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Enter the Collaboration Network Slice: Enterprising the Home

by Ben Parker, CTO at Guavus, a Thales company

As the COVID-19 pandemic spreads across the world, it’s clear that a new normal has emerged. From how we eat and exercise to how we work has changed for us all.

The unplanned move of so much of the world’s workforce to work from home (WFH) has produced some very interesting data. For one, many companies did not see a reduction in employee productivity. In fact, employee productivity grew over 15% in most countries.

The use of collaboration tools that were often used as a maligned necessity are now common to many. Even some people have been heard secretly discussing how nice it is to use Zoom and Klaxoon from the comfort of their homes instead of being stuck in a small conference room for hours on end.

The numbers speak for themselves:

Source: Wandera

Source: Wandera

So, what does this mean for Communications Service Providers (CSPs)?


We view this as an incredible opportunity for CSPs to begin catering to those who work from home. Provide services that enable the collaboration tools to work at peak performance and provide security that enterprises can rely on.

Prior to the pandemic, working from home and open use of collaboration tools was somewhat of an exception.

Now, this is the new normal. From the Fortune 100 to the small companies that power the world’s economies, the CSP has an opportunity to reduce their IT costs, simplify the use of these tools, and even make working from home more productive than it has been in the past.

5G is at the top of everyone’s mind. With 5G comes powerful capabilities to deliver new products and services that will drive the 4th industrial revolution. One of those key capabilities is the ability to deliver network slices. A network slice is a logical separation and control of resources that are dedicated to a specific enterprise or user community. Let’s discuss a new user community that would benefit from Network Slice services.

Remote workers have often found themselves on video conference calls when the internet connectivity becomes unstable. Now the entire group stops and shuts off video, mutes themselves, or even reconnects over another media. This is very disruptive and frustrating to the users and an inefficient way to do business. The CSP has an opportunity to mitigate this problem.

The “collaboration slice” is a service that could be offered by the CSP. It’s a service that may be provided to individual subscribers, who need enhanced quality-of-experience (QoE) and security for WFH services, that aligns with their company’s need for a low-cost (compared to a dedicated enterprise-class network slice) and efficient (compared to a cumbersome virtual private network) WFH connectivity solution.

In a 5G network, the collaboration slice would be enabled and managed using artificial intelligence (AI) / machine learning (ML) and advanced analytics capabilities like these:

– RAN Analytics would monitor each Radio Access Network (RAN) sector including macro and small cell deployments. These analytics will show effective goodput, latency, RAN Key Performance Indicators (KPIs), handoffs, and device types. This data will be presented in real-time to operations teams and also stored for fusion with other data sources to improve operational decisions and troubleshooting. Coverage maps will be generated and continuously updated to show CSP staff where coverage is available. In some cases, it may be best to offer a subscriber a femtocell or picocell in order to provide in-home coverage until the network build reaches the subscriber’s home or office. This tool is key to providing true visibility into RAN performance, cost, and capacity to the CSP ensuring customers are provided the level of quality while the CSP maintains the correct cost associated with delivering the service. In short, RAN Analytics ensures the best balance of subscriber QoE and CSP femtocell/picocell investment.

– Content Analytics will be employed to characterize all forms of usage on the network. Fused with RAN data it will be possible to see how each subscriber and device are using the network. This includes identification of specific applications and understanding what resources are consumed by the application. Content Analytics does not require any specific subscriber knowledge or intrude on private information. It will allow each application to be identified and characterized continuously to understand the normal operating parameters of the application and the network, and enable network resources to be optimized for the applications being used by WFH users, ultimately improving their experience and satisfaction. The speed at which these applications are updated will force a continuous learning model to ensure both consumer usage and application resource needs are adjusted in real time and fed to the network data analytics function (NWDAF), part of the 3GPP’s 5G standardization efforts. Additionally, this data would be employed for marketing purposes to identify users of collaboration tools who may benefit from enhanced QoE by improving the underlying quality-of-service (QoS).

– NWDAF will be deployed to collect data on a per-slice level to understand the resource utilization and performance of each slice. AI/ML-based Analytics added to NWDAF that will include detailed RAN Analytics, Slice QoE Requirements, Cost Models, and Content Analytics information will enhance input to policy, scaling, and service level agreement (SLA) management of the slice. These network slice management actions will likely be far more dynamic than they have been in the past. We will see policy and resource shifts on an hourly basis as compared to weekly or monthly. These resource needs will be balanced by cost constraints and this can only be done when the CSPs have true control over end-to-end cost and demand models.

– Operational Intelligence will be a key function in 5G networks, with the goal to improve operational efficiency (faster mean-time-to-understand/diagnose/resolve problems) in order to reduce costs and elevate customer experience/satisfaction. Taking the insights generated from the AI/ML-based analytics mentioned above, information relating to future network element failures and current degradations will be provided to operations teams to reduce the time to understand and troubleshoot failures. This information can also be fed directly to orchestration systems to automatically mitigate many predicted network failures. Continuous analytics allows for enhanced Root Cause Identification and Machine Learning will improve both identification and decisioning. Predicting future failures will be demonstrated as part of Operational Intelligence. The ability to accurately predict failures will give operations teams the ability to triage and mitigate failures before the customer even sees a decay in network performance. This translates into fewer SLA violations and overall lower cost of operations.

Whether it’s a WFH slice or any other form of enterprise service, one thing is clear. The consumer-oriented way of operating networks will be supplanted by the need for CSPs to offer enterprise-class service levels and possibly even guarantees to remain competitive. This represents a transformation for many CSPs and this transformation will be facilitated by the use of analytics tools to provide both the critical insights and automated action needed to deliver the service levels that will be needed.

Image attribution: iStock (image #01), Wandera (graphs #02 & #03), National Broadband Network (graph #04)