In a world where everyone expects instant results, carriers need to think about how to deploy infrastructure that can handle near real-time workloads. AI workloads are being moved closer to customers, as carriers shift from centralized computing power to the network edge. Consumers and businesses expect immediate access to nearly endless amounts of computing power to be readily available to support their daily activities, and operators need to deliver this as a sustainable business.

The challenge for operators is deploying infrastructure quickly and cost effectively and using technologies which maximize the utilization (and return on investment) of their AI processing nodes. By placing workloads at the edge, operators don’t need to worry about congesting the links between the cell sites and the rest of the network. Edge networks also offer improved security by reducing the number of network elements that have access to the data supplied by end users.

While not all applications need real-time performance, the number of use cases for interactive AI are constantly increasing. Consider use cases like augmented reality where edge compute nodes process and augment video from your camera to help you make decisions based on what you see around you. Here are other examples that will drive the adoption of real-time edge computing for AI:

• Healthcare – medical equipment such as robotics used in surgeries rely on real-time data collected from robotic sensors (position, force, 3D images and other visual feedback) to aid surgeons during an operation.
• Autonomous vehicles – data collected from sensors that can identify objects, their position, distance, and trajectory determine the vehicle’s actions such as applying brakes, accelerating, making turns or stopping.
• Financial transactions – detecting/preventing fraud during an attempted financial transaction.
• Network Optimization – based on network bandwidth, deciding how to route customer traffic in real time.
• IoT Device Monitoring – As part of industry 4.0, many corporations will rely on automated monitoring and decision making to predict and prevent problems before they occur.
• Smart Cities – Using AI to improve security, traffic flow on streets, and to promote the efficient use of resources by sending them where they will have the greatest impact on citizens.

While not an exhaustive list by far, these examples illustrate how real-time data can enhance our lives. It also illustrates how we rely on real-time data and how latency can be detrimental. Any Internet of Things (IoT) device that is used in critical decision making is also impacted.

Solving Network Latency – The Mobile Edge Data Center
The applications described above require a lot of high-performance computing power to process the data needed to make decisions. Using traditional data centers means that the data can actually live in data centers thousands of miles away. Large data center companies have attempted to minimize this by having data centers in various locations around the world.

The answer lies in having the data processed as close to where it is being used as possible. This is known as Edge Computing where time-sensitive data is processed at the point of origin located in close geographical proximity to the device that needs it with as little latency as possible.

Many times, the Edge computing data center is an intermediary data center handling sensitive and urgent information while a connection to a traditional center handles less urgent data processing and analysis.

The Modular Data Center as an Edge Computing Solution
Modular data centers are used by businesses for a variety of reasons. They have also emerged as a perfect solution for applications that need Edge Computing for many reasons.

1. Reduced Latency – Because modular data centers can be placed virtually anywhere, they can be near to the data source which means it greatly minimizes the time it takes for data to travel. The result means faster response times in those critical situations.
2. Flexibility & Scalability – In businesses that need to react to changing consumer buying patterns and fluctuating demand, modular data centers can be moved to different locations. Adding/removing computing power is relatively easy compared to traditional data centers.
3. Reduced bandwidth – By processing data locally, the amount of data traveling over the internet or private networks is reduced which optimizes the business’ bandwidth usage.
4. Reduced costs – Modular data centers deployed locally can be more cost-effective than transmitting large volumes of data from a distant data center.

Data Security and Privacy
No matter what type of business you have, ensuring that your company data and that of your customers is secure and private is paramount. If your business is processing sensitive information in real-time, having data travel from several data center nodes to another is a risk that can result in significant financial fines and loss of reputation.

In addition to solving latency, modular data centers used as Edge architecture minimizes the data exposure as the data is kept locally in a data center owned and secured by you. Modern modular data centers (MDCs), like those from ECOBLOX, have built-in security including access control, video monitoring, alarm systems and automatic notifications.

Modular data centers are used in a variety of environments today by businesses who need high performance computing for their AI-led apps or any other type of application that needs to process large volumes of data. However, as consumers and businesses around the world rely on real-time data processing and analysis, those that are mission-critical must have data center access that is local, secure and affordable. Modular data centers are inherently the best option.