20150402_Testing LTE Network Performance for New Service Requirements_JDSU

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Testing LTE Network Performance for New Service Requirements

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Today's Presenters

Patrick Donegan

Chief Analyst

Heavy Reading

Assaji Aluwihare

Director of Product Management,

Assurance Solutions, JDSU

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Why The Mobile Network Still Isnt Meeting Expectations

13%

16%

39%

45%

48%

39%

0% 20% 40% 60% 80% 100%

Outage

Degradation

All networks & services are affected

Some networks & services are affected, but not all

Only one type of network or service is affected

Source: Mobile Network Outages & Service Degradations, October 2013

Outages & Degradations

Congestion the main cause of degradations

The well-known problem of bursty traffic hasnt gone away.

User impact in the first five years of mobile broadband era.

Risk of a far greater impact with new service innovation around delay-sensitive apps like VoLTE, interactive video & gaming.

Operator business objectives heavily reliant on these new services.

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Planning & Configuring The Mobile Network

Outputs Packet priortization Capacity planning Configuration settings Policy control Committed Information Rate (CIR), QoS Queues and

the Committed Burst Size (CBS) buffers.

Inputs Network Test & Measurement results. How the network has been performing.

A step change needed in outputs & inputs Phenomenal job being done More intelligent contention without congestion

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Testing Performance In The Backhaul An Increasingly Heterogeneous environment

L2 & L3 protocols

Different physical layers

Multiple hops

Different architectures

Third party wholesalers

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Traditional Performance Monitoring Does Not Reflect User Experience

Todays PM is synthetic and inadequate PM methods are single technology, networks are

heterogeneous

End-to-end tests often only provide a roundtrip view

Synthetic tests must be designed into the network

Samples are at fixed time slots, do not adapt to real traffic

Pre-defined test windows often miss transient issues

Traditional service management is inadequate Only uses statistical data from network elements

May only have data from synthetic test based tools

RFC 2544

Y.1564

Y.1731

TWAMP

End-to-end visibility requires correlation of huge amounts of data

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Long Traffic Samples Intervals Disguise Smaller Bursts

0

50

100

150

200

250

300

Link Total Bandwidth - Downlink (Mbps)

DateTime1 (1 secs)

DateTime2 (30 secs)

DateTime3 (60 secs)

DateTime4 (300 secs)

1-15min sampling intervals causes smoothing of QoSimpacting traffic

Limits visibility of bursts

Critical for traffic engineering, shaping and policing

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Lack of Segmentation Delays Trouble Resolution

Modern networks have complex aggregation schemes hub and spoke

End-to-end tests do not reveal performance at each hop

Problem isolation could take hours or even days, involving expensive dispatches

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Segmented Performance Monitoring With Live Traffic

Hop-by-hop visibility into performance

Granular view of utilization finds bursts

Must meet MEF compliant standards-based testing

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The Value of Segmentation and Performance Monitoring with User Traffic

Live subscriber performance metrics across routes, by circuit, DSCP value, or service

Passively monitor application performance

Measure to rate of traffic not fixed timeslots

Find network hops and QoS values with excess one-way delay or jitter in real time

Capture packets for deep analysis

Detect and correct or optimize worst hops contributing to poor E2E performance

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Use Sampling Rates That Adapt to Traffic Rates

Traditional periodic sampling leaves gaps

Real-traffic based monitoring adapts the number of samples to the data rate

Visibility into true performance is preserved

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High Resolution Sampling Provides Visibility into Micro Bursts

Model networks without overly averaged utilization

Find burst by seeing live traffic: 1 second metrics

100ms micro-burst analysis

Real Example: Micro-burst peak observed at 420% of network element metric

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Use Case: Find Bandwidth Utilization on Key Circuits

Monitor bandwidth utilization:- Total bandwidth used- Bandwidth by DSCP class

For each path with aggregated traffic to and from eNodeBs

For groups of paths:- a group of traffic classes- a group of eNodeBs in a region- a group of eNodeB attached to a specific RNC

Aggregate groups up to regional or all network level (management dashboard)

Compare current used BW with baseline obtained from turn up tests

Understand usage trends

Save OPEX

Backhaul can be 26% of operator OPEX*

Identify opportunities to consolidate backhaul

Identify opportunities to re-engineer circuits

Save OPEX

Backhaul can be 26% of operator OPEX*

Identify opportunities to consolidate backhaul

Identify opportunities to re-engineer circuits

*Source: Amdoc 2013

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Conclusion: The Benefit to Operators

Save OPEX on backhaul

Understand and optimize backhaul links for actual throughput and QoS requirements

Find misconfigured network elements

Identify, unexpected, superfluous traffic

Measure at true rate for shaping and policing (100ms vs mins)

Ensure all third party backhaul SLAs are being met

Accelerate service deployment, minimize impact on subs, resolve issues faster

Understand how new services impact existing service performance of real subscriber traffic vs synthetic traffic

Ensure quality of low-latency services like VoLTE, in real time

Avoid problems such as microbursts; measure real traffic loads, avoid under sampling with synthetic traffic

Detect QoS problems that could create costly issues such as customer dissatisfaction due to calls drops

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Q&A