Autonomous Vehicle Cloud Platforms
Are you ready to ride in an autonomous vehicle? The world has a way to go before people fully trust self-driving cars and their use reaches critical mass. However, the global autonomous vehicle market continues to expand rapidly. Market analysts projected it to grow from $1.9 trillion in 2023 to $13.6 trillion by 2030, an average annual growth of 32.3%.
As the industry grows and data volumes balloon, the ability of autonomous vehicles to perform efficiently and safely hinges on the capacity of public cloud platforms. Whether controlling a handful or hundreds of cars all at once, the systems directing cars will need to tap into a scalable, on-demand IT infrastructure.
Massive Amounts of Data to Process
Ideally, that infrastructure is geographically dispersed across regional data centers to deliver compute power to the edge—as close as possible to the vehicles. Autonomous vehicles also require High-Performance Computing resources to process, store, and analyze massive amounts of data. Research from Lucid Motors (via Visual Capitalist) suggests modern software-defined vehicles produce anywhere between 1.4 and 19 terabytes of data an hour. That’s an increase of over 5,000% in just 10 years.
With this exponential increase in the amount of data, storing and managing it presents a formidable challenge. The data also needs significant processing power for real-time analysis. For these requirements, cloud infrastructures must offer speed, customization, reduced costs, and efficient processing.
How Public Cloud Providers Solve the Challenges
Public cloud providers have the answer to all these challenges. They allow self-driving vehicle management companies to offload, process, store, and access data from anywhere 7/24. Providers with High-Performance Computing capabilities can also enable machine learning and other artificial intelligence tools.
These technologies streamline information exchanges between traffic and vehicle data and permit autonomous vehicles to communicate and learn from each other. End-to-end cloud platforms also enable fleet management companies to develop, train, deploy, and improve AI algorithms and models continuously. This will increase passenger safety and improve driving experiences.
In addition, the cloud facilitates autonomous vehicle software enhancements. Over-the-air updates can take place constantly to improve functionality, resolve bugs, and adapt to changing road conditions.
Vehicle fleet managers also must consider security. Breaches could have dire repercussions. Public clouds solve this challenge, too, with advanced encryption and security protocols to protect sensitive information. They staff their data centers with dedicated security analysts to monitor and mitigate threats quickly.
Disaster recovery is vital for every business, but perhaps more so for the autonomous vehicle industry. Customers who get stuck in the middle of nowhere because a server goes down won’t be customers for long. The leading public cloud providers offer automatic server failover within their data centers as well as the ability to switch to a data center in another region should a natural disaster strike.
A Hybrid Option to Public Clouds
Besides public cloud providers, fleet management companies with autonomous vehicles also can turn to colocation data center providers that offer bare metal solutions. This hybrid hosting option combines colocation and public cloud infrastructure to deliver compute horsepower and the freedom to choose any operating system, application, and data management tools.
Bare metal environments enable business outcomes that are unique to the autonomous vehicle industry. Customers also can implement their preferred application containers, software architectures, end-user productivity platforms, databases, front-ends, back-ends, analytics, security controls, and automation.
To Learn More
Many companies unintentionally create a hybrid IT infrastructure that could add complexity, cost, and risk. However, with an intentional, proactive, and well-defined hybrid IT strategy—that includes on-premises, colocation, private, and public cloud options—your company can achieve the agility, performance, and security you require.
To assess your readiness for a hybrid environment that combines the public cloud and colocation capabilities, check out Hybrid IT Infrastructure: Proactive Planning and the Right Provider Make All the Difference. To learn more about hybrid public cloud solutions for autonomous vehicle technology, contact DataBank today.
How do data centers guard against natural disasters?
Imagine the shaking of an earthquake, a furious storm, or a quickly growing fire. Hopefully, you built your facilities to withstand such natural disasters.
Have you also considered the resiliency of your data center? At a minimum, your provider backs up your IT infrastructure and your critical data. Your provider also deploys failover capabilities to other regional data centers to remain operational when disasters strike.
