Every day, the nexus between our digital and physical selves grows closer. This is aided by the ubiquity of our “always-on” connected selves, made possible through our smartphones.
Tech vendors and telcos have been long heralding the future of the 5G network as the savior of IoT-connected devices and mobile connectivity. But global or even local access to 5G networks is far from mainstream, with the uptake of users far from the critical mass needed to gain a real benefit.
While 5G can reduce network latency between endpoints and nearby mobile towers, the distance to data centers remains a pain point — particularly for latency-sensitive applications. For genuine mobile app functionality, we need edge computing to minimize and improve response times.
Mobile App Development Needs Edge Computing
Excessive latency kills the mobile app user experience. Users don’t like sitting and waiting when interacting with an app, and high network latency makes users more likely to abandon an app. The app market is highly competitive, so developers need to create apps with a great user experience in all locations.
While mobile networks are getting faster every day, millions of mobile users worldwide rely on slow devices with low-bandwidth connections. For these users, the complex demands of modern mobile apps and the extended round-trip times communicating with distant servers are a severe bottleneck to mobile performance. Slow devices and poor connectivity can result in unstable links with limited bandwidth and variable latency, making it hard to run an app consistently.
How Edge Computing Can Help
Constantly sending data to the cloud is time-consuming and intensive use of bandwidth. By processing data close to where it originates (at the edge), we avoid the round-trip time to the cloud.
Also, consider that edge architecture is forever evolving. Developing technologies such as progressive web apps create a rich landscape for innovation. Containers, Kubernetes, and lightweight application services provide an opportunity to accelerate application development from cloud to the edge.
Containers are easy to deploy and manage. They’re well suited for the requirements of edge applications in terms of modularity, segregation, and immutability. Additionally, a combination of container and microservices makes it easy to scale to suit our needs.
We can run our application’s backend at the edge using tools like StackPath’s VMs and containers. These can reduce latency and round-trip time by deploying essential services to strategically targeted geographic locations.
We can also use edge computing to cut down the payload size we deliver to end users. When an app calls a backend service, that service often calls several other servers, merges the results, and ends up sending app users much more data than they need. This may not be a significant issue on a fast network, but every byte matters on a slow 3G connection. We can also use edge computing to call other services, then optimize the result to include only what the end user needs.
We don’t need edge computing to pare down response payload size. But, as mentioned above, running at the edge reduces latency — so we get the benefits of both reduced latency and less data going across the wire.
And, by developing mobile apps at the edge, it’s possible to offload some computing tasks from the user’s device to the edge to reduce the impact of poor device performance. The edge is the ideal place for this. If we’re outsourcing some of our app’s functionality to a backend microservice, we want the lowest possible latency to ensure we can receive data to compute and send results with as little latency as possible.
Running a mobile application at the edge ensures functionality even with poor connectivity that results from users being in an underground railway station, rural area, or crowded sports centers, for example. In a situation where a user is watching a video app on an underground train, for example, edge computing processing capabilities are closer to where the data originates and less likely to suffer poor connectivity.
This improves the ability to perform real-time analytics for actionable insights. It minimizes the amount of data sent to the cloud and between sensors, minimizes latency, and reduces time, energy, and bandwidth expenditures.
Use Cases for Running a Mobile Application on the Edge
Processing data at the edge can be essential to the user experience in various industry verticals. Let’s turn to some use cases.
Translation and mapping applications are great examples of scenarios where you want an application to be responsive while traveling. Depending on where in the world you travel to, there’s no guarantee you’ll have access to a high-quality, high-speed mobile data connection. So, edge computing can really help in these connection-necessary scenarios.
While we may be heading for a metaverse, AR and mixed reality in mobile gaming is still huge. Pokémon Go generated over $1 billion in revenue in 2020. But gaming is traditionally resource-intensive, and no one wants to experience lag at a critical moment in a game.
Edge computing ensures a truly real-time and immersive experience that isn’t interrupted by lagging or buffering. This is crucial to user experience.
The ability to process data at the edge is crucial for worksites with poor connectivity, such as inside mammoth warehouses or at job sites in remote locations. In this situation, edge computing can provide the means to deliver real-time operational insights.
This flexibility ensures workers are always aware of all internal operations and changes without altering their current daily routines. In turn, mobile edge solutions can enable workers to share information and insights across the business in real-time.
Health and Safety
Edge computing enables workplaces to access near real-time data from mobile phones to provide monitoring and predictive analytics. Employees can receive alerts on their phones from other employees assisting with workplace compliance.
And, in our current health era, tracking movement makes it possible to see if there are hotspots where people aren’t wearing masks, or identify areas where people get too close to each other, requiring traffic flow changes.
Monitoring Crowd Occupancy
Many cities have deployed location intelligence at subway stations and other transit hubs to track occupancy and adjust schedules to reduce overcrowding. Apps developed at the edge can, in addition to following how crowded these spaces are, also advise passengers which cars are the least crowded in real-time and suggest alternative, safer modes of transportation.
Edge-Based Facial Recognition
Many developers incorporate facial recognition into their apps for use cases such as identity verification (Know Your Customer or Know Your Client) for mobile banking and insurance. Edge computing makes it possible to incorporate facial recognition into apps without the delays of cloud processing or large file transmission.
Not all mobile users have access to high-speed Internet connections all the time. But we can incorporate edge computing to increase the benefits for low-bandwidth users. We can develop new app capabilities, create new business models, and increase user satisfaction by embracing edge computing.
Edge computing is significant for global customers seeking a comparable user experience and customer satisfaction for their end users, regardless of the region they’re in. This is also critical for new businesses seeking entry into large areas where telcos traditionally don’t provide Internet connectivity.
Give your applications and data the speed they need that the centralized cloud can’t deliver. Embrace edge computing. StackPath can help you start and support your journey along the way with customer support, tutorials, blog posts, and other developer resources.
Get in touch with StackPath today to find out more.
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