AI networks

AI Enhanced Networking

 

In previous blogs, we discussed the importance and technologies of AI-enhanced networking infrastructure for IT Services and NetOps teams. It is now time to discuss how AI-enhanced networking capabilities are used in operations today.

AI-enhanced networking technology is being used by more organizations around the world to improve their IT capabilities. It uses ML and AI capabilities to gain a better understanding of their network and security postures and risks. This helps them increase their operational resilience.

There are many AI-enabled networking technologies out there. Each organization must consider the specific use cases they wish to implement. Based on the success stories we have seen from customer using Cisco products, I would like you to consider three use cases that show the benefits of an AI enhanced networking infrastructure for NetOps teams. These can be applied for nearly every industry.

1. AI-driven Issue Identification 

The NetOps team used to have to manually look at logs and events from various systems in order to find the cause of a network problem. This can be time-consuming and increase outage times for customers. If the problem is intermittent or cannot be fixed consistently, this becomes even more difficult. IT Services or Network Ops teams are unable to manually review every log, event, and performance indicator in the network. it is possible to identify and fix intermittent problems.

AI-Enhanced Infrastructure continuously generates operational telemetry. AI/ML techniques make use of the telemetry data in order to create dynamic system baselines. They also detect significant deviation from these baselines and detect system anomalies. These anomalies can be used to suggest root causes and possible solutions. AI-enhanced infrastructure is able to pinpoint the source of faults and provide steps for resolving them for IT Services (ITOps) or Network Services teams (NetOps).

This AI-driven network observation improves the NetOps team’s experience. It also ensures that the network user has the best possible experience. Most network issues can be fixed quickly or prevented entirely.

Some NetOps teams may find it difficult to use an automated issue resolution system. We have already discussed the fact that AI-driven systems need trust in order to operate seamlessly as a closed-loop system. It is possible to integrate remediation into ITOps and NetOps workflows. This allows network professionals to validate the AI’s analysis before implementing remediation. Integration of the AI system and IT service management (ITSM), systems that provide approval gates, can help achieve this.

The Cisco DNA Center Assurance is currently available to assist organizations in making this happen. The deployment of DNA Center Assurance can give you better insight into your network and help to identify and fix issues. As a Cisco Authorized Reseller JNS is positioned to help you implement Cisco DNA Center.

2. Improving Wireless Performance 

The wireless connectivity standards have changed dramatically, starting with 802.11a/b and ending with Wi-Fi 6E. Wireless deployment has become more complex due to the increased speeds, number and bandwidth of channels, etc.

Radio resource management (RRM), which allows for dynamically adjusting the channel allocation and transmission powers of radio APs within a deployment to prevent interference from adjacent channels and co-channels.

Wi-Fi 6E has four-channel bandwidths and more than 80 channels. There are multiple attributes that can be optimized for. It is impossible to optimize Wi-Fi 6E manually because of all the possible operational combinations. NetOps or ITOps is able to cope with this complexity thanks to AI/ML capabilities.

AI-enhanced Radio Resource Management is able to analyze both the present and historical conditions, and set optimal wireless configuration parameters. It can also deliver optimal performance as network conditions change over time.

Another important AI-enhanced capability in wireless networks is the impact of the physical environment on its performance. The best wireless network architecture design depends on many factors such as the number of users, obstacles like windows or walls, and even the layout of the buildings.

To architect a wireless network, we used a 2D wireless visualization to visualize the space. Most of the devices in office networks were computers sitting at fixed heights on desks. With IoT devices and sensors, mobile devices and a variety of working environments, it is necessary to optimize wireless performance throughout the entire physical space.

Cisco DNA Center and Wireless 3D Analyzer are helping NetOps and ITOps teams save time planning, troubleshooting and validating network coverage. This immersive 3D visualization of a deployment environment offers a first-person virtual tour of the building, as well as other capabilities.

3. Enhanced Endpoint Analytics 

Different security and QoS policies may be required by endpoints when they connect to the network. NetOps teams could accomplish this in two ways when onboarding devices: (1) they set the policy for each device that is connected to the network; or (2) they set a policy and manually identify which device belongs to which group. Both models are susceptible to human error due to the rapid growth in network devices brought about by IoT.

It is difficult to manually categorize every device in a network. This poses a serious security risk. Bad actors could gain access to your network and launch attacks on it.

AI/ML uses device behavior to recognize and understand endpoints that are connected to a network. This reduces human error by either NetOps or an IT Services team. Automatic identification classes and assigns each endpoint to the right group. This ensures that the device group is subject to consistent policies with minimal human intervention.

Cisco DNA Center’s AI Endpoint Analytics capability allows ITOps and NetOps teams greatly reduce security risks and manual operations time. It gathers deeper context and visibility from the network and devices ecosystem to help them make better decisions. Once endpoints have been connected to the network, Cisco Identity Services Engine can monitor their behavior and enforce policies and segmentation in order to create a zero-trust environment.

These are just three examples of possible use cases that IT Services teams can use immediately. However, I would like to emphasize the importance of using AI-enhanced Infrastructure in business-relevant ways and setting the right pace for your AI transformation.

About JNS

As not only a Managed Services Provider but also Cisco Partner we are able to deliver on helping businesses use Cisco DNA Center for their environments. Call us today to learn how our IT Services team can help your team deploy Cisco DNA Center.