However, sophisticated data centers go far beyond backup and failover capabilities. For example, when planning sites and building facilities, the leading providers both consider how climate may impact their operations as well as how their operations may impact the climate.
This is a vital perspective: There’s a close link between climate change and natural disasters that data centers consider in their siting and operations.
In this blog, we examine the measures data center providers take to protect the IT infrastructure and the data of their customers. We also present specific programs DataBank has implemented to safeguard its data centers against natural disasters.
The Keys to Holding Up Against Severe Weather
Data center providers implement a range of methods to assure their facilities can withstand severe weather conditions and climate change-related events:
- Built-to-Last Structures—Strong building materials like reinforced steel and concrete to survive earthquakes and strong winds.
- Strategic Locations—This includes siting data centers in areas less prone to natural disasters and using elevated platforms and advanced drainage systems to protect against floods.
- Smart Monitoring—Sensors provide real-time warnings about environmental conditions. These include seismic sensors to detect earthquakes and climate monitoring to track temperature, humidity, and other factors that impact conditions inside the data center.
- Backup Systems for Power and Cooling—To protect against the power outages that commonly occur during natural disasters. Providers proactively deploy backup generators, uninterruptible power supplies, and redundant cooling systems.
- Disaster Recovery Plans—Providers document all the steps for various disaster recovery scenarios. These include emergency protocols that guide staff on what to do in different types of emergencies. Providers also conduct regular drills to ensure they can act quickly during an actual disaster.
Continuity plans and recovery technology also require the knowledge of disaster experts. The leading data centers employ highly-trained professionals who specialize in emergency response. They act fast and coordinate responses with local disaster response teams.
DataBank Commits to Resilience
Resiliency against natural disasters has long been a cornerstone of data center operations at DataBank. We include resilience in our Environmental program and integrate resiliency throughout our design standards and operations. This oversight helps us maintain 100% uptime at our facilities during instances of electrical grid interruptions.
Our resilience strategy prepares our data centers for unexpected natural disasters. Here are some of the key tactics we employ:
- Siting facilities outside 500-year flood plains, significant earthquake zones, and recurrent natural disaster areas
- Following site frameworks that include climate risk considerations
- Assessing the physical stresses on facilities during data center siting and construction processes
- Designing buildings to accommodate temperatures of at least 115 degrees Fahrenheit
- Reinforcing facility structures to protect against earthquakes, hurricanes, tornados, and high winds
- Incorporating a minimum N+1 redundancy in power and cooling systems
- Conforming to building standards to withstand heat, floods, and fires
- Implementing physical adaptation measures to increase site resilience
We also engage third-party suppliers of building materials and technology services to increase value chain resilience. Along with climate risk assessments, we address the risks posed by climate change at each of our facilities. Each facility also has specific emergency action plans tailored to the attributes of the building and the local climate.
A Framework to Disclose Climate Risks and Opportunities
As part of the DataBank commitment to resilience to protect customer infrastructures from natural disasters, we produced reporting in line with the Task Force on Climate-Related Financial Disclosures (TCFD), which is now a part of International Financial Reporting Standards. In this report, we tied risks and opportunities to financial impacts to integrate risks into our enterprise risk management program.
TCFD’s framework helps data centers and other entities identify climate-related risks and opportunities and align with strategic planning. Examples of initiatives that emerged from our focus on TCFD were climate-related siting considerations for new construction and preparedness planning.
Maintaining Operations During Historic Texas Storm
When severe weather affected Texas in the winter of 2021, all eight DataBank Texas facilities—Dallas, Austin, and Waco—operated successfully throughout and after the storm. Using utility power with generator backups in reserve, we experienced no outages and no customers experienced negative effects. During this fluid situation, we also maintained ongoing communications with customers whenever power systems and fuel levels changed.
