A Sandvine Technology Showcase

Contents

Executive Summary ................................... 1

Introduction to QualityGuard ....................... 2

QualityGuard Congestion Response System ... 2

A Closed-Loop Dynamic Control System .... 3

Hitting the Target Goodput ................... 4

Balancing Cost-Savings with Good QoE ...... 4

Congestion Detection (the Trigger) ............. 4

Congestion Mitigation (Enforcement) ........... 5

Latency Tolerance based on Application

Category ............................................. 5

Application-Agnostic Enforcement .............. 6

Deploying and Verifying Fairshare Traffic

Management ........................................... 7

Topology-Aware Detection and Enforcement . 7

DSL Networks .................................... 7

Cable Networks .................................. 7

Mobile Networks ................................ 8

LTE Networks .................................... 9

Automatic Calibration and Location Hierarchy10

Verifying Results – QualityWatch Congestion

Reporting ........................................... 10

Predicable and Verifiable Cost-Savings .......... 12

Conclusions ............................................ 13

Summary of Detection Techniques ............. 13

Summary of Management Considerations ..... 14

Related Resources ................................ 15

Executive Summary Network congestion is defined as the situation in which an

increase in data transmissions results in a proportionately

smaller (or even a reduction in) throughput. In other words,

when a network is congested, the more data one tries to send,

the less data is actually successfully sent.

There are many factors that must be considered and understood

to critically evaluate congestion management systems, as the

accuracy of detection mechanisms varies widely and many

factors determine how effective the management policies

themselves are in alleviating the congestion. Only by linking an

accurate trigger with a precise management policy can CSPs

achieve their congestion management goals.

QualityGuard is a closed-loop dynamic control system for

network congestion management that automatically measures

access resource latency in real-time and continuously works to

provide the optimum output to provide good QoE to the vast

majority of attached subscribers. QualityGuard performs the

following two main functions:

1. Automatically detects congestion by measuring real-time

subscriber QoE via aRTT latency

2. Automatically removes or prevents congestion by shaping

traffic until achieving a maximum throughput rate just

short of the point where the access resource would slide

into congestive collapse, which can be thought of as the

“target goodput ”

QualityGuard is the most effective means of achieving a perfect

balance between cost-savings through the extension of access

resource lifetime and the preservation of good QoE for the vast

majority of subscribers during times of congestion.

The QualityGuard Congestion Response System

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Introduction to QualityGuard Congestion occurs in any shared resource, whether it is city streets during rush hour or the line-up for a

washroom during breaks during a live sporting event. Financially speaking, it is inefficient to scale

these resources so that they are available for all users at all times. This same principal applies to

guaranteeing speed and availability for an Internet connection – it would cost substantially more than

what a typical subscriber pays each month to accommodate such a scale.

Network congestion is defined as the situation in which an increase in data transmissions results in a

proportionately smaller (or even a reduction in) throughput. In other words, when a network is

congested, the more data one tries to send, the less data is actually successfully sent. There are many

factors that must be considered and understood to critically evaluate congestion management systems,

including:

Defining the goal: what is a CSP trying to achieve?

Network neutrality: does the solution fit within a particular regulatory environment?

Topology awareness: does the solution rely upon a complete understanding of the network’s

structure?

At a high level, a congestion management solution has two functional components:

A mechanism to alleviate the impact of congestion; the actual ‘management’

A mechanism to switch on the alleviation mechanism; the detection ‘trigger’

However, the accuracy of detection mechanisms varies widely, and many factors determine how

effective the management policies themselves are in alleviating the congestion. Only by linking an

accurate trigger with a precise management policy can CSPs achieve their congestion management

goals.

QualityGuard Congestion Response System Solving the access network congestion problem requires the ability to detect congestion at the exact

time it is occurring, which suggests that predictive values such as time-of-day, concurrent users, and

bandwidth thresholds are not accurate.1 As a detection trigger, latency proves most accurate because

it can be measured in real time. Once detected, the congestion response should be flexible and

accurate enough to ensure that congestion management goals are achieved in accordance with the

CSP’s operating and regulatory parameters. The QualityGuard Congestion Response System makes

Sandvine’s Fairshare Traffic Management product the first solution to achieve this goal.

