The Telecom industry has touted the level of mobile product enablement that will come with 5G. Low latency, fast data speeds and, highly reliable mobile connectivity will open the door to applications from enterprise-grade fixed wireless connectivity, augmented reality to autonomous cars, and even remote surgery. More importantly, 5G is about the cloudification of the telco infrastructure and opening the Edge to innovation and developer communities. All these applications have one key hurdle in common: Will wireless connectivity provide the level of reliability to support these performance-sensitive applications?
We go about our daily routines using products where our trust in their reliability means that we are betting our lives on them. Planes, cars, surgical equipment (etc.), the way they’ve arrived at this level of trust is rigorous testing. Producers of these products and services have perfected testing in two ways: first, the ability to simulate the real-life application of the product. Second, the use of extensive testing platforms that allow for replicating and testing every scenario.
Telecom testing processes today are, unfortunately, not at that “bet-my-life” level. Labs are a limited replica of production. Test validation processes are highly manual, costly, and slow. Network slicing promises to address the former to some degree. However, the processes that achieve full automation of network functions and service orchestration while maintaining true network resource isolation have yet to attain enough maturity to address the challenge in the short term.
But there is hope. The technology already exists to solve for much of this. The first step is the creation of production service replication in test-beds automatically, on-demand, including decommissioning once the service is verified. This, first and foremost, is dependent on CI/CD pipelines that not only automate DevOps processes but integrate detailed testing pipelines through, what we call, Continuous Testing and Continuous Validation (CT/CV) pipelines. The CT/CV pipeline is an end-to-end test automation framework that includes: classifying the service call flow patterns through implementing machine learning, closing the loop on test execution, and allowing for an exponential increase in the level of detectable call flow variations that is virtually independent of any human intervention.
What we know from the millions of call-flows we have processed, is that lab and production traffic can be similar, meaning that the resulting insights are highly portable. By creating the right test batteries, combined with the automated network replication processes mentioned earlier, including CT/CV, our pre-production certification process not only produced much more reliable network functions and services but our Machine Learning (ML) models trained to recognize call flows in pre-production were reused in production.
The results of the business are impressive. Applying preventive measures before production rollout means that the quality of the software is at a much higher level than before, reducing the number of consumers impacting events down the road.
If we plan to open up our networks to innovations from third-party developers and performance-sensitive applications, we would have to begin our journey towards uncompromised quality before services hit the consumers. Consumers cannot be guinea pigs; we have machines for that.