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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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Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...