AI Networking: Pioneering the Future of Enterprise Network Operations

The year 2023 is poised to be a momentous period for artificial intelligence (AI) technology, as it has unequivocally demonstrated its capacity to exceed human performance benchmarks. This remarkable achievement can be attributed to the ever-expanding availability of robust computing power, alongside the continuous advancements in AI algorithms and training models. One of the notable advancements include OpenAI's ChatGPT, which utilizes the Generative Pre-trained Transformer 4 (GPT-4) as a multimodal large language model.

However, according to the Founder, Chairman, and MD of Microland, Pradeep Kar, it is anticipated that conversational applications, which utilize supervised and reinforced learning techniques, will encounter various challenges, both from a technical and ethical standpoint. These challenges may require careful consideration and attention to ensure such applications' safe and effective use. But at the same time, Gartner also predicts that by 2026, generative AI technologies such as Chat GPT, will account for 20% of initial network configuration, rising up from near zero in 2023.

Meanwhile, countries and governing bodies are now taking steps towards regulating AI to safeguard the welfare of society, particularly the youth and consumers. The impact of these AI advancements on the enterprise world remains uncertain and awaits observation.

Potential opportunities in enterprise networking

Is it truly as straightforward as implementing AI to simplify network operations? The reality may not be that simple. Organizations depend on Enterprise Networks to establish connections between individuals, devices, and applications, all with the aim of delivering excellent services to their customers. However, with the increasing number of devices, applications, and data, networks are becoming more complex and dynamic.

This is becoming a key challenge for network operators who need to manage network performance, ensure network security, and minimize downtime while keeping up with the evolving technologies and customer demands. To overcome these challenges, AI Networking is a powerful technology that can help organizations improve network operations. Through the real-time processing and analysis of extensive data collected from network devices and systems, AI algorithms have the capability to swiftly make intelligent decisions that optimize network performance, bolster security measures, and enhance user experiences. AI Networking enables network operators to automate repetitive tasks, leading to a reduction in human errors and subsequent rework while ensuring allocation of resources to more valuable activities.

The Gartner report on the 2023 Technology Adoption Roadmap for Infrastructure and Operations highlights AI Networking as a valuable technology with numerous benefits, such as increased cost-efficiency, improved speed and agility, enhanced employee productivity, and superior consumer capabilities for businesses. However, it is also classified as a high-risk technology due to potential issues such as talent unavailability, high costs, cybersecurity risks, technical incompatibility, architecture complexity, and vendor lock-in. Therefore, it is crucial to carefully select the right technology, and seek the assistance of a suitable system integrator or consultant for deployment. Additionally, there will likely be a high demand for skilled professionals in this field.

Let’s understand what is AI Networking

AI Networking refers to the incorporation of AI cognitive and training models, techniques, and technologies into enterprise networking systems. It represents an application and methodology that applies artificial intelligence (AI) techniques and technologies within the realm of enterprise networking. By utilizing AI algorithms, machine learning, and automation, AI Networking aims to improve various facets of networking, such as network management, performance optimization, security, and troubleshooting.

AI Networking helps improve enterprise networks' effectiveness, availability, and reliability. This is achieved through analyzing network data, intelligent decision-making, automating day-to-day operational and remediation tasks, and adapting to changing network conditions.

Presently, AI Networking is recognized under various other terms, including automated networks, self-driving networks, healing networks, and intent-based networking. These approaches are employed in LAN, WAN, SD-WAN, managed network services, and multi-cloud networking, utilizing diverse forms of AI or ML techniques. Consequently, they all fall within the umbrella of AI Networking.

The primary goal of AI Networking is to transform the traditional human-centric approach to network operation, which relies on automation as a supplement, into intelligent and adaptive systems that are technology-centric. This transformation aims to optimize network performance, bolster security, minimize downtime, and provide superior user experiences. In this white paper, we delve into the realm of AI Networking, examining its technologies, advantages, factors to consider during implementation, the array of options available in the market, and guidance on selecting an appropriate approach for your network.