 

 

Joint Network Systems
1100 Brickell Bay Drive
Miami Florida 33231
Tel: 866-JNS-NETS
www.jointnetworks.com

AI networks

AI Enhanced Networking

 

In previous blogs, we discussed the importance and technologies of AI-enhanced networking infrastructure for IT Services and NetOps teams. It is now time to discuss how AI-enhanced networking capabilities are used in operations today.

AI-enhanced networking technology is being used by more organizations around the world to improve their IT capabilities. It uses ML and AI capabilities to gain a better understanding of their network and security postures and risks. This helps them increase their operational resilience.

There are many AI-enabled networking technologies out there. Each organization must consider the specific use cases they wish to implement. Based on the success stories we have seen from customer using Cisco products, I would like you to consider three use cases that show the benefits of an AI enhanced networking infrastructure for NetOps teams. These can be applied for nearly every industry.

1. AI-driven Issue Identification 

The NetOps team used to have to manually look at logs and events from various systems in order to find the cause of a network problem. This can be time-consuming and increase outage times for customers. If the problem is intermittent or cannot be fixed consistently, this becomes even more difficult. IT Services or Network Ops teams are unable to manually review every log, event, and performance indicator in the network. it is possible to identify and fix intermittent problems.

AI-Enhanced Infrastructure continuously generates operational telemetry. AI/ML techniques make use of the telemetry data in order to create dynamic system baselines. They also detect significant deviation from these baselines and detect system anomalies. These anomalies can be used to suggest root causes and possible solutions. AI-enhanced infrastructure is able to pinpoint the source of faults and provide steps for resolving them for IT Services (ITOps) or Network Services teams (NetOps).

This AI-driven network observation improves the NetOps team’s experience. It also ensures that the network user has the best possible experience. Most network issues can be fixed quickly or prevented entirely.

Some NetOps teams may find it difficult to use an automated issue resolution system. We have already discussed the fact that AI-driven systems need trust in order to operate seamlessly as a closed-loop system. It is possible to integrate remediation into ITOps and NetOps workflows. This allows network professionals to validate the AI’s analysis before implementing remediation. Integration of the AI system and IT service management (ITSM), systems that provide approval gates, can help achieve this.

The Cisco DNA Center Assurance is currently available to assist organizations in making this happen. The deployment of DNA Center Assurance can give you better insight into your network and help to identify and fix issues. As a Cisco Authorized Reseller JNS is positioned to help you implement Cisco DNA Center.

2. Improving Wireless Performance 

The wireless connectivity standards have changed dramatically, starting with 802.11a/b and ending with Wi-Fi 6E. Wireless deployment has become more complex due to the increased speeds, number and bandwidth of channels, etc.

Radio resource management (RRM), which allows for dynamically adjusting the channel allocation and transmission powers of radio APs within a deployment to prevent interference from adjacent channels and co-channels.

Wi-Fi 6E has four-channel bandwidths and more than 80 channels. There are multiple attributes that can be optimized for. It is impossible to optimize Wi-Fi 6E manually because of all the possible operational combinations. NetOps or ITOps is able to cope with this complexity thanks to AI/ML capabilities.

AI-enhanced Radio Resource Management is able to analyze both the present and historical conditions, and set optimal wireless configuration parameters. It can also deliver optimal performance as network conditions change over time.

Another important AI-enhanced capability in wireless networks is the impact of the physical environment on its performance. The best wireless network architecture design depends on many factors such as the number of users, obstacles like windows or walls, and even the layout of the buildings.

To architect a wireless network, we used a 2D wireless visualization to visualize the space. Most of the devices in office networks were computers sitting at fixed heights on desks. With IoT devices and sensors, mobile devices and a variety of working environments, it is necessary to optimize wireless performance throughout the entire physical space.

Cisco DNA Center and Wireless 3D Analyzer are helping NetOps and ITOps teams save time planning, troubleshooting and validating network coverage. This immersive 3D visualization of a deployment environment offers a first-person virtual tour of the building, as well as other capabilities.

3. Enhanced Endpoint Analytics 

Different security and QoS policies may be required by endpoints when they connect to the network. NetOps teams could accomplish this in two ways when onboarding devices: (1) they set the policy for each device that is connected to the network; or (2) they set a policy and manually identify which device belongs to which group. Both models are susceptible to human error due to the rapid growth in network devices brought about by IoT.

It is difficult to manually categorize every device in a network. This poses a serious security risk. Bad actors could gain access to your network and launch attacks on it.

AI/ML uses device behavior to recognize and understand endpoints that are connected to a network. This reduces human error by either NetOps or an IT Services team. Automatic identification classes and assigns each endpoint to the right group. This ensures that the device group is subject to consistent policies with minimal human intervention.

Cisco DNA Center’s AI Endpoint Analytics capability allows ITOps and NetOps teams greatly reduce security risks and manual operations time. It gathers deeper context and visibility from the network and devices ecosystem to help them make better decisions. Once endpoints have been connected to the network, Cisco Identity Services Engine can monitor their behavior and enforce policies and segmentation in order to create a zero-trust environment.

These are just three examples of possible use cases that IT Services teams can use immediately. However, I would like to emphasize the importance of using AI-enhanced Infrastructure in business-relevant ways and setting the right pace for your AI transformation.

About JNS

As not only a Managed Services Provider but Cisco Partner JNS is able to deliver on helping businesses use Cisco DNA Center for their environments. Call us today to learn how our IT Services team can help your team deploy Cisco DNA Center.

 

 

Joint Network Systems
1100 Brickell Bay Drive
Miami Florida 33231
Tel: 866-JNS-NETS
www.jointnetworks.com