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Technology Vision 2020 Personalizing the Network Experience

FutureWorks Nokia White paper

Technology Vision 2020 May 2014

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CONTENTS

Executive summary 3Why personalizing the network experience matters 4Optimize network, experience and business simultaneously

6

Use cases 7Use case – predictive performance 7Use case – real time customer care 8Use case – applications performance 9Use case – value based Quality of Experience 10

Driving actions at the speed of the user 11Signaling data tell us much about the user status 12Turning vast amounts of data into immediate business value

13

Telco scale big data technology 14Conclusion 15

FutureWorks

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Executive summaryAsk a typical mobile broadband operator what their key optimization problem is from a business perspective and they may well answer: How do I provide the best experience for my subscribers, while at the same time utilizing my limited network resources well and maximizing my revenues?

In reality, today’s networks are mostly a case of “one size fits all”. Exponential traffic growth is not matched by revenue growth. High average revenue per user (ARPU) subscribers churn due to poor network experience. All traffic, whether high or low value, is treated equally. Unfortunately, such mismatches are likely to worsen as many more applications, devices and sensors become available.

By “Personalizing the Network Experience” many operators can solve their major optimization problem - aligning resources, revenues and experience. To do this, a telco-scale system is vital to enable all signaling data across the network to be captured in real-time, and then correlated with customer and business data to allow immediate action. Such an approach would enable the network to accurately predict what will happen next and then act on these predictions, even down to the level of an individual connection. In practical terms, the system would, among other things, be able to:

• Automate and correct in real time any faults that are degrading services for subscribers

• Ensure a consistent video experience by using predictions for a high value user traveling a specific route

• Dynamically adapt the network to sudden changes in use

• Dynamically alter policy and radio settings according to user value and network load

Analyzing the colossal amount of raw network data and turning it into immediate business value can only be achieved with big data technologies applied to telco environments and enhanced by machine learning and machine reasoning . Nokia is running several innovation projects to realize the personalized network experience. For example, a FutureWorks innovation project, overcomes the most critical challenge of telco big data - scaling up processing capacity in line with the growing volume of data. The project uses parallel computing and a virtualized architecture to handle massive volumes of telco big data in real time, with no theoretical capacity limit. It has already processed one million messages per second, reflecting the peak hour data volume of a real 3G network with 10 million subscribers.

FutureWorks

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Why personalizing the network experience mattersNetwork resources, revenue and customer experience need to be balanced in order to deliver data profitably to subscribers. Personalizing the network experience is vital to achieve this.

Some fundamental changes in the market are encouraging operators to adopt a personalized network experience approach.

The first is the increasing competition for scarce network resources by different data streams. Rapid growth of mobile broadband will lead to a 1000-fold increase in traffic within ten years. This growth will be accompanied by more diverse requirements driven by the changes in devices, throughput and performance needs. The pressure on network resources will explode as billions of subscribers demand high quality network performance and millions of different applications become available. Personalization can help to allocate network resources in the best way.

Secondly, the investment needed to build the capacity required for exponential traffic growth is not matched by increasing revenues. Although the overall mobile communications market (including operator services, applications, advertizing, content, devices and infrastructure) is predicted to grow by 2020, network operators will face increasing pressure on their overall share of the market (Source: GSMA: VISION 2020). Personalization will help operators to gain the largest possible share of the overall global mobile communications market.

Technology Vision 2020

Technology Vision 2020 focuses on enabling mobile broadband networks to profitably deliver 1 gigabyte of personalized data per user per day by 2020. Technology Vision 2020 comprises six technology pillars:

• Enabling 1000 times more capacity to meet accelerating data demand in traffic hot spots

• Reducing latency to milliseconds to prepare for the applications of the future

• Teaching networks to be self-aware and simplify network management by extreme automation

• Personalizing network experience to enable the business models of the future

• Reinventing telco for the cloud to create on-demand networks that are agile and scalable

• Flattening total energy consumption despite accelerated traffic growth

FutureWorks

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Furthermore, network quality is increasingly important as a competitive factor. Nokia’s global Acquisition and Retention study reveals that network and service quality are responsible for 30 to 40% of customer loyalty, and are becoming increasingly important. In particular, high ARPU customers will switch operator if they feel they are getting inadequate quality. Personalization can reduce churn by matching user value and expectations with network and service quality.