What is the difference between AI-Ops and AI Networking?

AIOps technology utilizes artificial intelligence for managing infrastructure operations. Although AIOps and AI Networking employ machine learning and artificial intelligence to enhance network operations, they differ in certain aspects. You may be curious about the distinction between AIOps and AI Networking.

AIOps revolves around the utilization of AI and machine learning to enhance the management of IT operations. It entails the application of machine learning algorithms to streamline tasks, identify and resolve issues, and optimize operations across various IT domains. AIOps finds relevance in different areas of IT, including network operations, application development, and cybersecurity.

AI Networking is a distinct discipline that harnesses the power of AI and machine learning to elevate network operations. It leverages machine learning algorithms to analyze network traffic, predict network performance, automate network management tasks, and fortify network security. The fundamental aim of AI Networking is to enhance the efficiency, reliability, and security of network operations.

In summary, both AIOps and AI-Networking share a common thread of utilizing AI and machine learning to improve network operations. However, AIOps primarily focuses on managing overall IT operations, whereas AI-Networking is specifically geared towards optimizing and streamlining network operations.

Implementation of AI Networking

Recognizing the benefits and real-world applications of AI Networking in an enterprise is advantageous, but it is equally important to grasp the diverse features and implementation approaches associated with it. Achieving successful implementation of AI Networking requires the integration of hardware, software, and networking technologies in a harmonious manner. The collaboration of these elements is vital to realize the intended outcomes of AI Networking. Presented below are key components essential for implementing the evaluated AI Networking technology.

AI Networking Techniques and functionalities

AI Networking leverages various AI techniques and algorithms to optimize and automate network operations, management, and security. Here are some of the critical AI techniques used in networking. While you look for AI Networking, some of the essential methods that you should be looking at in the AI-Networking Platform are as below:

• Machine Learning is an AI technique that improves systems with data. It has many networking applications, like predicting performance and identifying security threats. By studying past data, it can detect patterns and predict future behavior.
• Deep learning uses artificial neural networks to solve complex problems, including analyzing network data to identify performance issues, traffic patterns, and security threats.
• Natural Language Processing (NLP) utilizes artificial intelligence to comprehend human language. It has practical applications in networking by enabling the analysis of text-based data to identify errors or security breaches.
• Computer Vision: The analysis of visual data through AI is known as Computer Vision. It proves to be highly beneficial in networking as it helps to detect anomalies in devices, including unauthorized access points.
• Reinforcement Learning: Networking performance can be improved through trial and error using reinforcement learning. This enables routing and traffic management to adjust according to the network's changing conditions.

While these methods are essential, using the methods effectively with the critical functionality is very important to have an effective platform for AI Networking. Some of the critical functionalities for AI Networking include:

Benefits and use cases of AI Networking

Through the application of sophisticated AI techniques and algorithms, it becomes possible to optimize network performance, minimize downtime, strengthen security measures, and enhance user experiences by a significant 25% improvement.

Below are some of the primary benefits of AI Networking:

• Improved Network Performance: AI Networking optimizes network resources, reduces latency, and enhances bandwidth utilization by analyzing traffic patterns and user behavior. It identifies bottlenecks and takes proactive measures to reduce downtime, resulting in faster speed, better performance, and improved user satisfaction.
• Reduced Downtime with Predictive Maintenance: AI Networking can minimize business downtime by detecting potential problems before they disrupt operations. AI algorithms can predict and prevent device failures, congestion, and power outages by analyzing network data and patterns. This ensures network reliability and availability when needed.
• Enhanced Security: AI Networking helps organizations enhance their network security by quickly identifying and preventing potential security breaches through network traffic analysis. This proactive approach protects networks and data from cyber-attacks.
• Better User Experience: AI Networking improves user experience by enhancing network performance, ensuring availability, and providing personalized services. AI algorithms analyze user behavior and optimize resources for fast and reliable services.
• Cost Savings:  AI Networking in an organization saves costs by automating network operations, reducing downtime, and eliminating human errors. It predicts and prevents downtime, provides valuable insights into network capacity and user behavior, and improves network planning and design while reducing overall costs.

