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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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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 ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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