Operators typically seek to provide the best customer experience, ensure network resources are well utilized and maximize revenues - all at the same time. In reality, networks are mostly “one size fits all”, where the exponential growth of traffic is not adequately monetized, where high ARPU customers churn due to poor network quality and where both high and low value traffic are treated with the same priority.

“Personalizing the network experience” provides a new way to differentiate customers, traffic and the delivered services in order to solve the key optimization problem.

Fig. 1: Aligning resources, revenues and experience – to increase operator profitability

Differentiate the traffic

Differentiate the offering

Differentiate the customers

Customer Experience

Revenue Network resources

Personalize network

experience

FutureWorks

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Optimize network, experience and business simultaneouslyNetwork elements, devices, applications, users and revenues generate massive amounts of data every second. By using these data sources to support their decision-making and action-taking, operators can optimize network performance, business goals and customer experience simultaneously.

In January 2014, Nokia launched “Teaching Networks to be Self-Aware” as part of its Technology Vision 2020. This encompassed a cognitive networks approach with automation, self-optimization, predictive operations, network coordination and decision support all working to improve network quality and productivity.

“Personalizing the Network Experience” is the next step in this cognitive networks approach and allows decisions previously taken by humans to be performed at machine speeds and scales. Machine learning and machine reasoning enables real-time optimization based on customer experience management and business guidelines.

The approach entails processing massive amounts of data from metrics, then analyzing the results to develop insights. This enables the operator to differentiate the network traffic, the customer and the offering to customers. Additionally, business guidelines are developed to govern the real-time implementation of customer experience management in the network.

Fig. 2: Optimize network, experience and business simultaneously - leveraging data and machine intelligence technologies

thgisnI

Metrics

Optimization

Metrics

Guidelines

thgisnI

Management

Metrics

Personalize network experience

Teach networks to be self-aware

Business

Experience

Network

Networks Devices Applications Users Revenue

Differentiate the traffic

Differentiate the offering

Differentiate the customers

Customer Experience

Revenue Network resources

Personalize network

experience

Self-management

FutureWorks

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To illustrate these possibilities, the next section describes some use cases, in which cognitive network techniques overcome specific business challenges and produce a better experience for the subscriber.

Use cases - predictive performanceA subscriber pays for high quality service to be able to listen to SpotifyTM music streaming while traveling. While delivering this higher service package to enable the subscriber to enjoy a consistent quality of experience (QoE), the operator must also avoid service deterioration for best effort subscribers in the face of limited network resources that create cell congestion in the area.

• Business guidelines: allocate scarce network resources while minimizing the impact on the experience of all users

• User: heavy user of audio streaming - listens to Spotify five hours a day, particularly when traveling to work in the morning

• Network: normally good but known to have a few cell sites that can become congested

The operator collects data on the network and user behavior for a period of time. The network realizes that the subscriber uses music streaming along the same route at about the same time each day. The operator can even predict when cell congestion will happen, based on long term measurements from network prediction algorithms.

One particular morning, foul weather is forecast which will lead to heavy congestion along the route. From immediate monitoring, the network can see the high value user, subscribed to a streaming service, is traveling through the area and streaming music.

The network sets a higher session priority for the user and reduces the scheduling frequency of subscribers using interactive, but delay-tolerant, services like Facebook and Twitter.

The user is able to pass through the congested area with break-free music playing at the same quality as always. Meanwhile, other users notice no change in their service since only delay-tolerant services for budget users were scheduled less frequently.