Organizations that adopt AI Networking can reap several advantages, such as enhanced network performance, decreased downtime, improved security, superior user experiences, and cost savings. However, it is crucial to consider the key use cases that align with your specific network operations. Below, we outline some use cases pertaining to Enterprise networking.

1. Failure detection: Determining the probability of hardware failures proactively.
2. Reroute traffic: While there is a performance issue in a Wide area network, a wireless AP, or a specific network segment, notify the change, such as QoS change.
3. Identify the root cause or help identify the root cause by correlating multiple Data sets. E.g., identify the root cause for application congestion on the network.
4. Layer-2 issues and report, E.g., Media issues with Fiber optics, Ethernet issues in LAN, Underlay congestion in SD-WAN overlays, VPN.
5. Automated quarantines for an infected segment of a network.
6. Identify network congestion and utilization patterns, and reroute traffic intelligently based on user or application context.
7. Provide recommendations for action via NLP, and ChatOps, supply information based on human questions, and summarize log information and analysis.

With more significant adoption and increased use cases, enterprise networks can reap the benefits and move towards an AI Networking platform-driven network.

Embracing the AI Networking

In exploring the benefits, approaches, and techniques of AI networking, it's crucial to grasp who offers these services and how businesses can embrace them to reap the advantages outlined.

There are primarily three provider types and an approach through which you can integrate AI Networking into your Enterprise.

1. Technology Transformation: When you adopt technology from different OEMs like Cisco, Juniper, and Aruba among others, with functionalities built in them. Various platforms are available under OEMs, such as Juniper Networks Mist systems. The Juniper Mist AI platform is cloud-based and uses artificial intelligence, machine learning, and data analytics to provide extensive network management, automation, and troubleshooting capabilities. Other platforms are - Cisco's DNA center and VMware Nyansa Voyage, also known as VMware Edge Intelligence, are noteworthy examples.
2. AI-Ops adoption: Furthermore, there exists general AI-Ops platforms like ServiceNow, ScienceLogic, and Moogsoft. Nevertheless, it is crucial to assess your specific requirements and consider factors such as scalability, integration capabilities, and ease of use when selecting the optimal AIOps platform for your organization. As mentioned earlier, during the AIOps vs. AI Networking comparison, this evaluation process is pivotal in finding the perfect match for your needs.
3. Network tools Platforms: Specific tools or platform vendors like Kentik, SolarWinds, Augtera, and Prosimo have integrated AI capabilities into their network-specific solutions.
4. The integrated Platform approach: Global system integrators like Microland mainly focuses on integrating AI Networking technologies and tools that are connectable and combined with conventional and software-defined networks, offering more flexibility in AI networking capabilities. Integrating and operating as a homogeneous network with better orchestration with these tools and platforms is crucial. Let us look at Microland’s approach to AI Networking with their platform-first approach.

Microland platform-first approach for AI-Networking 

Microland takes a unique platform-first approach by utilizing its Intelligeni NetOps Platform. The platform-first system integrates technologies and tools to deliver end-to-end network services with its Platform, which has inbuilt automation and AI-Ops with Intelligeni. The Intelligeni NetOps Platform makes integrating multiple tools, technologies, and OEM platforms easier to incorporate in a modular approach and operate efficiently. Intelligeni brings AI capabilities into infrastructure and networking. The method enables the customers to adopt new technology and tools, regardless of their network environment – legacy or software-defined. The result is an AI Networking enabled network with automated operations, allowing greater efficiency and productivity.

