Managing network ecosystems for dependable services is a complex task, especially, with multiple interfaces, hardware and equipment. High availability of networks is an integral part of business operations, and drives client experience. Network infrastructure provides the foundational building blocks for companies to meet the growing customer needs specific to access, data, interconnectivity, interoperability solutions, and infrastructure. The recent advances in AI and application of Machine Learning (ML) techniques can rapidly transform network operations from real-time monitoring to real-time prediction. Most of the AI models and platforms are designed to predict incidents few hours before by evaluating the symptoms, event paths and patterns.
AI driven innovations are triggering the next wave of growth, transforming globally distributed networks, including the tools and services required to deliver optimal value. Today, with high operational costs, dipping revenue margins, and demand for exponential growth, there is a veritable need to transform existing network operational models to a dynamic and adaptive on-demand network which can optimize costs, reduce power ingestions, forecast outage points, and self-configure to mitigate any service risks. There is significant increase in efficiencies when a network with cognitive abilities drives governance by identifying incidents across locations, and provides guidance towards the next best available hub to handle the task with no adverse impact on service outcomes. Hence, adopting AI is becoming an enterprise imperative.
Infusing AI to create intelligent networks: changing the core
Managing traffic and resources: Today’s networks are traversing the technology hyper curve. Being heterogeneous and high on traffic, they are leap years ahead of traditional networks which were simple, and had less complex traffic demands. With devices leveraging multiple technologies (Wi-Fi, 3G, 4G, etc.), network operators can bring together cells and radio beams and organize them to meet various user criteria. At various operational levels, multi-variant decision trees have to be applied through policy control tools. The decision tree objective is to drive optimum user experiences during high traffic demands. Two major catalysts in traffic management include Software Defined Networks (SDN) and Network Functions Virtualization (NFV). Both these approaches handle diverse traffic variations, dynamically assign computational resources, enable key decision-making algorithms to adapt to trending traffic conditions and enhance a network’s complex decision making abilities.
Enhancing efficiencies and cutting costs: Managing multiple networks with limited tools can pose operational challenges, since today’s network behaviors are very dynamic and there is a dire need to change the technology paradigms to support the networks. Operators are using cloud based virtualized networks to minimize operating costs while driving automation initiatives across networks. Data analytics is being recognized as a key lever to deliver true value through better network performance models and monitor networks through automated systems.
The AI leverage across the wireless network domain is primarily to solve three key business questions - how to manage voluminous increase in network traffic, how to ensure network runs under optimal capacity and what actions should be taken to iron out network constraints.
The key areas of AI interventions in network design, transformation, and optimization include:
- Self-organizing Networks: The challenge in network design is in managing resource allocation to meet evolving traffic demands. Traditional methods of solving network issues like rule driven system analysis and simulations have their set of challenges due to increased traffic and complex network architectures. ML driven networks can detect network anomalies, and self-learn, organize, adapt themselves based on user devices and traffic demands.
- Network Monitoring and Analysis: Existing ways of troubleshooting networks have their set of limitations. Support personnel rely on knowledge repositories to resolve incidents, and most guidelines have limited abilities to resolve new challenges and have a longer turnaround time.
Diagnostic analytics with machine learning can dig deeper into data and generate insights into existing network issues. This can empower support staff to drive troubleshooting methods with greater accuracy, detect issues, and solve them faster.
- Network Optimization: Manual intervention to optimize networks has limits due to its inefficiencies and costs. There is an increased need for automated, scalable solutions to reduce costs. These solutions allow closed loop optimizations through intelligent agents embedded with domain know-how and search settings, making them a perfect bet to optimally fix network issues.
- Network Planning and Security: Effective network planning is vital to ensure better user experiences and return on investment. Older networks relied on coverage capacities and were easily predictable, whereas today’s high bandwidth network traffic demands ML based predictive modeling tools to improvise network planning, data throughput, wider coverage, and better end user experience. Neural Networks are also used to shorten design, analysis, and development cycles of highly efficient networks. From a security perspective, ML based behavioral analytics can quickly detect breaches and comprehend data relationships deduced from multiple user actions, providing reliable defense at various layers across the network.
- Predictive Maintenance: Armed with the power to transform data into actionable insights, by using machine learning, modeling, data mining and game theory, predictive analytics can slice through past and present data to forecast future events.
- Network Performance Management: With continuous, automated processes, prescriptive analytics can improve accuracy in predictability by synthesizing big data, business rules, and machine learning to predict network performances.
- Failure Mode Analysis: Infers and finds the root cause and factors responsible for key network outcomes, explaining the reasons which contributed to these outcomes through data correlations.