Long term data gathering allows networks to accurately predict network performance down to the cell level which, when combined with information on user behavior and subscribed service levels, allows the operator to sell and deliver higher service packages customized to users’ needs. The high value user gets what’s been paid for - a consistent streaming performance - while the operator is able to efficiently schedule other users in a way that doesn’t affect their quality of service.

Fig. 3: Predictive performance for services like Spotify

Predicted cell congestion

8:15 am

8:20 95%

8:25 85%

8:30 70%

User perspective

FutureWorks

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Use case – real time customer careA potential new revenue stream for operators is to offer higher level SLAs to corporations or high value users by monitoring user connections. This requires real-time customer care and is achieved by enabling the network to detect faults, even those affecting just a single user, and automatically correcting them before users call to complain.

• Business guidelines: maintain Service Level Agreement (SLA) Key Performance Indicators (KPIs) for corporate users

• User: high value user based on a corporate SLA with the operator

• Network: monitoring all calls in the network with triggers based on QoE issues of high value users

A corporate user is experiencing session drops while traveling along a particular route.

The corporation’s SLA calls for a real-time trigger with session information to be generated for analysis. Since the network is monitoring all calls, the QoE of other users in this area, as well as network KPIs are analyzed to discover whether the session drop is specific to the area, cell or radio frequency (RF) carrier, or to the user, application or device, or other fault.

After analysis against multiple data sources, the network determines that the packet connection is experiencing “hangs” and instructs the packet core to reset the connection and reconnect to fix the problem.

Using multiple data sources or “crowd sourcing” helps to determine that the problem is user specific, eliminating other possible causes and speeding up the resolution of the problem.

The SLA for the corporate user is maintained by spotting problems quickly using network data and solving them before other customers have cause to complain.

Fig. 4: Real time customer care

Quality monitoring of all network sessions

Session drops

Corrected

User perspective

FutureWorks

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Use case – application performanceSudden changes in traffic patterns can be created by shifts in the popularity of Internet content, such as viral videos. Operators need to ensure that the network continues to deliver a good experience to users despite these rapidly changing events (a video today can go viral within 24 hours).

• Business guidelines: maintain video quality for all users by reducing speeds, if necessary, to avoid stalling

• User: high value user and budget users stream the same YouTube video

• Network: monitoring all sessions and optimizing for video buffer depletion and congestion per subscriber segment

Users start to watch a suddenly popular YouTube video, which has taken off in the last days. An operator is concerned about video performance because this is a key competitive differentiator for its network.

Data from the network shows that a user’s video session, which started well, is being degraded by video buffer starvation. The operator needs to know whether this is because the user is in a bad coverage area, whether there are YouTube server issues due to sudden demand, or whether there is cell congestion or a network fault.

Data from several sources is analyzed - around the user at the cell level, compared to other users watching the same video, and YouTube network throughput, to determine the severity of the problem. Actions need to be taken quickly since YouTube videos are relatively short, on average around 3-4 minutes, stalling of video being far more disturbing for a user than lower quality.

Network analysis shows that this is a congestion issue in the cell and since the operator has adopted a business rule to always have good quality for video as a differentiator, the network enforces hard limits for minimum acceptable video data rates in that cell. In addition, delay-tolerant services are scheduled less often to give preference to video over best effort services. This ensures all users can watch the viral video without stalling.

The ability to make these kinds of experience optimizations comes from insights into network performance, application performance and usage, guided by business logic.

Fig. 5: Application performance for services like YouTube

Budget

High Value

B

A

User perspective

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Use case – value-based Quality of ExperienceAn operator wants to provide different QoE levels for users based on their value, even while cell level traffic changes.

• Business guidelines: match differentiated offering with service quality while optimizing the use of all network resources

• User: high-value user of video streaming, budget user of social media

• Network: cell with both LTE and 3G experiences morning and afternoon congestion

User A is high value, consuming high bandwidth services like video, particularly in the morning and evening while watching financial news on a smartphone. User B is a budget user who enjoys social media apps throughout the day.

Network traffic in the cell varies greatly, with congestion peaks in the early morning and late afternoon and a mid-afternoon lull.

