Edge computing is growing rapidly, driven by the need for low latency of many IoT applications, but Paessler vice president for Asia Pacific, Sebastian Krueger says complex edge computing will require intelligent, predictive analytics to ensure optimal performance.
In Australia, Telstra and Amazon Web Services (AWS) have recently joined forces on edge computing in order to take more Australian businesses to the cloud. AWS’s edge compute solutions provide the ability to deploy applications closer to end customers, which increases their resilience and performance, delivering a faster and more seamless user experience.
Telstra’s 5G network enables application traffic from 5G devices to reach cloud services running in edge compute locations without leaving the Telstra network. This prevents the latency that would normally result from additional jumps around the internet and means customers in industries that require low-latency and high resilience, including media and entertainment, manufacturing, healthcare and gaming can take full advantage of the low-latency and high-bandwidth benefits, delivering enhanced speeds and increased flexibility.
Edge computing involves interacting with the real world. Devices like sensors, smart home appliances and smart vehicles all take in data from the external world as well as process it. They further receive commands from a central cloud hub and act on it in the real world. These devices need monitoring 24/7. Beneath the devices, there is a complex mini-cloud, complete with its own infrastructure, storage and networking. It is essential for this layer to be monitored for the edge devices to function optimally.
Edge computing needs predictive analytics
This means edge networks will require the latest in intelligent predictive analytics that can securely capture a huge amount of unstructured network and application data to guarantee network, application and infrastructure performance. This will ensure edge network resources such as computing, database and storage capacity are readily available and perform at their optimum level with low latency.
Governments began using predictive analytics back in the 1940s when early computers came into use. Since then, it has evolved to be the powerful tool it is today. The development of new technologies and the digital transformations of many industries has seen more data become available for analysis, enabling companies employing predictive analytics to increase profits and stay ahead of competitors
Predictive analytics enhances business functions and network operations as it mines information from historical data and reviews this information to establish future trends. Such analysis employs statistical techniques including data modelling, data mining, artificial intelligence and machine learning to making predictions.
Gaining an edge by analysing the edge
Typically, an organisation will use predictive analytics to understand customers and products, or to identify potential risks for the company. When combined with edge computing, predictive analytics becomes a virtual safety network that can be used to ensure everything within a business, department or service is able to operate smoothly and without disruption.
Organisations reliant on edge computing and unable to leverage the application of predicative analytics to their edge computing environment will suffer consequences: think of the challenges created when a banking network or an infrastructure network is unexpectedly knocked offline.
The pandemic has increased the need for flexibility and reduced costs, which is driving the adoption of cloud applications and seeing more workloads move to public clouds. Those organisations that have clarity and control across their entire IT infrastructure — including core, hybrid, data centres and the cloud — will be able to make faster and more informed business decisions if they have full visibility right to the user edge.
