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Leveraging AI/ML-driven Autonomous Networks for Next-Generation Enterprises
Mrinal Banerji Product Manager, Network Practice | March 25, 2021
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The state of IT infrastructure has drastically changed with networks becoming a critical enabler of modern, digital enterprises. However, the rapid transformation from data center based design to distributed architecture has not been easy, with numerous challenges in ensuring true optimization. Thankfully, we are at the forefront of the age of automation, where cutting edge innovations in artificial intelligence (AI) and machine learning (ML) are beginning to enable true automation and unleash the full potential of IT for business.

The Network Challenge for 21st Century Enterprises

Networks are the backbone of enterprise operations, connecting decision-makers, customers, and employees with the tools necessary to deliver value. For digital enterprises, these are the fabric that integrate essential business components like applications, IoT and connected devices, data streams, user experience platforms, and security firewalls. But despite the drastic rise in sophisticated software-driven solutions, current networks still face the challenge of managing the escalating complexity between these components.

Moreover, the post-pandemic era has seen an unprecedented growth in distributed workforce and remote operations, with networks having to rapidly adapt to a surge in new business requirements. This has made it more difficult for human operators to manage and leverage enterprise networks to deliver the full potential of their business value.

Driving Real Business Value with AI-Enabled Networks

Autonomous networks allow rapid scalability and operational agility by integrating artificial intelligence/machine learning to existing software-defined methods. This hardware agnostic approach ensures that the network architecture is virtually free from manual intervention in its configuration, maintenance, and monitoring. Moreover, it can enable actionable business insights by effortlessly ingesting vast quantities of data from multiple sources, such as IoT devices and enterprise systems spread across diverse geographies.

Autonomous networks are “self-healing” and ensure better end-user experiences in a rapidly changing landscape

Autonomous networks can leverage data-driven machine reasoning to address critical use-cases such as AI assisted network capacity planning, network provisioning and optimization, dynamic bandwidth and path selection, complex event processing, fault management, and outage prevention. The true differentiator of autonomous networks is their capacity to drive actual cost advantages to the enterprise by enhancing process efficiencies. This is accomplished by saving enterprises thousands of workhours every year, which the IT teams typically spend to ensure consistent operations. Furthermore, it also impacts business operations in a more meaningful way by reducing time spent on error correction, root-causing, and the management of overloaded network routes. These systemic operational efficiencies become critical as companies continue to pursue aggressive growth.

With an exponential surge in users and devices connected to their networks, enterprises need agility to scale as they manage their network bandwidth, capacity, and resources for optimal utilization. They can better manage, predict, and prepare for variable traffic patterns, various use-case deployments, and proactive analysis and corrections. Ultimately, autonomous networks become “self-healing” and can ensure better end-user experience in a rapidly changing technology landscape. In practical terms, this means reduced turnaround times for user requests, improved response rates, and easy adaptation and scalability in transitions to emerging systems built on IoT and 5G.

Autonomous networks possess far greater intelligence about their own systems and can monitor these in real-time, across endpoints. This frees up IT teams to focus more on business-centric product innovations and solutions, rather than tending to IT troubleshooting issues. Apart from saving direct human cost, intelligent networks provide value by predicting and preventing network faults and help organizations avoid costs due to performance issues, capacity overrun, downtime, service penalty, and loss of reputation. Artificial intelligence assisted network capacity planning and optimization helps real-time provisioning of network bandwidth to scale and compute resources based on demand. By automating the demand-capacity cycle and orchestrating dynamic bandwidth and path selection, autonomous networks also facilitate early provisioning of services. Similarly, the virtual network function and associated infrastructure can be upgraded continuously, eliminating the cost of obsolescence. Consequently, with successful deployment across a wide range of user scenarios, an autonomous enterprise network has emerged as a true enabler of “business intent” to deliver real business value.

Intent-Based Autonomous Networks

Next Generation Enterprises
 

With machine learning based design, autonomous networks can deploy a machine reasoning approach predicated on business intent as it relates to network behaviour. This simply means that enterprise networks can now play a proactive role in aligning network behaviour based on business parameters that are directly driven by executive policies. The image above highlights how a high-level strategic intent is achieved by concurrently using network policy frameworks, orchestration, and analytics in a fully closed automation loop. As a first step, the business or strategic intent is translated into network goals. To achieve this, policy frameworks, orchestration and analytics work together. The business intent is mapped to node level requirements and further broken into sub-tasks, which are then addressed by a closed-loop automation. A single closed-loop automation cycle can suffice to address small tasks; for larger tasks, the cycle will comprise multiple smaller iterations and learning cycles.

Autonomous networks, designed with complex event processing capabilities can also detect abnormal operating conditions based on the predefined business intent. In case of suspicious behaviour, it can monitor and adjust the user experience accordingly. Such multi-dimensional assessment allows the network to not only protect the enterprise and the customer, but also investigate the root cause of the issue for long-term resilience. Similarly, this same intent-based networking can rapidly conduct several business-centric activities such as rapid application testing, assurance troubleshooting, generating actionable remedial insights, and much more.

Intelligent networks can harness the reach and analytical power of machine learning to become self-servicing and self-healing. The machine reasoning aspect of autonomous networks is the major leap forward as it allows IT leaders to deliver IT services in real-time across the enterprise with greater agility and responsiveness. In such an implementation, enterprise systems gain the ability to be constantly improving, self-correcting, and a minimal burden on the human workforce. With artificial intelligence and machine learning powered applications, autonomous networks can also radically transform core areas of network management like data center virtualization, provision computing resources, and storage management to create a truly autonomous enterprise of the future.

Preparing for the Future, Today

There is little doubt that global digital enterprises are at an inflection point where the next great leap forward will be based on advanced automation, especially when it comes to managing enterprise systems, remote operations and a distributed workforce. Business leaders from across industries – from healthcare to manufacturing, and from transportation to agriculture – are beginning to leverage the power of networked operation. But to truly leverage the full potential of automation, we must grow our thinking beyond simple artificial intelligence and machine learning and strive toward building an “autonomous enterprise”.

Autonomous networks are the first step in enabling this reality as they can grow to become intelligent, cognitive engines of innovation. When aligned with business intent, these networks are so much more than just operational tools. They can empower business decision making and allow the human workforce to focus on creative innovation and customer relations. But even as this possibility lies within our reach, there is no time to waste in leading this paradigm shift. As recent disruptions have proved, the future we must prepare for is not years or months away but, can be as close as tomorrow. The only question is – will we be ready?

To learn more about how you can leverage the benefits of autonomous networks for your enterprise, reach out to us at Contact.NGN@hcl.com