The operator faces the challenge of handling the different users’ QoE needs as traffic peaks and dips in the cell throughout the day. The operator must optimize network capacity and quality across the heterogeneous layers to minimize CAPEX and OPEX, while continuing to meet demand.

During congestion, the network will automatically alter the policy and radio settings for all users, matching demand and quality according to user value. The high value user stays on LTE but the maximum data rate is capped during congestion and uncapped in non-busy hours. Meanwhile, the budget user stays on uncongested LTE cells and is handed onto 3G when the LTE cell becomes congested.

Handovers between LTE and 3G are based on the value of the user and the application being used versus the available capacity of the cell. Instead of blindly making handovers or changing schedule parameters, the network can intelligently make QoE decisions to account for the value of the user, application type used, and the ability to support services as cell load varies throughout the day.

Fig. 6: Value based Quality of Experience

Aggregated traffic 3G/LTE cell

Sample per user traffic in cell

Morning Afternoon

User AUser B

Busy hour fluctuations

User perspective

FutureWorks

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Driving actions at the speed of the userAll the use cases discussed in the previous section are time dependent, allowing rapid action to be taken to ensure users get a personalized level of service. Nokia envisions that networks will drive actions at the speed of the user, where network elements are supporting user actions instantaneously. For example, instant actions happen in the present or “now” which would be network attaches, handovers, etc. from milliseconds to seconds to support “reflexive” activities.

For periods of more than about 15 minutes, operators need to know how to predict future needs. Having an ultimate knowledge of network and subscriber behavior allows the use of predictive models to fix network problems. They can even be used to predict user behavior and adjust the network to serve a user before they arrive in a congested area, as in the streaming audio example.

Finally, between these two extremes is the ‘middle time space’ for dynamic adaption, covering actions from seconds to minutes, a period where three of the use cases are placed.

This is the new ‘sweet spot’ for moving the network to the speed of the users, allowing it to make immediate changes to personalize the network experience for the user in a real and tangible way.

The speed of action over wireless has progressed from weeks and hours and even to half an hour with the possibility of a quarter of an hour time period. However, the hurdles to making adaptations in times below 15 minutes, as in our use cases, requires an appreciation of the enormous amounts of data a wireless network produces.

Fig. 7: Driving actions at the speed of the user

Immediate “Dynamic adaptation”

Instant “NW reflex”

seconds minutes > 15 minutes

Predictive performance based user habits

Value based QoE

Application performance

Customer Care

t NOW

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Signaling data tell us much about the user statusTelecom networks generate not just big data, but colossal amounts of data every second. To put this into some perspective:

• The Visa credit card network hits bursts of 18,000 transactions/sec during peak shopping times such as Christmas

• Twitter had a peak in 2013 when it was processing 143,000 tweets/sec from 250 million users

• Whatsapp performs around 578,000 transactions per second for 450 million active users

• A wireless network for a country of 10 million people produces an astounding one million transactions per second

Every time a subscriber’s device interacts with the network, signaling data points are generated. Each user generates a unique footprint in the network, and through their combined activities, these users produce large volumes of data every second. Taking the entire subscriber base and network into account this adds up to a large volume and variety of data produced every second. Networks must check the data for the accuracy of what is happening, in order to enforce polices. To handle the “immediate time” and adapt to users’ actions as they occur, operators need to solve the big data problem. To be clear, big data in our context is not about the private messages and contents of the subscribers.

Fig. 8: In telco data is not big – it’s colossal and most of it is real-time

Verified for accuracy

Every second in real-time

Unique footprint

Massive network data

Wireless network

1,000,000 Transactions per second

Data

Twitter*

Visa*

143,000

18,500

WhatsApp*

578,000

Variety Velocity Veracity Volume

QoE equals user policy

Music most popular in the mornings

Video quality links to coverage

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Turning vast amounts of data into immediate business valueNokia advocates the use of big data and analytics to translate the volume, variety, velocity and veracity of big data into business value. It all starts with the network signaling data to support a user’s actions. The challenge is to build a system which can extract, expose and analyze the data stream.