Although news stations reported delayed fuel deliveries, we saw no interruptions in fuel delivery because we pre-emptively arranged for deliveries before there were bottlenecks. Weather forecasts told us this storm was coming, so we topped off our fuel beforehand and arranged for deliveries in advance. We kept monitoring fuel use in real time and diverted deliveries among our Texas data centers based on usage. We also sourced backup fuel from out-of-state providers.
Our emergency response plan also included equipping all facilities with sufficient food and water supply and providing nearby hotel accommodations for on-site staff—if next-shift personnel were unable to travel safely to the data centers.
Safeguarding Data That Powers the World
The proliferation of disaster-resistant data centers throughout the world demonstrates the basic need for stability in unpredictable environments. From strengthened architectures to advanced business continuity technologies, sustainable energy, and climate impact, data centers represent a holistic approach to resilience. Safeguarding our digital lives makes sure all that important data that powers our world is safe and secure—even in the face of the most powerful natural disasters.
Data centers supporting HPC and machine learning
Your developers created machine learning models to identify patterns and trends in content and data. This will enable your management team to tap into unseen intelligence to make decisions on how to run your company and serve customers more efficiently. You’re ready to leverage artificial intelligence to the hilt.
Of course, those models also need sufficient compute resources to crunch through your large data volumes and content repositories. Only then can the complex algorithms accelerate model training and inference. That’s where High-Performance Computing (HPC) comes into play.
In this blog, we examine why machine learning models demand HPC resources. We also explore how HPC resources depend heavily on colocation data centers. HPC demands high-density racks that deliver sufficient power and cooling to keep server clusters running optimally—so the machine learning models can continuously analyze data and improve their outputs.
The Complexity of Machine Learning Models
Machine learning models use complex processes, methodologies, and frameworks to govern how applications learn from data and predict outcomes. These include training algorithms to recognize patterns and process information—without explicit programming.
The models also rely on preprocessed data as an interplay among various learning techniques:
- Supervised (regression and classification)
- Unsupervised (clustering and dimensionality reduction)
- Reinforcement (rewards by trial and error)
Each of these adds layers of complexity. Developers then select an appropriate machine learning model. The algorithm learns from training the data and adjusts internal parameters based on the input features and the desired outputs. During this step, any adjustments must occur iteratively to train the data for that model.
HPC Provides the Firepower to Support Machine Learning
Machine learning models require the increased processing power of HPC to handle their multi-layered architectures, rigorous training requirements, and inference processes. HPC supercomputers with parallel processing techniques efficiently handle these tasks that would otherwise take an impractically long amount of time on standard computing systems.
One of the key drivers for the use of HPC to run machine learning models is the immense volume and variety of data that modern models process. Big data causes an explosion of information, with enterprises creating petabytes daily. Data also comes in various forms—ranging from structured databases and tables to unstructured text from social media and blogs and semi-structured files.
HPC takes on this challenge by employing powerful compute resources (such as Nvidia H100 processors) advanced software, and optimized architectures to enable significant processing speed and performance gains. When applied to machine learning, HPC can process vast quantities of data lightning-fast. This enables more accurate models that produce insights previously unattainable from the data.
The Required Infrastructure for HPC
For companies that use HPC to drive their machine learning models, deploying the right infrastructure proves just as vital. The leading colocation data center providers deliver ideal infrastructures for HPC environments.
Here are some of the key attributes:
- Availability: Reliable and redundant power and cooling systems as well as backup generators to make sure computing systems run at all times.
- High-Speed Connectivity: With multiple carrier networks and nearby network hubs, customers can choose interconnections that provide low-latency connectivity for machine learning models. This speeds up handling the large data volumes transferred among computing systems.
- Scalability: A range of rack and server configurations, customizable power and cooling options, and on-demand scaling allow HPC systems to quickly adapt. This allows machine learning models to meet changing business requirements—without costly and time-consuming infrastructure upgrades.
Another key benefit colocation data centers provide is reduced costs. Customers can outsource the management of their computing infrastructure to avoid the capital expense of building and maintaining a data center that supports HPC and machine learning.
