The trackside garage at an F1 race is a tight working environment and a team of only two IT technicians face pressure from both the factory and trackside staff to get the trackside IT up and running very fast. Yet, despite all these pressures, Aston Martin Red Bull Racing do not have a cloud-led strategy. Instead they have chosen to keep all IT in house.
The reason for this is performance. F1 is arguably the ultimate performance sport. A walk round the team’s factory in Milton Keynes, England, makes it abundantly clear that the whole organization is hell bent on maximizing performance. 700 staff at the factory are all essentially dedicated to the creation of just two cars. The level of detail that is demanded in reaching peak performance is truly mind blowing. For example, one machine with a robotic arm that checks the dimensions of the components built at the factory is able to measure accuracy to a scale 10 times thinner than a human hair.
This quest for maximum performance, however, is hampered at every turn by the stringent rules from the F1 governing body – the FIA. Teams face restrictions on testing and technology usage in order to prevent the sport becoming an arms race. So, for example, pre-season track testing is limited to only 8 days. Furthermore, wind tunnel testing is only allowed with 60% scale models and wind tunnel-usage is balanced with the use of Computational Fluid Dynamics (CFD) software, essentially a virtual wind tunnel. Teams that overuse one, lose time with the other.
In order to maximize performance within uniquely difficult logistical and regulatory conditions, the Aston Martin Red Bull Racing team has had to deploy a very powerful and agile IT estate.
According to Neil Bailey, Head of IT Infrastructure, Enterprise Architecture and Innovation, their legacy trackside infrastructure was “creaking”. Before choosing hyperconverged infrastructure, their “traditional IT had reached its limits”, says Bailey. “When things reach their limits they break, just like a car,” adds Bailey.
The team’s biggest emphasis for switching to HPE’s hyperconverged infrastructure, SimpliVity, was performance. Now, with “the extra performance of SimpliVity, it means it doesn’t get to its limits,” says Bailey. HPE SimpliVity has helped reduce space, has optimized processing power and brought more agility.
One of the first and most important use cases they switched to hyperconverged infrastructure was post-processing trackside data. During a race weekend each car is typically fitted with over 100 sensors providing key data on things like tyre temperature and downforce multiple times per second. Processing this data and acting on the insights is key to driving performance improvements. With their legacy infrastructure, Bailey says they were “losing valuable track time during free practice waiting for data processing to take place.” Since switching to HPE SimpliVity, data processing has dropped from being more than 15 minutes to less than 5 minutes. Overall, the team has seen a 79% performance boost compared to the legacy architecture. This has allowed for real time race strategy analysis and has improved race strategy decision making.
Data insights helps the team stay one step ahead, as race strategy decisions are data driven. For example, real time tyre temperature data helps the team judge tyre wear and make pit stop decisions. Real time access to tyre data helped the team to victory at the 2018 Chinese Grand Prix as the Aston Martin Red Bull cars pitted ahead of the rest of the field and Daniel Ricciardo swept to a memorable victory.
Hyperconverged infrastructure is also well suited to the “hostile” trackside environment, according to Bailey. With hyperconverged infrastructure, only two racks are needed at each race of which SimpliVity only takes up about 20% of the space, thus freeing up key space in very restricted trackside garages. Furthermore, with the team limited to 60 staff at each race, only two of Bailey’s team can travel. The reduction in equipment and closer integration of HPE SimpliVity means engineers can get the trackside data center up and running quickly and allow trackside staff to start work as soon as they arrive.
Since seeing the notable performance gains from using hyperconverged infrastructure for trackside data processing, the team has also transitioned some of the factory’s IT estate over to HPE SimpliVity. This includes: Aerodynamic metrics, ERP system, SQL server, exchange server and the team’s software house, the Team Foundation Server.
As well as seeing huge performance benefits, HPE SimpliVity has significantly impacted the work patterns of Bailey’s team of just ten. According to Bailey, the biggest operational win from hyperconverged infrastructure is “freeing up engineers’ time from focusing on ‘business as usual’ to innovation.” Traditional IT took up too much of the engineers’ time monitoring systems and just keeping things running. Now with HPE SimpliVity, Bailey’s team can “give the business more and quicker” and “be more creative with how they use technology.”
Hyperconverged infrastructure has given Aston Martin Red Bull Racing the speed, scalability and agility they require without any need to turn to the cloud. It allows them to deliver more and more resources to trackside staff in an increasingly responsive manner. However, even with all these performance gains, Aston Martin Red Bull Racing has been able to reduce IT costs. So, the users are happy, the finance director is happy and the IT team are happy because their jobs are easier. Hyperconvergence is clearly the right choice for the unique challenges of Formula 1 racing.
Project Bloodhound saved
The British project to break the world landspeed record at a site in the Northern Cape has been saved by a new backer, after it went into bankruptcy proceedings in October.
Two weeks ago, and two months after entering voluntary administration, the Bloodhound Programme Limited announced it was shutting down. This week it announced that its assets, including the Bloodhound Supersonic Car (SSC), had been acquired by an enthusiastic – and wealthy – supporter.
