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How Stephen Hawking Taught Us an Important Lesson About Preparing for Traffic Spikes

Archana Kesavan

The recent outage of the University of Cambridge website hosting Stephen Hawking's doctoral thesis is a prime example of what happens when niche websites become exposed to mainstream levels of traffic.

The widespread fame of the author as one of the figureheads of science generated a level of interest the university's web team was not prepared to handle, resulting in a familiar story: Website goes live; minutes or hours later, it crashes due to the large influx of traffic.

While it is obvious that the University of Cambridge didn't expect the level of traffic they saw, there are steps organizations and enterprises of all sizes can take to prevent this kind of digital downtime.

On Oct. 23, Hawking's Ph.D thesis went live, but by Oct. 24, the website had crashed. The release of the paper was timed with Open Access Week 2017, a worldwide event aimed at promoting free and open access to scholarly research. Though the scholarly research was made available through the university, within 24 hours of its release, no one could access it.

According to a Cambridge spokesperson, the website received nearly 60,000 download requests in less than 24 hours, causing a shutdown of the page, slower runtimes, and inaccessible content for users.

While this could be the first time a doctoral thesis invoked such widespread interest, this kind of problem, due to overloaded networks has unfolded before. In this case, it seems that the sudden increase in the number of visitors saturated the infrastructure that hosts and delivers this research. This happens when the amount of processing power required to determine what the searcher is looking for and where to send it exceeds the ability of the machines (routers, switches and servers) on the network to respond.

Organizations like Cambridge University often have limited processing power on their networks either because they build their own data centers, reducing their flexibility to respond to spikes in traffic. While each individual request may only take a fraction of each machine's resources, when several come in at once, it can slow connections, create congestion or even absolute failure.


Figure 1: Global locations unable to access the Cambridge University website, with errors in the connect and receive stages.


Figure 2: Traffic from all over the world terminates within the Cambridge infrastructure, as indicated by the spike in packet loss

For a web property like the Cambridge library, this is a temporary surge in traffic -- but not all websites are this lucky. The lesson is that if an organization isn't prepared, this is how a problem would manifest itself. Pre-planning for a spike would include increasing capacity on existing infrastructure. Leveraging a CDN can also help distribute the load across servers/geographies.

As you make important decisions about your company's website, there are many factors you'll want to consider, especially if you're expecting a surge (like on Black Friday or Cyber Monday). For sites that have spiky, but predictable traffic, here are a few options to help them stay online:

■ Use a CDN to serve up traffic round-the clock. This costs more but will have the best customer experience.

■ Flip on a CDN service well before known traffic peaks. If Cambridge had done this prior to releasing Hawking's thesis, they could have stayed afloat during the massive download requests.

■ Diversify with multiple data centers and upstream ISPs. If your organization has only one data center and one upstream ISP — if the ISP or their single data center goes down, your service goes with it.

■ Within the data center, load balanced network paths and web servers can also help reduce performance impacts.

The University of Cambridge may not plan to release another legendary scientist's thesis again anytime soon, but when it comes to web performance, you can have a guaranteed return if you properly prepare for your network's next big event.

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How Stephen Hawking Taught Us an Important Lesson About Preparing for Traffic Spikes

Archana Kesavan

The recent outage of the University of Cambridge website hosting Stephen Hawking's doctoral thesis is a prime example of what happens when niche websites become exposed to mainstream levels of traffic.

The widespread fame of the author as one of the figureheads of science generated a level of interest the university's web team was not prepared to handle, resulting in a familiar story: Website goes live; minutes or hours later, it crashes due to the large influx of traffic.

While it is obvious that the University of Cambridge didn't expect the level of traffic they saw, there are steps organizations and enterprises of all sizes can take to prevent this kind of digital downtime.

On Oct. 23, Hawking's Ph.D thesis went live, but by Oct. 24, the website had crashed. The release of the paper was timed with Open Access Week 2017, a worldwide event aimed at promoting free and open access to scholarly research. Though the scholarly research was made available through the university, within 24 hours of its release, no one could access it.

According to a Cambridge spokesperson, the website received nearly 60,000 download requests in less than 24 hours, causing a shutdown of the page, slower runtimes, and inaccessible content for users.

While this could be the first time a doctoral thesis invoked such widespread interest, this kind of problem, due to overloaded networks has unfolded before. In this case, it seems that the sudden increase in the number of visitors saturated the infrastructure that hosts and delivers this research. This happens when the amount of processing power required to determine what the searcher is looking for and where to send it exceeds the ability of the machines (routers, switches and servers) on the network to respond.

Organizations like Cambridge University often have limited processing power on their networks either because they build their own data centers, reducing their flexibility to respond to spikes in traffic. While each individual request may only take a fraction of each machine's resources, when several come in at once, it can slow connections, create congestion or even absolute failure.


Figure 1: Global locations unable to access the Cambridge University website, with errors in the connect and receive stages.


Figure 2: Traffic from all over the world terminates within the Cambridge infrastructure, as indicated by the spike in packet loss

For a web property like the Cambridge library, this is a temporary surge in traffic -- but not all websites are this lucky. The lesson is that if an organization isn't prepared, this is how a problem would manifest itself. Pre-planning for a spike would include increasing capacity on existing infrastructure. Leveraging a CDN can also help distribute the load across servers/geographies.

As you make important decisions about your company's website, there are many factors you'll want to consider, especially if you're expecting a surge (like on Black Friday or Cyber Monday). For sites that have spiky, but predictable traffic, here are a few options to help them stay online:

■ Use a CDN to serve up traffic round-the clock. This costs more but will have the best customer experience.

■ Flip on a CDN service well before known traffic peaks. If Cambridge had done this prior to releasing Hawking's thesis, they could have stayed afloat during the massive download requests.

■ Diversify with multiple data centers and upstream ISPs. If your organization has only one data center and one upstream ISP — if the ISP or their single data center goes down, your service goes with it.

■ Within the data center, load balanced network paths and web servers can also help reduce performance impacts.

The University of Cambridge may not plan to release another legendary scientist's thesis again anytime soon, but when it comes to web performance, you can have a guaranteed return if you properly prepare for your network's next big event.

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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 ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

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 ...

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 ...

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 ...

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 ...

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 ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

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.

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 ...