Edge Computing for the Technology Sector

Rather than moving information to a central cloud (whose servers may be located a considerable distance away), edge computing brings compute, storage, and analytics capabilities closer to the devices and locations where data is generated. This approach reduces network latency (the round trip that data must take between its point of origin and destination of final use), and allows devices and systems within an edge cloud zone to react more quickly to changing conditions. Edge computing deployments can therefore provide benefits across all sectors of the economy.

According to Frost & Sullivan, the multi-access edge computing (MEC) sector is estimated to reach $7.23 billion by 2024. This growth extends to all industries, with the organisation observing that edge computing in some form was employed by 90% of enterprises in 2022. In the technology sector, the trend has been toward smarter, cloud-connected edge computing deployments, which enable technology providers to iterate more quickly, and deploy products and services more rapidly.

In this article, we’ll look at the implications of edge computing or edge cloud for technology providers in three key areas: software and systems development, technology deployment, and data analytics.

Edge Computing in Software and Systems Development

The role of edge computing in software and systems development is in some way comparable to that of a Content Delivery Network or CDN, in the general internet. However, unlike a CDN which operates with discrete assets like images, servers and applications, an edge computing service provides a more flexible environment -- one in which developers can run custom code and application logic on the edge cloud’s dedicated servers. Using edge computing infrastructure, developers can also harness the power of Virtual Machines (VMs) more readily, for example with more sustained execution and greater efficiency within a single virtual machine.

In the development arena, edge cloud specifically provides benefits for applications that require low latency, lower connectivity costs, and reliable uninterrupted connectivity. For hardware and systems developers, moving computing to the edge can help to eliminate the need for user devices to perform complex computations. This gives manufacturers greater freedom to produce  cheaper and slimmer devices with longer battery life.

Application and systems development at the edge confers  a number of advantages, including:

• Increased Processing Speeds: With workloads closer to devices and end users, data can travel over shorter distances, and across fewer network junctions.
• Greater Reliability: Less complex network traffic flows reduce the risk of congestion, and the data and performance errors that it can produce.
• Bandwidth Consumption and Cost Reduction: Using edge computing to cache and filter data locally results in smaller amounts of information needing to be transmitted to the public cloud.
• Ability to Scale: Developers can dynamically create workloads, and scale them up or down on demand. This also helps reduce latency and control costs.
• Increased Data Security: Developers can store information on servers at their own premises, or those managed by the edge service provider. Leading edge providers have specialist security infrastructure in place, to mitigate threats.

Overall, edge computing helps developers and technology manufactures run applications and systems which are faster, more reliable, and secure. This leads to better performance, and can ensure an increased Return On Investment (ROI).

Technology Deployment Using Edge Cloud

Mobile communication technologies such as 4G LTE and the emerging 5G networks are facilitating the expansion of edge cloud infrastructure across a range of applications and use cases.

For example, by overlaying real-time instructions on real-world elements via smartphones and smart glasses, Augmented Reality (AR) systems can improve the performance and productivity of people interacting with critical situations, such as in field repair work and remote surgery. Edge computing nodes can take on the work of image recognition and real-time computation to quickly identify objects on the scene and provide information about them. This can allow low-powered AR headsets to perform less intensive tasks such as object tracking, and conserve battery power.

Edge computing can help regulate the flow of sensor data for Internet of Things (IoT) deployments. For example, to minimise bandwidth and selectively manage computations, an edge cloud network could receive data from many sensors, aggregating and processing the information, before sending it on to central servers. The edge network may also act as a buffer if those central servers go down, storing data from the IoT sensors to transmit at a later time.

In smart traffic management and connected vehicle applications, edge computing can also play a role. For example, city authorities could use edge networks to run and continually train an Artificial Intelligence (AI) system for traffic control. Each edge network could respond to the unique movement patterns of each location, to regulate the working of traffic lights, confirm and relay incidents, and collect metrics such as the number of cars passing an intersection. At ground level, this approach could ensure a more pleasurable driving experience for commuters, while reducing the incidence of traffic snarls and accidents. An automated AI system could also assist city planners by reducing their burden of manual work hours, and the risk of human error.

For technology providers in the recreational arena, the network segmentation associated with edge computing allows the selective broadcast of information to many players in the same area -- a feature known as application-level multicast. For example, a central game server might decide that one area of a map grid with hundreds of players should receive a bonus. By sending a single command to the edge node responsible for that grid, the edge node can assume responsibility for relaying the desired information on to each player. This approach enables providers to both fine-tune their output to audiences and make more efficient use of their network infrastructure.

The Role of Edge Computing in Data Analytics

In order to accurately assess prevailing conditions and extract meaningful insights from enterprise data, analysts and data scientists require access to fresh data which reflects the real world as it is at any given moment. This information must be live and ideally real-time, rather than training data.

With its specialist access to IoT sensor output and localised data streams, edge computing can provide data scientists with both training data and real-time performance metrics. Access to real-time information from real-world environments provides data scientists with vital indicators of how their AI models stand up to anomalies and variations that can’t be recreated in labs or test environments. Using this knowledge, they can modify and redeploy their AI models.

Edge computing networks can provide the low latency, real-time flexibility, and responsiveness needed to collect, clean, annotate and ultimately feed data back into AI models for training on a continuous basis. Edge computing also enables the smart extraction of relevant data which can allow this feedback loop to keep running so that data scientists can continually improve and adapt their systems.