Introduction
The advancements in technology have had a significant impact on network design and management in recent years. This paper aims to explore and report on network design and management trends over the past three years in the areas of virtualization, security, hardware, network management tools, software-defined networking, and wireless. This paper focuses on automation, software-defined networking (SDN), and intent-based networking (IBN).
Network Design Trends
Network Automation at Scale
Network operations (NetOps) personnel are no longer burdened with time-consuming daily activities owing to automation, which has also increased network availability. According to a Gartner survey, around 70% of data center networking procedures are carried out manually, which adds to the time, expense, and risk of errors while decreasing flexibility. Continuous service improvements can result from the automation of the network optimization process itself (Cisco, 2020). Network automation is becoming a reality as a result of advancements in SDN, IBN, virtualization, programmability, and open platform controllers. By 2025, the long-term goal of complete intent-based policy enforcement will begin to materialize. The ability to scale up dynamic segmentation and service optimization rules across domains, such as access, WAN, DC, multi-cloud, and IoT, will be provided by networking teams. Over the next five years, automation will most impact networking.
Figure 1 shows the Google Trends data for the search term “network automation” from 2019 to 2021. The graph shows a consistent increase in search volume over the years, indicating the growing interest in network automation.
Figure 1: Google Trends Data for “Network Automation” from 2019 to 2021.
Software-defined Networking
SDN offers a big step forward in enabling network-wide automation. Segregating the control and forwarding planes enables networking teams to operate networks as end-to-end systems, increasing management flexibility and efficiency. Since the control plane is directly programmable, applications and network services are not affected by the underlying hardware and infrastructure. Programmable SDN controllers enable the logical centralization of network intelligence. SDN was initially developed to streamline complicated data center infrastructures that required server-to-server traffic and portable, dynamic workload migrations (Xia et al., 2019). Software-defined WAN (SD-WAN), which can improve user experiences when accessing applications and cloud services, and software-defined access (SD-Access), which helps protect user and device access more effectively, are based on the same ideas.
Intent-based Networking
The main goal of network teams is to consistently provide the business with protection, performance, and services. SDN represents significant advancements in automation, but it is only one component of the solution. To support increasingly dynamic and digitally based business models, organizations also require ongoing network optimization and monitoring (Cisco, 2020). IBN provides automation tools that self-monitor the network to make sure it complies with the policies defined at configuration time. In order to continually and dynamically align the network with changing business demands, as well as respond to shifting network loads and other environmental influences, IBN collects business intent. It then leverages analytics, machine learning, machine reasoning, and automation to do so. This could entail continuously implementing and guaranteeing user, security, compliance, and IT operations rules, as well as service performance criteria.
Artificial Intelligence Enabled Assurance
In contrast to Machine Reasoning (MR), which employs acquired knowledge to move through a range of potential outcomes in search of the best solution, Machine Learning (ML) enables a system to scrutinize data and infer knowledge from it. Finding and using hidden patterns in “training” data is the objective of machine learning. Deep domain knowledge is needed to solve some problems, and MR is ideally suited for this. Network teams have the ability to employ AI more efficiently and use data to make sure that their networks function well and are consistently in line with business requirements. The complexity of the communications and networking environment is learned by AI using vast amounts of network-sourced data, and it is then able to dynamically suggest network adjustments (Batarseh et al., 2021). AI is essential to an Intent-Based Networking (IBN) model because of its capabilities. AI and cutting-edge networking technologies, such as IBN, are changing network operations. Testing brand-new apps can now be completed in a matter of minutes as opposed to weeks. When an assurance engine locates the fundamental causes of network problems and suggests changes, network troubleshooting becomes much simpler.
Network Assurance
Network assurance, a continuous assurance that the network condition and status are consistent with the intended aim, is one application area where AI has succeeded. Operators can utilize machine learning and machine reasoning to ensure the required network performance, particularly in complicated event processing, correlated insights, and remediation, which are the three key areas of assurance. When it comes to the operation of the network, ML can offer greater insights and visibility. By utilizing preloaded expert knowledge obtained from debugging processes of related problems, MR increases the power of ML (Rovira-Sugranes et al., 2022). By selecting the most relevant remedial actions utilizing the available knowledge bases, remediation enables continuous alignment to intent.
