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The Perils of Downtime in the Cloud

Cliff Moon

The mantra for developers at Facebook for the longest time has been "move fast and break things". The idea behind this philosophy being that the stigma around screwing up and breaking production slows down feature development, therefore if one removes the stigma from breakage, more agility will result. The cloud readily embodies this philosophy, since it is explicitly made of of unreliable components. The challenge for the enterprise embracing the cloud is to build up the processes and resiliency necessary to build reliable systems from unreliable components. Otherwise, moving to the cloud will mean that your customers are the first people to notice when you are experiencing downtime.

So what changes are necessary to remove the costs of downtime in the cloud? Foremost what is needed is a move to a more resilient architecture. The health of the service as a whole cannot rely on any single node. This means no special nodes: everything gets installed onto multiple instances with active-active load balancing between identical services. Not only that, but any service with a dependency must be able to survive that dependency going away. Writing code that is resilient to the myriad failures that may happen in the cloud is an art unto itself. No one will be good at it to start. This is where process and culture modifications come in.

It turns out that if you want programmers to write code that behaves well in production, an effective way to achieve that is to make them responsible for the behavior of their code in production. The individual programmers go on pager rotation and because they have to work side by side with the other people on rotation, they are held accountable for the code they write. It should never be an option to point to the failure of another service as the cause of your own service's failure. The writers of each discrete service should be encouraged to own their availability by measuring it separately from that of their dependencies. Techniques like serving stale data from cache, graceful degradation of ancillary features, and well reasoned timeout settings are all useful for being resilient while still depending on unreliable dependencies.

If your developers are on pager rotation, then there should be something to page them about. This is where monitoring comes in. Monitoring alerts come in two basic flavors: noise and signal. Monitoring setups with too many alerts configured will tend to be noisy, which leads to alert fatigue.

A good rule of thumb for any alerts you may have setup are that they be: actionable, impacting, and imminent. By actionable, I mean that there is a clear set of steps for resolving the issue. An actionable alert would be to tell you that a service has gone down. Less actionable would be to tell you that latencies are up, since it isn't clear what, if anything, you could do about that.

Impacting means that without human intervention the underlying condition will either cause or continue to cause customer impact.

And imminent means that the alert requires immediate intervention to alleviate service disruption. An example of a non-imminent alert would be alerting that your SSL certificates were due to expire in a month. Impactful and actionable, absolutely. But it doesn't warrant getting out of bed in the middle of the night.

At the end of the day, adopting the cloud alone isn't going to be the silver bullet that automatically injects agility into your team. The culture and structure of the team must be adapted to fit the tools and platforms they use in order to get the most out of them. Otherwise, you're going to be having a lot of downtime in the cloud.

Cliff Moon is CTO and Founder of Boundary.

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The Perils of Downtime in the Cloud

Cliff Moon

The mantra for developers at Facebook for the longest time has been "move fast and break things". The idea behind this philosophy being that the stigma around screwing up and breaking production slows down feature development, therefore if one removes the stigma from breakage, more agility will result. The cloud readily embodies this philosophy, since it is explicitly made of of unreliable components. The challenge for the enterprise embracing the cloud is to build up the processes and resiliency necessary to build reliable systems from unreliable components. Otherwise, moving to the cloud will mean that your customers are the first people to notice when you are experiencing downtime.

So what changes are necessary to remove the costs of downtime in the cloud? Foremost what is needed is a move to a more resilient architecture. The health of the service as a whole cannot rely on any single node. This means no special nodes: everything gets installed onto multiple instances with active-active load balancing between identical services. Not only that, but any service with a dependency must be able to survive that dependency going away. Writing code that is resilient to the myriad failures that may happen in the cloud is an art unto itself. No one will be good at it to start. This is where process and culture modifications come in.

It turns out that if you want programmers to write code that behaves well in production, an effective way to achieve that is to make them responsible for the behavior of their code in production. The individual programmers go on pager rotation and because they have to work side by side with the other people on rotation, they are held accountable for the code they write. It should never be an option to point to the failure of another service as the cause of your own service's failure. The writers of each discrete service should be encouraged to own their availability by measuring it separately from that of their dependencies. Techniques like serving stale data from cache, graceful degradation of ancillary features, and well reasoned timeout settings are all useful for being resilient while still depending on unreliable dependencies.

If your developers are on pager rotation, then there should be something to page them about. This is where monitoring comes in. Monitoring alerts come in two basic flavors: noise and signal. Monitoring setups with too many alerts configured will tend to be noisy, which leads to alert fatigue.

A good rule of thumb for any alerts you may have setup are that they be: actionable, impacting, and imminent. By actionable, I mean that there is a clear set of steps for resolving the issue. An actionable alert would be to tell you that a service has gone down. Less actionable would be to tell you that latencies are up, since it isn't clear what, if anything, you could do about that.

Impacting means that without human intervention the underlying condition will either cause or continue to cause customer impact.

And imminent means that the alert requires immediate intervention to alleviate service disruption. An example of a non-imminent alert would be alerting that your SSL certificates were due to expire in a month. Impactful and actionable, absolutely. But it doesn't warrant getting out of bed in the middle of the night.

At the end of the day, adopting the cloud alone isn't going to be the silver bullet that automatically injects agility into your team. The culture and structure of the team must be adapted to fit the tools and platforms they use in order to get the most out of them. Otherwise, you're going to be having a lot of downtime in the cloud.

Cliff Moon is CTO and Founder of Boundary.

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In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...

Despite the frustrations, every engineer we spoke with ultimately affirmed the value and power of OpenTelemetry. The "sucks" moments are often the flip side of its greatest strengths ... Part 2 of this blog covers the powerful advantages and breakthroughs — the "OTel Rocks" moments ...

OpenTelemetry (OTel) arrived with a grand promise: a unified, vendor-neutral standard for observability data (traces, metrics, logs) that would free engineers from vendor lock-in and provide deeper insights into complex systems ... No powerful technology comes without its challenges, and OpenTelemetry is no exception. The engineers we spoke with were frank about the friction points they've encountered ...

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