QualityGuard is a closed-loop dynamic control system for network congestion management that

automatically measures access resource latency in real-time and continuously works to provide the

optimum output to provide good QoE to the vast majority of attached subscribers. QualityGuard

generates a score from 0 to 100, based on latency measurement, to represent the overall service

quality experienced by subscribers. This score value becomes the input to the response system

algorithm. The algorithm takes the score, as well as the change in the score over the last interval, and

calculates an output value for the Policy Traffic Swicth (PTS) dynamic bandwidth shaper, which is

adjusted as required at each sample interval. The goal is that the bandwidth at any given time should

represent the optimal target goodput, which is the maximum traffic throughput that the node can

1 For a complete exploration of access network congestion and the various techniques used to address it, see the Sandvine

whitepaper Network Congestion Management: Considerations and Techniques.

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handle while delivering good QoE to the vast majority of subscribers attached to the resource.

QualityGuard performs the following two main functions:

1. Automatically detects congestion by measuring real-time subscriber QoE via aRTT latency

2. Automatically removes or prevents congestion by shaping traffic until achieving a maximum

throughput rate just short of the point where the access resource would slide into congestive

collapse, which can be thought of as the “target goodput2”

A Closed-Loop Dynamic Control System Like instantly opening an express lane for all vehicles whenever and wherever a traffic-jamming event

occurs on the highway, QualityGuard provides an accurate and reliable method of getting more out of

existing access network capacity. QualityGuard automatically measures access network quality in real-

time and continuously works to provide the optimum output to provide good QoE to the vast majority

of subscribers during times of congestion. Subscriber QoE, in the form of aRTT (access Round Trip

Time) measurements, is the input for the QualityGuard ‘state machine’ that operates based on the

dynamic interactions of control theory3 to automatically detect and prevent network congestion.

Figure 1 provides a conceptual reference for how QualityGuard functions. In this case, QualityGuard

has been configured to take action when detected subscriber QoE falls below a real-time quality score

of 85, which corresponds to an aRTT latency measurement of about 250ms.

Figure 1 – QualityGuard congestion response in practice

2 http://en.wikipedia.org/wiki/Goodput 3 http://en.wikipedia.org/wiki/Control_theory

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Solutions that lack the accuracy of real-time quality detection and, in mobile networks, subscriber

mobility awareness will often enforce policies based on inaccurate static thresholds that target the

wrong subscriber traffic on uncongested resources. The consequence is a failure to achieve both the

business objective of predictable, targeted CapEx deferral and the technical objective of ensuring high

good QoE for the vast majority of subscribers on congested cells during times of congestion.

QualityGuard provides the optimal approach in terms of maximizing both subscriber QoE and

infrastructure lifetime for a given access resource.

Figure 2 – Maximizing subscriber QoE and infrastructure lifetime

Hitting the Target Goodput From a technical standpoint, QualityGuard’s goal is to detect congestion whenever and wherever it

occurs in the access network, and then take action (shape low-value traffic) to find the optimal target

goodput for the access resource. The target goodput is the maximum throughput that the access

resource can maintain while still providing a good QoE to the 95-99% of subscribers.

Balancing Cost-Savings with Good QoE QualityGuard is therefore the most effective means of achieving a perfect balance between cost-

savings through the extension of access resource lifetime and the preservation of good QoE for the vast

majority of subscribers during times of congestion.

Congestion Detection (the Trigger) An access network resource slips into congestive collapse as throughput approaches maximum capacity;

at this variable point, latency begins to increase exponentially until it reaches a final tipping point

where the access resource, such as a DSLAM or mobile cell, experiences congestive collapse. This is

when subscribers experience the greatest deterioration of QoE for real-time applications, which

accounts for 90-95% of usage during times of congestion.

One of the main advantages of using latency as the detection trigger is that it can be measured in real

time and directly correlates to the congestive collapse of the equipment and a sudden degradation of

service quality as experienced by the vast majority of subscribers. The real-time measurement

capability of the Sandvine Policy Engine allows QualityGuard to detect the approach of congestive

collapse and take action to prevent it from occuring at all.

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Figure 3 - Relationship between throughput and latency

The aRTT metric represents a measurement of the time from when a packet enters the operator’s

network to when the response packet leaves it via the same point. It is a direct measurement of

latency, and it has proven to be a highly accurate way of measuring the QoE of subscribers using real-

time applications.

Simply looking at subscriber count, or combining it with bandwidth throughput, fails to account for the

reality of application type and how a minority of subscribers downloading files can dominate bandwidth

on an access resource.

Congestion Mitigation (Enforcement) Sandvine’s Fairshare Traffic Management product provides all of the key components required to

deploy an accurate and comprehensive congestion management solution.

SandScript4 is not constrained by the limits of rigid-form policy systems, which means that it can offer

a variety of adaptable enforcement options to ensure accuracy and regulatory compliance.