1. To extract the signaling data, it first needs to be normalized into a format across all the different network elements

2. Then the data needs to be decoded and enriched to bring out events and counters

3. Using a real-time database or direct interface, the data is exposed to external apps using open interfaces, which support customization and continuous queries

4. Finally, the network uses pattern detection, insight creation and machine learning technologies to analyze and create actions.

This is an easy problem to solve for one user, 100 users or even 1,000 users, but to do this for every user in real-time at a rate of a million or more transactions per second, requires wholly new methods. The biggest problems start at the collection of data, where queue management must be simplified. Next, the use of cloud technology enables computing on demand as well as massive parallel processing of all the data streams. Finally, the system needs to be protected against data loss and faults in order to handle data at the extreme level of reliability demanded by telcos.

Building a telco scale data engine that can handle the variety, volume and velocity of data is not a simple engineering problem. It requires the application of novel ideas and methods.

FutureWorks

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Telco scale big data technologyThe challenge lies in the performance of the big data technology and its combination with analytics and automation. The design of Nokia’s “real-time time data processing engine” innovation project overcomes the most critical challenge of telco big data, which is to scale up processing capacity linearly as the volume of big data grows.

Nokia has been able to implement a system that resolves the bottlenecks, allowing the engine to handle massive volumes of telco big data in real time, and grow with the data volumes with a proven linear scalability.

Nokia has tested its real-time data processing engine to one million transactions per second, which meets its telco scale goals. The latest development is a field test phase in live customer environments. This engine can scale to even higher loads and Nokia will keep pushing its capabilities through operator collaboration.

Nokia combines high performance big data capabilities with analytics and automated action to allow dynamic QoE management. This means that the QoE of end users can be automatically optimized on the fly, according to insights about the network, the session and the user.

Nokia is already offering leading Customer Experience Management (CEM) and Self-Organizing Networks (SON) portfolios and as these innovations mature they will continue to allow faster, more intelligent decision making.

Fig. 9: Meeting the operational requirements to process telco big data

> Live customer network testing ongoing

> Innovation cooperation with big data technology leaders

> Built in multi-vendor capability

Scaling processing capacity with growing big data volume

Personalize the network experience

Parallel computing and virtualized architecture

Proven linear scalability

Tested up to 1 M transactions/sec

Compute power

Performance

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ConclusionNokia believes that with “Personalizing the Network Experience”, operators will be able to align data traffic growth, revenues and customer experience by changing from a conventional network model to actions at the speed of the user for profitable delivery. Personalizing the Network Experience is the fundamental approach to increase operator profitability:

1. Exponential growth of data traffic can better be monetized by differentiation of traffic, users and offerings.

2. Churn of high ARPU customers can be lowered as network and service experience accounts for 30 to 40% of the loyalty of mobile users.

3. Different traffic streams can be better handled according to their individual requirements. This will become ever more important as the variety of different traffic patterns increases with new applications such as machine-to-machine and HD video services.

Personalizing the Network Experience can be summarized as the nexus between network automation, CEM and predictive analytics.

Nokia is putting all these building blocks in place to create a personalization solution with cognitive networks to solve operator challenges and eliminate the “one size fits all” network model.

The data in the network is immense, rich and can unlock business value if processed for dynamic adaptation to user needs in less than 15 minutes or in timescales longer than 15 minutes for long term predictions.

Cognitive networks can be made a reality, as Nokia has solved one of the biggest hurdles for exposing the data. This technique is being used by more than ten innovation projects which Nokia is working on with operators. With its Technology Vision 2020, Nokia will continue to develop the technology enablers for Personalizing the Network Experience, which is good for delighting the user but even more essential for the future of mobile operators.

FutureWorks

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Visiting address: Karaportti 3, ESPOO, Finland Switchboard +358 71 400 4000

Product code C401-00985-WP-201405-1-EN

© Nokia 2014.

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