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Google Cloud Platform > Case Studies > Auto Trader (UK): Enhancing Agility and Security in Automotive Marketplace with Istio and Google Cloud
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Auto Trader (UK): Enhancing Agility and Security in Automotive Marketplace with Istio and Google Cloud

Technology Category
  • Infrastructure as a Service (IaaS) - Private Cloud
  • Infrastructure as a Service (IaaS) - Public Cloud
Applicable Industries
  • Automotive
  • Consumer Goods
Applicable Functions
  • Product Research & Development
Use Cases
  • Construction Management
  • Vehicle-to-Infrastructure
Services
  • Cloud Planning, Design & Implementation Services
  • System Integration
The Challenge

Auto Trader (UK), the largest digital automotive marketplace in the United Kingdom, was facing challenges with its existing infrastructure. The company's infrastructure was previously deployed from its own data centers via a private cloud. The company wanted to improve its security and encryption, and create a more agile architecture to enable faster innovation. Auto Trader (UK) wanted to upgrade its security layers and implement mutual Transport Layer Security (mTLS) authentication between its apps. However, retrofitting these changes to its previous infrastructure would have been difficult due to the complexity of modifying hundreds of applications and building additional elements. The company also wanted to optimize its CPU allocation, which was previously uniform across all instances due to the inability to accurately measure the impact of changes.

About The Customer

Auto Trader (UK) is the United Kingdom's largest digital automotive marketplace. The company simplifies the journey of buying or selling a car for consumers, retailers, and manufacturers by connecting buyers and sellers, and offering a range of useful tools. These tools include detailed reviews of dealerships and new cars, valuation tools, and integrations with car finance and insurance partners. The company has a history of technological innovation and a working culture of experimentation. Auto Trader (UK) is mature from an engineering perspective, which enables it to get involved with early engagement access programs.

The Solution

To overcome these challenges, Auto Trader (UK) decided to migrate from a virtual machine-based infrastructure to a containerized one on a public cloud. The company moved its backend applications to Kubernetes Engine (GKE) and implemented Istio, an open-source, transparent service mesh. This move was aimed at achieving their security goals and making system improvements. Istio provided the company with increased visibility over applications in terms of response times and other important service metrics. This increased visibility enabled Auto Trader (UK) to optimize its CPU allocation by adjusting CPU and RAM in GKE and observing the impact across all performance metrics. The company also implemented automation to reduce operational overheads, using Cloud Load Balancing to spread instances over three zones and serve their domain names using Cloud DNS. Auto Trader (UK) also started using Network Intelligence Center for network monitoring and gaining visibility into the network.

Operational Impact
  • The migration to Google Cloud has brought significant benefits to Auto Trader (UK) from both an operational and development perspective. The company's security has been improved in several ways. With Istio enabling mTLS, customer requests are now completely encrypted right through to the application handling the request. This is a significant step forward in terms of security. The fine-grained level of control over network policies on Kubernetes also means the company's applications are more isolated, making the network more secure in the case of an attack. The company has also seen gains in efficiency. Thanks to the CPU and RAM optimizations put in place with Istio on Kubernetes Engine, the company is 75 percent more efficient in terms of usage, without impacting performance. This has enabled the company to manage and predict future spending more reliably.

Quantitative Benefit
  • Reduced deployment time by almost 97%, from 10 minutes to 20 seconds.

  • Improved resource utilization by up to 75%.

  • Saved several hours of faultfinding and support calls with the Performance Dashboard in Network Intelligence Center.

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