Latency Tolerance based on Application Category With a means to detect imminent congestion accurately, the policy action can be to briefly shape

recent heavy users and/or low priority “experience-later” application traffic during times of

congestion. This shifts the effects of congestion to the contributors, while ensuring subscribers using

real-time applications (90-95%) have a highQoE. However, to do this, the solution must be aware of the

network topology and which subscribers are attached to specific access resources, including when they

move between cells in mobile networks (subscriber mobility awareness).

4 For more information see the Sandvine technology showcase SandScript: The Advantage of Freeform Policy.

Congestive

collapse

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Mitigation policies include the option of targeting specific application categories based on their

tolerance for degraded latency. From the reversed perspective of how a CSP would prioritize

application categories on behalf of subscribers, this can be thought of as “high-value, medium-value

and low-value”. Some applications are more tolerant than others regarding latency, so it’s necessary to

define traffic hierarchically in terms of the perceived impact on a subscriber’s real-time experience.

For example:

High-Value: VoIP, Gaming, Web Browsing – essentially any Internet activity where even the slightest

increase in latency results in a significantly degraded online experience.

Medium-Value: Video streaming – here increased latency can have an impact, but when managed

carefully and intelligently some increase in latency can be accommodated while preserving the overall

online experience.

Low-Value: P2P and bulk downloading – here the impact of latency has the least effect on subscriber

experience, since the intention is to draw data from the network and experience the benefit later.

Application-Agnostic Enforcement Fairshare Traffic Management offers the flexibility of application-agnostic policy for the enforcement

actions that prevent congestion from occurring. This reflects the reality that subscribers are often both

contributors to and victims of congestion, and local regulations may prohibit the identification of

application type in policy.

Sandvine studies on short-term subscriber usage show that, when congestion is occurring, over a 15-

minute period one to five percent of subscribers are typically using 80% of an access resource’s

bandwidth. Using the short-term usage history of users (15 minutes) targets the true contributors to

congestion for a fair, proportionate, and application-agnostic solution. This enforcement option can be

used in isolation or combined with awareness of application value category.

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Deploying and Verifying Fairshare Traffic Management Fairshare Traffic Management deploys into any access network type, which means QualityGuard can be

applied to any fixed or mobile network to automatically manage congestion.

Topology-Aware Detection and Enforcement Fairshare Traffic Management integrates a granular understanding of the access network topology to

ensure that congestion is detected at the appropriate location for an effective mitigation strategy that

captures all affected traffic. QualityGuard detects and mitigates congestion as close to the network

edge as possible.

DSL Networks

Fairshare Traffic Management is deployed in some of the largest DSL networks in the world, including

networks with multiple layers of subtended DSLAMs5. By measuring latency to determine when a DSLAM

is approaching congestive collapse, QualityGuard ensures consistent accuracy while overcoming the

challenge of dynamic maximum capacity for subtended DSLAMs. Figure 4 shows the deployment for DSL

networks:

Figure 4 – Sandvine Fairshare Traffic Management with QualityGuard for DSL networks

Cable Networks Fairshare Traffic Management has been deployed in cable networks since 2007, including the largest

Tier-1 MSOs in the world6. In cable networks, Sandvine takes advantage of the highly-accurate and

detailed cable network control plane protocols to detect congestion based on predictable maximum

bandwidth threshold for each individual QAM. This ensures congestion detection is always accurate.

Figure 5 shows the deployment in cable networks.

5 For a complete explanation of the reality of dynamic capacity in DSL and cable networks, see the Sandvine whitepaper Network

Congestion Management: Considerations and Techniques. 6 As of 2014, Fairshare Traffic Management is deployed in 5 of the top 6 North American MSOs, and 6 of the top 10 MSOs across

the globe.

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Figure 5 – Sandvine Fairshare Traffic Management with QualityGuard for cable networks

Mobile Networks In mobile networks, Fairshare Traffic Management measures subscriber QoE at the sector level to

accurately detect when congestive collapse is about to occur so that the solution can take action to

prevent it. The ability to remain “locked” onto the variable point of congestive collapse that typifies

mobile networks makes QualityGuard the only accurate method available in the industry.

Subscriber Mobility Awareness

The solution also maintains visibility of which subscribers are attached to which access network

resources at any particular point in time. Subscriber mobility awareness is needed to maintain the

correct association between a mobile subscriber and the access segment to which they are attached.

Without it, policy control consists of blind swings and best guesses with unverifiable impact and

questionable regulatory compliance.