The Intelligeni NetOps Platform  includes two main modules: Intelligeni Transform module and Operate modules of Intelligeni NetOps platform. The Intelligeni Transform module helps customers transition their network from its current state to a desired shape, incorporating new technology and adopting agile project management practices regardless of the project's size. The Operate modules of Intelligeni NetOps platform assists customers in managing and operating their enterprise network. The Operate modules of Intelligeni NetOps platform includes Observability and AI Ops modules, which enable AI Networking and context-aware monitoring. NetDevOps automates network operations and ensures that the network is available as code. The platform's visualization layer provides persona-based visibility into networks and measures XLAs. The analytics and visualization offer extensive analyses of project status, readiness, cost analysis, project velocity, and more, while the Operate modules of Intelligeni NetOps platform visualization layer provides information on network status, compliance, capacity, and performance (including SLA and XLA), and enables proactive network management with optimized resources and performance. Under Operate modules of Intelligeni NetOps platform, the User Experience (UX) module provides the user and location experience score and proactive problem management. The platform's key advantage is its ability to seamlessly integrate with any network, allowing customers to use their existing tools and technologies.

Intelligeni is the actual AI Networking engine behind the Intelligeni NetOps Platform; while Intelligeni NetOps Platform brings the detailing and helps to manage the network, Intelligeni brings, including:

1. Full stack observability understands and optimizes network performance, relying on network analytics and incorporating comprehensive visibility and diagnostics covering all aspects from end-users to applications to computing.
2. Cognitive QoS Models can provide noise reduction and intelligent root cause analysis, focusing on identifying alerts affecting the infrastructure and network performance.
3. Automated Resolution: Automate diagnostic and resolution actions through left shifting to accelerate incident resolution significantly. Experience the power of solving incidents ten times faster than traditional methods.

Intelligeni brings AI Networking capabilities under its features. The core features that Intelligeni helps with are:

1. Full Stack Observability: Intelligeni has Observability which is crucial for identifying and diagnosing system problems when the unexpected happens. It enables quick identification of issues like a failing microservice due to a change in firewall rules.
2. Anomaly Detection and Prediction: Static thresholds can be ineffective in modern app environments, causing missed signals or unnecessary alerts. So, managing thresholds for thousands of Microservices and networking components with unique computing stacks is nearly impossible. Intelligeni has the anomaly detection and prediction capability to Filter log messages and call out the anomalies.
3. Alert correlation and noise reduction: While System malfunctions generate too many alerts, causing cognitive overload and alert fatigue for Ops Teams, Intelligeni effectively helps engineers to correlate with its AI capability and reduce the system noise.
4. QoS Impact and Behaviours: Intelligeni’s Computational QoS Model includes a Semantic Graph representing an application system's Deployment Architecture.
5. Intelligent Automation: Intelligeni lets you use IGQL language and Python/JavaScript bindings to communicate with its Knowledge Graph. This enables you to create decision rules for automation using the wealth of semantic information available.
6. Collaborative Resolution: Intelligeni helps engineers diagnose and solve problems with complete visibility and accurate analysis. The Explorer interface with ChatOps capability provides a unified platform for resolution teams to work on by accessing metrics, traces, logs, and events from various sources and tools.
7. Distributed Core and Edge model: Intelligeni has a SaaS-based Core and an on-premises/cloud-hosted Edge model. The Edge component collects data from devices and tools, performs diagnostic and remedial actions, and interacts with all devices. The SaaS-based Core stores data and provides Ops engineers comprehensive visibility across Edges, especially in hybrid and multi-cloud environments.

The Focus on User experience and zero-trust 

The platform-first approach prioritizes user experience while the user accesses the application from anywhere and utilizes zero-trust networking methodologies. This method ensures that enterprises can accurately measure and manage user experience using proper XLA based on factual data rather than perception-based experiences. Read how Microland offers cutting-edge solutions like Smart Branch SD-WAN and Secure Access Service Edge (SASE) that empower customers to establish robust Zero Trust Network Access (ZTNA) throughout their enterprise networks, irrespective of the application's location.