Intent-Based Networking
Intent-based networking (IBN), a network design that allows automated administration of networks, is another area where AI has proven useful. With the help of the vast amounts of data collected from the network, AI can propose changes to the network in real-time after learning about the complexity of the communications and networking environment (Cisco, 2020). Because of these capabilities, AI is essential to an IBN model. The testing of new applications can take place in minutes rather than weeks with an IBN model, and debugging network faults is made substantially simpler by an assurance engine’s ability to identify root causes and suggest changes.
Security
Finally, another area where AI has been adopted is security, specifically in network security. ML and MR are being used to develop predictive models that can identify potential security threats and vulnerabilities in real-time, allowing for faster remediation of any issues. AI-enabled security solutions can also help to reduce false positives and negatives, ensuring that security teams are not overwhelmed with alerts and can focus on real threats. The subsets of security that AI has been used in are intrusion detection, prevention, and anomaly detection. According to Batarseh et al. (2021), there has been a steady increase in the complexity of hyper-connected organizations over the past three years. As such, the adoption of AI-enabled assurance, IBN, and AI-enabled security solutions is expected to grow as network complexity and scale increase.
Management Trends
Network Operations Integration into the IT Process
In the last three years, the networking industry has experienced a significant transformation toward business optimization and digitalization. According to Cisco’s research, IT leaders are leading their organizations’ digital transformation and modernizing their IT infrastructure and operations to meet emerging digital demands. Networking teams have adopted a DevOps-led approach to integrating the network into IT processes and streamlining end-to-end workflows to become more responsive to business needs. In a 2019 Global Networking Trends Survey, 63% of IT leaders planned to put in place advanced networks that can dynamically meet business needs within three years (Cisco, 2020).
Network Operation Readiness
One notable development is the change from a reactive to business-optimized network operations attitude, which is motivated by organizations’ urgency in preparing for rising demands on the corporate network. 71% of IT leaders expected to accomplish predictive or business-optimized network operational readiness within two years (Cisco, 2020). Every facet of network operations, including network operations integration into the IT process, is evolving due to the expansion of modern network technology. Nearly one-third of IT leaders noted the value of enhancing network coordination and integration with other IT teams. At the same time, 26% highlighted the value of enhancing their capacity to interact with lines of business (Cisco, 2020).
Integration with DevOps
The integration of the DevOps approach for open-platform networks with other internal and external systems is yet another trend. This strategy will be useful for NetOps teams to combine network technologies and procedures with other IT domains, such as SecOps systems and IT service management (Jabbari et al., 2018). Over time, the network operations position will increasingly include translating business and IT intent into network policy. The business and IT intent will be immediately reflected in the network operations, which will be more dynamic and automated.
Conclusion
The advancements in technology have resulted in significant improvements in network design and management, and automation, SDN, and IBN are among the most prominent trends in this field. Network automation has increased availability and reduced manual workload, enabling network teams to scale up dynamic segmentation and service optimization rules across domains. SDN enables networking teams to operate networks as end-to-end systems, increasing management efficiency and flexibility. IBN provides automation tools that self-monitor the network and dynamically align it with changing business demands. AI-enabled assurance has made network troubleshooting and operation more efficient and has helped ensure the required network performance. These emerging trends are transforming the networking landscape, and they are expected to significantly impact network design and management in the future.
References
Cisco, I. (2020). 2020 Global Networking Trends Report.
Xia, W., Wen, Y., Foh, C. H., Niyato, D., & Xie, H. (2019). A survey on software-defined networking. IEEE Communications Surveys & Tutorials, 17(1), 27-51.
Batarseh, F. A., Freeman, L., & Huang, C. H. (2021). A survey on artificial intelligence assurance. Journal of Big Data, 8(1), 60.
Jabbari, R., bin Ali, N., Petersen, K., & Tanveer, B. (2018). Towards a benefits dependency network for DevOps based on a systematic literature review. Journal of Software: Evolution and Process, 30(11), e1957.
Rovira-Sugranes, A., Razi, A., Afghah, F., & Chakareski, J. (2022). A review of AI-enabled routing protocols for UAV networks: Trends, challenges, and future outlook. Ad Hoc Networks, 130, 102790.
write