot fool all of the people all of the time."
Abraham Lincoln

Some of the outage figures quoted by organizations look ludicrously small to me. Without casting aspersions on the veracity of these figures (or availability statistics), I do feel that some examination of them is needed.

The non-availability of a system is often quoted thus:

where MTTR is the Mean Time To Repair a particular outage.

”We recognized we'd run the wrong job and restarted correctly in just 3 minutes, thus our MTTR = 3 minutes."

If you substitute the word "recover" for "repair" in the above definition you will be closer to the truth. However, your database(s) are almost certainly on Planet Zog as far as consistency is concerned and the "repair" of that will often take much longer. The correct definition of MTTR should be "mean time to recover" and the equation then looks as above but with a new MTTR:

The last item in this equation I call the ramp up time, the time to get the show back on the road. This can be small but is often much larger than repair time, as shown in the diagram below. A decent Service Level Agreement (SLA) will opt for this definition of "fixed" for an issue and will include the ramp up time in the recovery time specification.

The recovery of a database or other data and metadata corrupted by human error or malware can take a considerable time to restore to the working status demanded by the end users.

This is borne out by several “Never Again” cases outlined in the Availability Digest (under the heading: Never Again) where financial bodies — banks, stock dealings — have repaired faults but taken many hours to recover normal working conditions again. ”The system was repaired at 11am and trading commenced normally at 2:30pm" is a typical (hypothetical) report on such situations.

The final point to make is that there are several viewpoints of an “outage” or period of "downtime", depending on your place in an organization. The end user's view will be that the outage lasts as long as he/she is prevented from using IT to do the job they are supposed to do. The server specialist's view might be that the outage of his hardware was a mere minute or two before it was fixed whereas the network person will say" “what's all the fuss about; everything on the network is working fine?”

It all depends on your viewpoint and I know what viewpoint the company CEO, the users and the board will take. Do you?

Dr. Terry Critchley is the Author of “High Availability IT Services” ISBN 9781482255904 (CRC Press).

Hot Topics

The Latest

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

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 ...

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 ...

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 ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...