By integrating with a RAN probe solution, such as RADCOM's QiSolve solution, Fairshare utilizes real-

time subscriber location indicators to precisely maximize network utilization and QoE. Sandvine's

empirical data demonstrates that traffic management products without real-time location awareness

may "lose" up to 20 percent of users in an hour, and mismanage traffic as a result. Figure 6 shows the

architecture of the solution for UMTS mobile networks:

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Figure 6 – Sandvine Fairshare Traffic Management with QualityGuard for UMTS Networks

Sandvine recently tracked the movement of over 20,000 mobile data subscribers in a major North

American city to understand and predict sector-based congestion. Over the course of the hour, roughly

20 percent of users were still engaged in the same data session but had moved to a second cell sector.

No two sectors have an identical congestion profile at a moment in time. Competing traffic

management products identify the initial location of subscribers' connections but may "lose" 20% of the

users, thereby misapplying policy due to erroneous location information.

LTE Networks In LTE networks, Fairshare Traffic Management measures subscriber QoE at the eNodeB level to

accurately detect when congestive collapse is about to occur so that the solution can take action to

prevent it. The solution integrates with the S1 interface for sector-level congestion awareness, as

shown in Figure 7.

Figure 7 – Sandvine Fairshare Traffic Management with QualityGuard for LTE Networks

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Automatic Calibration and Location Hierarchy As mentioned in the previous section, SNMP provides an automated way of retrieving the location

hierarchy of edge resources in cable networks, while in mobile and DSL networks no such mechanism

exists. QualityGuard includes a Location Hierarchy feature that takes the effort out of mapping the

edge network topology by automatically provisioning it into the solution. This feature maintains a

flexible configuration while precisely identifying and labeling multiple complex location hierarchies. It

automatically enables separate shapers, controllers, and associated tuning parameters at every level of

every location type for every access type, including multiple access types in a converged network.

In addition, the Automatic Calibration feature allows QualityGuard to automatically “learn” the

optimal latency benchmark value achievable at each individual access resource for any network type,

fixed or mobile, based on observed measurements. Operators still have the option of manual fine-

tuning, but the ability to provision an initial, location-specific setting automatically further reduces

effort while embedding dynamic QualityGuard accuracy in every access network resource. Figure 8

shows an example of how the automatic calibration feature works in LTE networks.

Figure 8 – Automatic Calibration in LTE Networks

Verifying Results – QualityWatch Congestion Reporting Using reports generated by a focused set of congestion-related business intelligence called

QualityWatch, and driven by Sandvine’s standard reporting interface, Network Demographics, the

following three graphical reports demonstrate the positive effect of QualityGuard.

Figure 9 shows the net effect of QualityGuard on Layer-7 OTT bandwidth for a resource experiencing

massive congestion problems. When web browsing traffic begins to increase and real-time subscriber

QoE falls below a configured benchmark, QualityGuard shapes the bulk transfer traffic of subscribers

currently contributing to the congestion condition while creating capacity for the other 95-99% of users

also attempting to use the resource.

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Figure 9 – Effect of QualityGuard on bandwidth

Figure 10 shows QualityGuard’s effect on latency in the form of aRTT measurements, and Figure 11

shows the effect on the calculated quality score.

Figure 10 – Effect of QualityGuard on latency

Figure 11 – Effect of QualityGuard on quality

Fairshare enforces

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Predicable and Verifiable Cost-Savings With the ability to accurately track subscriber location in any access network to the lowest resource

granularity, and then detect and prevent congestion based on real-time subscriber QoE, it is possible to

precisely predict the cost-savings associated with the extension of a resource’s lifetime. Fairshare has

a soluton payback period of just a few months, and generates millions of dollars in savings over the

first few years.

Figure 12 – Return on investment analysis – Fairshare Traffic Management with QualityGuard

Some operators choose to take the money they would have spent on extending the access network and

bank it to generate interest or use their cost savings for additional network enhancements, such as

expanded fixed or mobile coverage.

Rural North American DSL operator Tier 1 North American Cable operator

Tier 1 Western European Mobile operator

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Conclusions It is clear that, during times of congestion, the most precise means of managing congestion in the

access network is a closed loop response system that uses real-time quality inputs to maintain the

optimal target goodput for an access resource to deliver good QoE to the vast majority of subscribers.

Only by linking an accurate trigger with a precise management policy can a CSP ensure that the goals

of congestion management – whatever they are – are achieved. Fairshare Traffic Management with

QualityGuard offers operators the technological capability to pull ahead of their competition with

predictable and verifiable cost-savings through a solution that also preserves subscriber QoE.

