August 17, 2021
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, presenting numerous challenges toward achieving true optimization.
We, at DRYiCE Software, are at the forefront of automation and cutting-edge innovations in artificial intelligence and machine learning. DRYiCE enables true automation and helps unleash the full potential of IT for businesses.
The Network Challenge for ‘Digital-First' Enterprises
Networks are the backbone of enterprise operations, connecting decision-makers, customers, and employees with the tools necessary to deliver value. For enterprises, they are the fabric that integrates essential business components, such as applications, IoT and connected devices, data streams, user experience platforms, and security firewalls. However, despite the drastic rise in software-driven solutions, current networks still face the challenge of managing the escalating complexity between these components.
The post-pandemic era has seen unprecedented growth in the distributed workforce and remote operations, with networks having to adapt rapidly to a surge in new business requirements. This has made it more difficult for human operators to effectively perform network management and leverage enterprise networks to deliver the full potential of their business value.
Driving Real Business Value with AI-enabled Networks
The “new normal” has imposed an exponential surge in users and devices connected to enterprise networks, creating a need for agility to scale network bandwidth, capacity, and resources for optimal utilization. Enterprises must adequately prepare themselves to predict and manage variable traffic patterns, various use-case deployments, and proactive analysis and corrections.
Autonomous networks fulfill these requirements as they allow rapid scalability and operational agility by integrating artificial intelligence or machine learning into 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 enables actionable business insights by effortlessly ingesting vast quantities of data from multiple sources, such as IoT devices spread across diverse geographies.
Autonomous networks can leverage data-driven machine reasoning to address critical use-cases, such as AI-assisted network capacity planning, network provisioning and optimization, and complex event processing. Additionally, they also assist in dynamic bandwidth and path selection, fault management, and outage prevention.
The true differentiator of autonomous networks is their capacity to drive actual cost advantages by enhancing process efficiencies. This saves enterprises thousands of work-hours every year, which the IT teams typically spend to ensure consistent operations. Furthermore, it also impacts business operations in a 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.
Autonomous networks, backed by artificial intelligence and machine learning, eventually become self-healing and provide better end-user experiences in a rapidly changing technology landscape. This leads to reduced turnaround times for user requests, improved response rates, and easy adaptation and scalability in transitions to emerging systems built on IoT and 5G. These networks possess greater intelligence about their systems and can monitor them in real-time across end-points. This enables the IT teams to focus on business-centric product innovations and solutions rather than tending to IT troubleshooting issues.
Apart from saving direct human costs, intelligent networks can predict and prevent 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 can help 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. 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
With ML-based design, autonomous networks can deploy a machine reasoning approach predicated on business intent related to network behavior. So, enterprise networks can now play a proactive role in aligning network behavior based on business parameters 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 hand-in-hand. The business intent is mapped to node-level requirements and further broken into sub-tasks, then addressed by closed-loop automation. A single closed-loop automation cycle can sufficiently address small tasks; for larger tasks, the cycle will comprise multiple smaller iterations and learning cycles.
Autonomous networks with complex event processing capabilities can also detect abnormal operating conditions based on the pre-defined business intent. In case of suspicious behavior, it can monitor and adjust the user experience accordingly. Such multi-dimensional assessment allows the network to protect the enterprise and the customer and investigate the root cause for long-term resilience. Similarly, this 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 a 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 constantly improve, self-correct, and pose a minimal burden on the workforce.
With AI- and ML-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 for the future.
Preparing for the Future Today
There is little doubt that global digital enterprises are at an inflection point, and the next great leap forward will be based on advanced automation. Business leaders from across industries– from healthcare to manufacturing and transportation to agriculture– are beginning to leverage the power of networked operations. But to truly harness the full potential of automation, we have to step beyond simple applications of AI and ML and strive toward building an autonomous enterprise.
Autonomous networks are the first step in enabling this reality as they develop into intelligent, cognitive engines of innovation. When aligned with business intent, these networks are more than just operational tools. They can empower business decision-making and allow the 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 proven, the future we must prepare for can be as near as tomorrow. The only question is– will we be ready?
Mrinal Banerji is a part of the DRYiCE Product Management Group. He is responsible for formulating product roll-out strategies, GTM plan, and new alliances. He has close to 10 years of experience into strategy consulting, counterparty due diligence, competitive and customer intelligence, sales enablement, and business intelligence for global technology clients. Prior to HCL, Mrinal has worked with Evalueserve, KPMG, Ericsson, and IBM in various capacities.