The impact of AI and Machine Learning on service assurance
Today’s operators are undergoing vast digital transformations to help shape their roadmaps for future innovation. That includes transforming existing networks to more virtualised environments and preparing for 5G. The new networks must be more robust and agile and at the same time, able to adapt to whatever the future will bring, Anand Gonuguntla, co-founder and CEO of Centina. Operators must also be prepared to manage a continued trend of software as a service and cloud-based service models.
Assuring quality of existing and future services becomes both more challenging and more critical to these operators as the competition heats up. Customers demand affordable and excellent quality on-demand, ready for anything they want to do—both at the business and residential level.
That makes service assurance in today’s world a challenge. Leveraging the latest advancements in Machine Learning and Artificial Intelligence, will become imperative for today’s operators to continuously assure their networks in a dynamic environment. Choosing the right service assurance solution to adapt to these needs is critical.
Optimising and managing complex networks with Artificial Intelligence and Machine Learning
While traditional service assurance offers a more reactive approach to remediation of network issues, in a hybrid or virtual network environment, service providers can be much more proactive in both network monitoring and optimising performance.
Today’s AI driven service assurance solutions are offering predictive analytics tools, and invaluable business and network intelligence to its users. Spotting problems before they occur saves significant time and resources that both improve customer experience and prevent or reduce expensive down time.
Another important benefit that these kinds of predictive monitoring solutions offer is in SLA compliance and cost savings. Avoiding unnecessary customer credits because of network interruption has tremendous operational savings for service providers.
As 5G approaches and with it promises of ubiquitous connectivity, operators must be prepared to up their investments in service assurance. Ensuring that solutions leverage Artificial Intelligence and Machine Learning is critical. But how does a provider know what to look for?
Here is a list of AI and ML features that today’s best service assurance solutions should offer:
- Performance-based anomaly detection
The ability to collect and analyse performance data over long periods of time to learn what’s normal for the network and alert when network or service performance trends from past norms.
- Alarm and event-based anomaly detection and resolution
The ability to learn from event and alarm patterns and resolutions to automatically correlate network events together and pinpoint the root-cause of network and service outages. Machine Learning algorithms could then use knowledge bases to suggest or automate resolutions.
- Automated optimisation and remediation
After detecting network issues, the ability to automatically re-configure the network to optimise deteriorating performance or re-route services due to failures – either directly to network devices or through orchestration systems, controllers and Element Management Systems.
The author of this blog is Anand Gonuguntla, co-founder and CEO of Centina
About the author
With over 20 years’ experience in the telecom industry, Anand co-founded Centina. As CEO, Anand oversees all strategic planning and execution of the company’s corporate, sales and product initiatives. Under Anand’s leadership, in just 10 years, the company underwent global expansion and has been recognised by leading analyst firms such as Gartner and Frost & Sullivan. The company was also ranked by Deloitte as one of the fastest growing companies in America and has achieved 314% growth from 2009 through 2013. These accolades are a validation of Centina’s enterprising spirit and it’s commitment to it’s core values.
Prior to Centina Systems, Anand held leadership positions at Xtera Communications and Fujitsu Network Communications. Anand also holds patents in the area of network management and is well published in the communications industry. He received his master’s degree in Electrical Engineering from the University of North Dakota and a bachelor’s degree in Electronics and Communications Engineering from Jawaharlal Nehru Technological University, India.