Some time back, when a major cable operator was looking into why a particular residential neighborhood suffered from continuous service degradation, it found that the culprit was one resident who had built a home hosting business offering P2P file sharing and torrenting. His monthly usage? Over 10TB a month…on his home network!
While this is an egregious case of excessive usage, it’s not far-fetched to assume that in the not-too-distant future, this amount of data consumption will be the norm. With faster internet speeds, 4K and UHD streaming, and expanded access through more affordable connectivity programs, Internet traffic is expected to grow 15-20% each year. Of that traffic, 5-10% of subscribers will be responsible for 50-65% of bandwidth consumption.
The disproportionate usage by the heaviest users will drive unnecessary CapEx. Ideally, resources should be optimized according to what the majority of users need, not what the heaviest users demand. But this would require a new way of planning.
Rethinking One-Size-Fits-All Network Optimization
Traditional approaches to planning look only at throughput minimums and maximums and ignore that in real-time, users might be perfectly happy with what they’re already getting, even if the throughput is lower than expected. If someone is web browsing or doing emails, they don’t care about speed or performance as much as someone who is streaming video or gaming.
Not every customer requires the same amount of throughput, latency, or other network-based metric. Volume- and throughput-based approaches to network optimization fail to accurately convey the quality-of-experience (QoE) people are getting. Volume-based planning means you throw capacity at problems that don’t really exist, and possibly neglect areas where problems do exist.
Static policies have little impact on saving capacity when it matters the most – during congestion time. And they can have a negative impact on customer experience outside of congestion times.
Buying spectrum, building new sites, and upgrading copper, coax, and fiber in an attempt to make “unlimited” infinitely possible to everyone is no longer sustainable. Operators need to optimize around the customer, not the network.
Automating Network Optimization for Intent-Based Decision-Making
Real-time visibility into who is actually having a good or bad experience, regardless of the throughput delivered, can better inform upgrade decisions, allowing you to add capacity, when and where it’s really needed.
This is the purpose of our App QoE-powered Sandvine Network Optimization Portfolio, which includes an array of Use Case software modules that enable you to better manage network traffic and ease congestion, such as:
These Use Cases enable more efficient QoE-based planning and automated, real-time optimization beyond what static policies can do in times of congestion, when more immediate, dynamic decision-making is needed.
Each Use Case software module creates algorithms around intent and the experience subscribers will need to be happy. Using advanced machine learning-based application classification and content categorization, these solutions evaluate multiple dimensions of satisfaction, establishing QoE “scores” that – through an automated closed loop – trigger dynamic policies and actions in real time.
This is especially helpful during periods of compromised QoE, when nodes start getting congested. That’s when our intent-based network optimization solutions kick in and start reallocating resources to ensure subscriber QoE scores remains high.
For example, during times of congestion you may want to protect mobile users from fixed wireless access (FWA) users that are doing HD streaming over platforms like Netflix or Hulu. Or, perhaps you want to protect the experience of gamers, who are typically sensitive to latency and lag. Since video is more about throughput while gaming is more sensitive to latency, you’ll want to prioritize different packets, traffic, or segments based on the intent for different types of subscribers.
The key to responding in real-time is real-time visibility into not only the network, but also the application type, the link (upstream or downstream), and the subscriber. That way, the intent can be supported by appropriate policies and actions. That’s why it’s critical that actions and policies be automatically applied, optimizing existing resources according to the intent, and according to when it matters the most.
Enabling Strong QoE for Business Users
The intent and the appropriate policies to support that intent will vary according to the type of subscriber QoE you aim to deliver. For example, residential intent and business intent can be very different. Residential can be 70% video, gaming, and web browsing, and business can be 40% video conferencing, browsing, Zoom and emails.
Different applications have different KPI requirements, such as throughput, latency, and packet loss, to achieve a certain level of QoE. The specific traffic mix of applications in a node, especially during congestion, relates directly to true Subscriber and Application QoE in that node. Therefore, some nodes that are considered “congested” using traditional metrics (such as throughput) can possess acceptable levels of Subscriber and Application QoE, and can be deferred for upgrades.
Because you can’t give all resources to everyone all the time, you have to establish and ensure minimum QoE scores for the most subscribers. You need a single source of truth so that you can trust you have independent, consistent, and accurate capacity measurements that help optimize experiences, while driving efficiencies so that networks work as efficiently as possible around different priorities and traffic types.
To get more information about Sandvine’s Intent-Based Network Optimization portfolio, register for our upcoming webinar, and see our Network Optimization portfolio in action. Also, schedule a demo to see how our solutions can help you reduce costs, improve ROI, and improve customer satisfaction.
Other resources to check out:
Global Internet Phenomena Report
Sandvine Use Cases
Customer Case Studies and stories