“We are absolutely delighted that on Monday 17th December, the business and assets were bought, allowing the Project to continue,” the team said in a statement.
“The acquisition was made by Yorkshire-based entrepreneur Ian Warhurst. Ian is a mechanical engineer by training, with a strong background in managing a highly successful business in the automotive engineering sector, so he will bring a lot of expertise to the Project.”
Warhurst and his family, says the team, have been enthusiastic Bloodhound supporters for many years, and this inspired his new involvement with the Project.
“I am delighted to have been able to safeguard the business and assets preventing the project breakup,” he said. “I know how important it is to inspire young people about science, technology, engineering and maths, and I want to ensure Bloodhound can continue doing that into the future.
“It’s clear how much this unique British project means to people and I have been overwhelmed by the messages of thanks I have received in the last few days.”
The record attempt was due to be made late next year at Hakskeen Pan in the Kalahari Desert, where retired pilot Andy Green planned to beat the 1228km/h land-speed record he set in the United States in 1997. The target is for Bloodhound to become the first car to reach 1000mph (1610km/h). A track 19km long and 500 metres wide has been prepared, with members of the local community hired to clear 16 000 tons of rock and stone to smooth the surface.
The team said in its announcement this week: “Although it has been a frustrating few months for Bloodhound, we are thrilled that Ian has saved Bloodhound SSC from closure for the country and the many supporters around the world who have been inspired by the Project. We now have a lot of planning to do for 2019 and beyond.”
Motor Racing meets Machine Learning
The futuristic car technology of tomorrow is being built today in both racing cars and
toys, writes ARTHUR GOLDSTUCK
The car of tomorrow, most of us imagine, is being built by the great automobile manufacturers of the world. More and more, however, we are seeing information technology companies joining the race to power the autonomous vehicle future.
Last year, chip-maker Intel paid $15.3-billion to acquire Israeli company Mobileye, a leader in computer vision for autonomous driving technology. Google’s autonomous taxi division, Waymo, has been valued at $45-billion.
Now there’s a new name to add to the roster of technology giants driving the future.
Amazon Web Services, the world’s biggest cloud computing service and a subsidiary of Amazon.com, last month unveiled a scale model autonomous racing car for developers to build new artificial intelligence applications. Almost in the same breath, at its annual re:Invent conference in Las Vegas, it showcased the work being done with machine learning in Formula 1 racing.
AWS DeepRacer is a 1/18th scale fully autonomous race car, designed to incorporate the features and behaviour of a full-sized vehicle. It boasts all-wheel drive, monster truck tires, an HD video camera, and on-board computing power. In short, everything a kid would want of a self-driving toy car.
But then, it also adds everything a developer would need to make the car autonomous in ways that, for now, can only be imagined. It uses a new form of machine learning (ML), the technology that allows computer systems to improve their functions progressively as they receive feedback from their activities. ML is at the heart of artificial intelligence (AI), and will be core to autonomous, self-driving vehicles.
AWS has taken ML a step further, with an approach called reinforcement learning. This allows for quicker development of ML models and applications, and DeepRacer is designed to allow developers to experiment with and hone their skill in this area. It is built on top of another AWS platform, called Amazon SageMaker, which enables developers and data scientists to build, train, and deploy machine learning quickly and easily.
Along with DeepRacer, AWS also announced the DeepRacer League, the world’s first global autonomous racing league, open to anyone who orders the scale model from AWS.
As if to prove that DeepRacer is not just a quirky entry into the world of motor racing, AWS also showcased the work it is doing with the Formula One Group. Ross Brawn, Formula 1’s managing director of Motor Sports, joined AWS CEO Andy Jassy during the keynote address at the re:Invent conference, to demonstrate how motor racing meets machine learning.
“More than a million data points a second are transmitted between car and team during a Formula 1 race,” he said. “From this data, we can make predictions about what we expect to happen in a wheel-to-wheel situation, overtaking advantage, and pit stop advantage. ML can help us apply a proper analysis of a situation, and also bring it to fans.
“Formula 1 is a complete team contest. If you look at a video of tyre-changing in a pit stop – it takes 1.6 seconds to change four wheels and tyres – blink and you will miss it. Imagine the training that goes into it? It’s also a contest of innovative minds.”
Formula 1 racing has more than 500 million global fans and generated $1.8 billion in revenue in 2017. As a result, there are massive demands on performance, analysis and information.
During a race, up to 120 sensors on each car generate up to 3GB of data and 1 500 data points – every second. It is impossible to analyse this data on the fly without an ML platform like Amazon SageMaker. It has a further advantage: the data scientists are able to incorporate 65 years of historical race data to compare performance, make predictions, and provide insights into the teams’ and drivers’ split-second decisions and strategies.
This means Formula 1 can pinpoint how a driver is performing and whether or not drivers have pushed themselves over the limit.
“By leveraging Amazon SageMaker and AWS’s machine-learning services, we are able to deliver these powerful insights and predictions to fans in real time,” said Pete Samara, director of innovation and digital technology at Formula 1.