The reality check of the current state of the Enterprise network

Despite the numerous benefits of AI-powered networking and the availability of various platforms, many enterprise networks still need to develop into mature network estates. According to the Juniper network research, only 9% of IT leaders consider their AI governance and policies, such as establishing a company-wide AI leader or responsible AI standards and processes, to be “fully mature.” According to the research survey, 96% of IT leaders expect AI to help reduce risks and improve quality within their organization in the next 12 months. However, only 25% believe that AI can bring significant changes to networking and the cloud, the business functions with the most potential to benefit from AI implementation. The hybrid model of work that emerged during the pandemic has added further complexity, with different security systems and heterogeneous networks, making it challenging for CIOs to advance towards a mature network capable of AI Networking and automated operations. One reason for this could be that network operations personnel tend to be cautious and don't entirely rely on the AI network's suggested actions to resolve network issues. They may need to verify the results beforehand, which can reduce the effectiveness of the AI's recommendations.

For an advanced and mature enterprise network with AI Networking capabilities, it is crucial to have a homogeneous system that has observability with context-aware ability, and can proactively detect and prevent issues with automated fixes. Implementing network-as-a-code with a skilled team of NetDevOps professionals is crucial in achieving XLAs instead of SLAs, providing significant benefits to the organization. Although the transition to an AI-enabled enterprise is a gradual journey that requires a mature networking state, the truth is that many organizations are yet to achieve basic hygiene in terms of operational and technological maturity.

The Microland Edge

Although many enterprises are yet to fully integrate AI into their networks, Microland offers a unique approach to help companies gradually achieve their goal of attaining an AI-powered homogeneous network.

The strategy involves standardizing the network infrastructure to improve hygiene and developing business cases for implementing AIOps or AI Networking. At this stage, it is more of setting the base for the future stage of operations and technology. Next, platforms are deployed, technology transformations are carried out if needed, and AI techniques are applied to the network for various use cases. At this stage, it is crucial to move the Enterprise networking into an AI-powered or supported infrastructure with Managed network platform enabled by Observability and AI-Ops, achieving the state of Automated Operations with AI use cases. This approach ensures that the Network services are maintained and operated at optimal performance. Enterprises can consume the approach with a commoditized model of Network as a service, which leaves the liability and responsibility of AI Networking technology with Microland. 

AI-powered network : Benefits realized

By utilizing Microland's platform-first approach with the  Intelligeni NetOps Platform, enterprises can experience up to a 30% improvement in network performance and up to a 45% reduction in MTTR, resulting in significantly less downtime. Additionally, enterprise customers can benefit from technology transformation 35% faster than usual. Implementing zero-trust security measures across users, networks, and applications with measurable XLAs can significantly impact business performance for enterprises.

Way forward for AI Networking

Enterprises must embrace the inevitable future of an AI-powered network. However, carefully selecting the right approach is equally important, as there is no one-size-fits-all solution. This involves selecting appropriate technology, tools, and platforms while ensuring seamless integration with existing networks to protect prior investments. Choosing the right consultant and approach to reach the desired outcome is critical, leading to significant business advantages.

About the Author:

Ramesh N G

Ramesh, is Senior Director – Digital Network Security at Microland, with 22 years of experience in Enterprise networking, specializing in Software-Defined Networking, Network automation, and Data center networking. Ramesh has accomplished many notable achievements, such as creating differentiation around Network operations, developing solutions for hybrid networking, and participating in various Industry forums like IEEE, Open Stack forum, and ONUG. At Microland, Ramesh is a remarkable driving force for innovative solutions and IP Building for Managed network services, contributing to developing solutions and services under Network and Cyber Security practice. In his leisure time, he indulges in playing badminton and embarking on long-distance runs. Furthermore, he relishes taking extensive drives to explore picturesque locales.