The demand for higher data speeds, capacity and coverage is adding layers of complexity to network design and management. Modern networks span various devices, locations and environments, making manual management a cumbersome task. The proliferation of next-generation technologies such as 5G, internet of things and edge computing has further intensified the complexity of network management. Traditional manual methods are proving to be inadequate in handling the scale and dynamic nature of these technologies, necessitating network automation to ensure seamless operations.
Network automation refers to the process of using software and technology to automate the management, provisioning, configuration and operation of network devices and services. Instead of manually configuring and managing each network device, automation enables tasks to be performed automatically, reducing the need for human intervention in tasks such as device provisioning, configuring changes, software updates, network monitoring and troubleshooting. By implementing network automation, organisations can achieve increased operational efficiency, reduced human errors, quicker response times and enhanced overall network reliability.
Traditionally, network automation has been achieved through the implementation of software-defined networking (SDN) technology. SDN separates the control plane from the data plane, allowing for centralised network management and configuration. This simplifies network administration and reduces the need for manual device configuration. Further, SDN enables quick provisioning and deployment of network services and resources through automated configuration changes and abstracts network functionality from hardware, making it easier to manage and configure networks without being tied to specific hardware vendors.
The conventional methods of network management and maintenance are rapidly evolving with the advent of artificial intelligence (AI). Telecom providers are now increasingly considering the adoption of AI-based solutions to automate their networks. This shift aims to leverage AI’s capabilities in data processing, real-time adaptation, issue prediction and network operation optimisation, giving operators greater flexibility and control over network management, and improved consumer response times.
Advantages of AI in network automation
AI facilitates network management by enabling predictive maintenance and optimisation strategies that surpass the capabilities of conventional network automation methods. AI-driven solutions employ machine learning (ML) algorithms to analyse historical data and identify patterns that indicate potential failures. By enabling enterprises to proactively address issues before they escalate, AI-powered networks can minimise downtime, enhance reliability and optimise resource utilisation. Further, AI algorithms can dynamically adjust routing, bandwidth allocation and load balancing to ensure optimal performance based on real-time traffic patterns. This leads to enhanced user experiences and efficient resource allocation. By automating routine tasks, AI reduces the need for manual labour and frees up skilled IT personnel to focus on more strategic initiatives, thereby yielding significant cost savings over time.
Moreover, AI-powered systems can understand and process human language, enabling network administrators to interact with the systems using natural language commands. This simplifies the management of complex network configurations. Further, AI systems can continuously learn and improve their decision-making capabilities over time as they encounter new data and scenarios.
AI can also facilitate the adoption of zero-touch provisioning (ZTP), an approach that enables devices to be automatically configured and provisioned with minimal manual intervention. ZTP is particularly useful in scenarios like remote branches, where deploying new equipment is logistically challenging. With its intelligent capabilities in tasks such as device identification, configuration management, and security policy enforcement, AI assumes a critical role in enabling ZTP. AI can autonomously manage the initial set-up and configuration of devices, ensuring a seamless and efficient deployment process without the need for manual intervention.
Further, AI-based algorithms can help strengthen operators’ defence against cyber threats by swiftly analysing vast amounts of network data and detecting patterns that indicate anomalies deviating from normal network behaviour. Further, AI-powered intrusion detection systems can identify and prevent unauthorised access attempts and malicious activities by monitoring network traffic and recognising the characteristics of known attacks. AI can also enhance user authentication methods by analysing various factors such as typing patterns, location and device usage, making authentication processes more secure and seamless.
Market uptake
Given the advantages of network automation, telecom operators worldwide are intensifying their efforts to shift from the manual management of networks. According to industry estimates, the global network automation market was worth $3,615 million in 2022 and is expected to grow at a compound annual growth rate of 21.9 per cent during 2023-33. The integration of algorithms based on AI and ML is further enhancing operators’ ability to build networks that are characterised by an even greater level of intelligence and automation.
Indian telecom operators and technology vendors are also beginning to embrace the idea of adopting AI-powered solutions, aiming to establish networks that are both intelligent and automated. For instance, Reliance Jio Infocomm Limited (RJIL) has collaborated with Guavus to leverage the latter’s AI-driven solutions in enhancing real-time customer experiences and extracting essential marketing insights. Meanwhile, Bharti Airtel has extended its partnership with Ericsson to modernise its network and enable automation. Under this collaboration, Ericsson will deploy the latest automation, ML and AI technologies to enhance Airtel’s mobile network performance and customer experiences. Airtel has also deployed Avanseus’s predictive maintenance solution across its operations in a bid to gain actionable operational insights. Further, Vodafone Idea Limited has chosen Cisco’s network automation systems to enhance user experience and expedite the introduction of services on its 4G and upcoming 5G networks. Additionally, it has automated its IT infrastructure and operations by adopting Red Hat Ansible’s automation platform. Meanwhile, Bharat Sanchar Nigam Limited is collaborating with Nokia for industrial automation solutions. Moreover, domestic technology vendor Tech Mahindra has implemented its intelligent network automation platform, netOps.ai, for Germany-based operator Telefonica Germany.
Issues and challenges
While the potential benefits of network automation and AI are substantial, there are several challenges that must be addressed to ensure their seamless integration and effective functioning. For one, setting up AI infrastructure, acquiring data, and training models require a significant upfront investment in technology, expertise and resources. Further, the coexistence of legacy systems with modern automated components can lead to interoperability challenges. To this end, developing standardised protocols and interfaces is crucial to ensure seamless integration. The complexity of AI algorithms and automated systems can also result in troubleshooting challenges. In this context, transparent and explainable AI models will be crucial in instilling confidence in these systems. Moreover, AI algorithms require substantial amounts of high quality data to learn effectively. Obtaining and maintaining diverse and accurate data sets can be challenging, especially for smaller enterprises.
Another major concern is handling sensitive network data while adhering to privacy regulations and ensuring data security, especially when AI systems require access to real-time network information. As AI becomes more integral to network operations, ethical considerations related to its decision-making capabilities, biases and potential impacts on society must be carefully addressed.
Conclusion
While contemporary approaches such as data virtualisation, configurable networks and software-based solutions facilitate network automation, the eventual transition towards AI- and ML-based network automation models holds the potential to transform conventional networks into deep learning networks. These advanced AI techniques can reveal historical trends, correlations within extensive data sets and implicit models, heralding a significant shift. The growing integration of AI and automation is reshaping the landscape of network infrastructure, enabling operators to use data analytics and ML-based solutions to fine-tune network parameters. The synergy between the two can help yield considerable reduction in network downtimes, resulting in enhanced customer experience. Additionally, it can simplify network expansion and reduce the risk of configuration errors.
Going forward, the evolution of AI and ML technologies will continue to help address other network challenges, thereby optimising efficiency and minimising latency. However, achieving greater synergies between AI and automation will depend on effectively addressing significant hurdles, including concerns related to interoperability, high upfront investments, and data security and privacy.