Summary of Detection Techniques Congestion management should only be applied when there is actual congestion on the network.

Consequently, an effective congestion management solution relies upon an accurate detection

mechanism to trigger the actual management policies.

Detection Technique Accuracy Explanation

Time of Day Extremely Low

Time of day as a trigger predicts that congestion occurs at times when it may not be happening. In certain locales, management policies may implemented in violation of network neutrality principles specified by regulatory law. At best, the approach is not narrowly tailored, and can be out of proportion by managing traffic when there is no congestion.

Concurrent User Thresholds

Low

Measuring concurrent users makes an assumption that the network is congested without actually verifying the case. The correlation between number of users and the presence of congestion can be very weak.

Bandwidth Thresholds Medium

There is an assumption that bandwidth automatically causes a degradation in quality of experience. While overwhelming bandwidth does have a high correlation with lowering QoE, it is entirely possible for a link or resource to be at or close to maximum capacity without causing subscribers to suffer lowered QoE. Critically, though, there is an underlying assumption that link/resource capacity has a fixed maximum, but that is not the case either for mobile access networks or for fixed access networks. As a practical result, it is impossible to pick a threshold that is guaranteed to be below the point at which congestive collapse begins.

Subscriber Quality of Experience

Extremely High

The best way to trigger congestion management is with a direct measurement of congestion itself. However, there is currently no standardized metric to measure detection. The next best thing is to measure a metric that is proven to have direct correlation with network congestion – ideally a metric that is caused by the congestion, rather than vice versa. Access round trip time (aRTT) is such a metric. Not only is aRTT known to increase dramatically when congestion is present, but it also has high correlation with subscriber assessments of quality of experience.

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Today, access round trip time provides the best possible congestion detection mechanism. When implemented in real-time, on a per-link basis, such an approach tells an operator precisely where, when, and for whom congestion is manifesting.

Summary of Management Considerations Since congestion is a result of overwhelming demand, the congestion itself cannot technically be

managed away. Instead, CSPs define how congestion manifests on the network. It is up to the CSP,

then, to determine and define the factors that are taken into account by a management policy.

Management Consideration Explanation

Defining Application Priorities

Some applications are more tolerant than others regarding latency – that is, they continue to deliver a high QoE even when latency is present - and this gives a convenient framework by which to define what gets prioritized access to limited network resources during times of congestion. To prevent application starvation (and the negative impact to QoE that result) the solution should be support a minimum rate capability.

Minimizing Negative Subscriber Impact

Since congestion management seeks to achieve a perfect balance between maximizing an access resource’s lifetime and maximizing QoE for the greatest number of subscribers, the goal here is (typically) to impact the fewest subscribers possible during a management period. The most effective predictor of congestion contribution is very short term usage data. Using the short-term usage history of users targets the true contributors to congestion for a fair, proportionate, and application-agnostic (where desired or required) solution. To be effective in the face of dynamic demand, this “short-term heavy user” category of subscribers must be updated as the clock advances.

Maximizing Precision

In combination with application and application category criteria, and precise selection of subscribers, topology awareness ensures that the highest positive impact is gained at the cost of impacting the fewest number of subscribers for the shortest amount of time. In mobile networks, the solution must be subscriber-mobility aware; that is, it must have a real-time (or near real-time) knowledge of subscriber location, otherwise management policies will be needlessly applied and will have little positive effect.

Policy Enforcement

The congestion management policies themselves take many forms (e.g., prioritization/de-prioritization, shaping and rate-limiting, weighted fair queues, etc.) and can also be enforced by multiple devices working towards the same goal. There are a range of strategies that can be part of an effective congestion management solution, and informed CSPs can work with a vendor to determine the best approach for a particular network.

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Related Resources In addition to the resources footnoted throughout this document, please consider reading the Sandvine

whitepaper Network Congestion Management: Considerations and Techniques.

Additionally, you might find the whitepaper Reasonable Network Management: Best Practices for

Network Neutrality to be helpful.

Finally, you can learn more about Sandvine’s activities regarding network neutrality, including our

public commentary, at https://www.sandvine.com/trends/network-neutrality.html

Invitation to Provide Feedback

Thank you for taking the time to read this technology showcase. We hope that you found it useful, and

that it helped you understand the value and capabilities of our QualityGuard congestion management

technology.

If you have any feedback or have questions that have gone unanswered, then please send a note to

whitepapers@sandvine.com

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