Experience with Supercomputing Environments
If your enterprise needs help deploying an HPC environment to support applications with machine learning models, DataBank is ready to help. We deliver high-density colocation HPC solutions in supercomputing environments such as Georgia Tech and the University of Maryland. We also support organizations in the financial services, cloud, software, and AI sectors.
We can help you determine exactly what you need from your data center. Our colocation facilities also take advantage of our Universal Data Hall Design framework—built on flexibility to accommodate deployments of varying sizes, layouts, and technology.
What Is Network Function Virtualization?
Research shows just how much Network Function Virtualization (NFV) transforms data center connectivity. In a 2024 report, Technavio forecasts the NFV market size will increase by more than $90 billion and grow 31% annually between 2023 and 2028.
What drives this growth? Primarily the widespread expansion of wireless networks to support social networks and mobile applications. Another key contributor is the demand for data centers to support exploding network data volumes.
How Does Network Function Virtualization Work?
NFV architectures streamline deploying and managing networks by applying virtualization technologies to network nodes. This allows system administrators to implement network functions and processes in application containers running on virtual machines. The machines can work as high-volume switches, routers, gateways, firewalls, and other devices. This eliminates the need to purchase custom appliances.
Using NFV, colocation data center providers enable virtual session border controllers to protect customer networks. This reduces the cost and complexity of installing physical network protection units. Providers take a similar approach with load balancers, intrusion detection devices, and wide-area-network accelerators.
NFV compares to Software-Defined Networking (SDN), which also uses virtualization to manage network infrastructures. However, where SDN virtualizes hardware, NFV virtualizes the services—replacing devices with functions that run on high-performance x86 servers. This approach decouples the network functions from the appliances and facilitates deploying interconnect services on-demand.
Benefits of Network Functions Virtualization
NFV delivers many benefits while supporting interoperability among multi-cloud and hybrid IT environments that include on-premises and private cloud infrastructures:
- Cost Savings—reduces hardware costs by eliminating dedicated proprietary hardware appliances. With standard servers, data centers lower procurement and maintenance costs.
- Efficiency—enables higher server utilization rates with multiple virtualized functions running on the same physical server, maximizing resource efficiency.
- Scalability—scales network functions on-demand. If traffic spikes, the architecture automatically deploys additional virtual instances without the need to add hardware.
- Optimized Resources—allocates resources dynamically based on real-time demand, ensuring optimal performance without over-provisioning.
- Faster Roll-Outs—allows enterprises to launch new services within minutes compared to traditional networking methods that rely on new hardware installations that can take weeks.
- Reliable Service Quality—improves networking performance through efficient load balancing and intelligent traffic steering.
- Resilience—replicates and distributes network functions distributed across servers to ensure redundancy and fault tolerance.
- Enhanced Security—updates security functions quickly to respond to new threats and minimize vulnerabilities. Also allows for targeted security implementations to enable the configurating of network functions based on specific security needs.
- Automation and Orchestration—enables automation of network services management, helping organizations to reduce operational complexities and manage networks more efficiently.
NFV frameworks also include orchestration tools that automate deployments. This approach produces consistent network operations while decreasing the likelihood of human error. You can also ensure services adhere to service-level agreements
Options for Deploying NFV
Within DataBank colocation data centers, customers can set up NFV architectures to support their servers and storage devices. Customers also have the option of setting up NFV using the DataBank METAL™ bare-metal-as-a-service model. DataBank preconfigures and deploys physical bare metal servers that customers can select, access remotely, and begin using immediately to configure NFV architectures.
With DataBank METAL, your company can capitalize on the NFV paradigm shift to streamline how you design, deploy, and manage networks. By leveraging the benefits—cost savings, scalability, faster deployment, improved service quality, automation, stronger security, and support for hybrid environments—you can enhance your operational efficiency and future-proof your networks.
Frequently Asked Questions