IoT, 5G and AR/VR have long been the crucial drivers for the increased adoption of edge computing. The market for edge computing in Asia-Pacific (APAC) is estimated to grow at a compound annual growth rate (CAGR) of 21% between 2019 and 2024 to reach $5.8bn in 2024, according to a study by GlobalData. In the COVID era, the increasing demand for high-speed networks to improve productivity is accelerating the adoption of edge computing in APAC at an unprecedented rate. Today we are seeing the increased use of video conferencing and content stream services that require higher bandwidth and zero-latency data transfer. In this world of hyperconnected remote work, the industry standard of five milliseconds is too slow.
Service providers are also experiencing an unprecedented growth in bandwidth demand and networks are constantly under strain due to spikes in traffic. The growth of personal devices has generated large volumes of data that need to be stored and processed to facilitate timely and effective decision making. To address this, business applications and data must move as close to the data ingestion point as possible, reducing the overall round-trip time, and ultimately allowing applications to access information in real-time. And this is much more achievable now.
Overcoming the Challenges with Data-Driven Solutions
For service providers in particular, edge computing comes with unique challenges. The proliferation of solutions at the edge means containers are constantly being deployed faster than humans can manage them. While orchestration tools can be used to automate deployment, observability is key in troubleshooting and assuring service in an automated manner.
While IT already has the information needed to identify the source of the problem and solve it, the challenges arise when sifting through reams of telemetry data spread across server components. The solution lies with AI capabilities, specifically machine learning, which powers the orchestration solutions that deliver predictive and scalable operations across workloads.
Combining machine learning with real-time network monitoring can provide the insights necessary to power automated tools capable of provisioning, instantiating and configuring physical and virtual network functions quicker and more accurately than if a human carried out the task. This process also means IT teams can spend their time on mission-critical, higher value initiatives that contribute to the bottom line.
Bringing AI to the Cloud
Machine learning also has a critical role in application life cycle management at the edge. In an environment that consists of a few centralized data centers, operators can determine the optimal performance conditions of the application’s virtual network functions (VNF). As the environment disaggregates into thousands of small sites, VNFs have more sophisticated needs that must be catered to accordingly.
Because operators don’t have the bandwidth to cope with these needs, machine learning algorithms can run all of the individual components through a pre-production cycle to evaluate how they will behave in a production site, giving operations staff the reassurance that the apps being tested will work the edge.
A Future Consumed by the Edge
As the edge takes off, it is fundamentally changing the way service providers are thinking about their infrastructures. The edge is increasingly viewed as prime beachfront property, often provided by and managed by service providers, to be optimized with AI and machine learning for almost limitless business purposes. And once this high immersive edge computing power is unleashed, we will see applications and new workloads coming to the edge that were simply unimaginable just five years ago.
Looking ahead, it will not just be service providers capitalizing on the edge. Soon, edge cloud environments will be open, secure and cloud-native with predictive and scalable operations that cater to a broad range of enterprise, consumer and telco workloads. Spending on edge computing are predicted to be the highest in the manufacturing, banking, financial services and insurance, IT and consumer verticals, accounting for half of the overall spending in 2024 according to research by Global Data. AI-driven predictive operations will also be leveraged to manage the complexity of operationalizing thousands of edge locations, creating an enhanced consumer and employee experience.