Data Driven Mobile Networks

Sachin KattiStanford University

Mobile traffic demand is skyrocketing

2000 2005 2010 2015 2020 2025

3.7 EB/month

30.6 EB/month

0.4 EB/month

< 1 PB/month

300 EB/month?

Richer content

New, smarter devicesPhones Smartphones Tablets Wearables

Audio, Text Web Video Virtual Reality

< 10 GB/month

Courtesy: Cisco VNI, 2015-2020

How will mobile networks meet this surging traffic demand?

Densification  will  bring  the  next  wave  of  capacity  scaling  in  mobile  access  networks

Spectral efficiency

Densification

Spectrum

SoftRAN: Enabling scalable densification

Operator Cloud

COTS Pods at cell sites with ARM/DSP

DSP

SoftRAN Platform

LTE, 5G, IoT ..

Servers, Switches, and I/O

Build mobile network out of COTS HW/SW

Macrocell

Picocell

Microcell

Macrocell

Picocell

Microcell

SoftRAN: Decoupled Control Plane

5

5G, LTE-A, WiFi User/Data Planes

SoftRAN Connectivity Substrate

DSP

Commodity  Servers,  Switches,  and  I/O

Macrocell

Picocell

Microcell

Macrocell

PicocellMicrocell

5G, LTE-A, WiFi Control Planes

SoftRAN Network OS

SDN’s dirty secret: How to build the control plane?

Densification à Complex Control Planes

For  densification  to  scale,  networks  need  to  proactively  manage  load,  interference  and  mobility  to  optimize  user  experience

Load (demand) and other factors (e.g interference, mobility) become highly dynamic

à need to be proactively managed

E.g: Cells can offload users to less-congested neighbors to balance load,

need to know expected user experience

Why is this challenging?

Existing approachesAggregate cell-level counters

blind to dynamism across time and users;reactive

Need to predict user performance every ~1sfor the best load distribution

ChallengeUser performance is hard to predictComplex function of many, potentially unknown and/or dynamic, network variables

Need  to  predict  user  experience  in  advance,hard  in  practical  networks  with  many  unknown/dynamic  variables

To packet core

13:00 14:00 15:00 16:00

05

1015

TIME

LOA

D (i

n M

B p

er s

econ

d)

13:00 14:00 15:00 16:00

05

1015

TIME

LOA

D (i

n M

B p

er s

econ

d)

0 10 20 30 40

01

23

4

USER #

LOAD

(in

MB)

Dedicated  fiber/micro

wave

Public  Internet

What do we need today to enable wide-scale densification?

Can we leverage a data driven approach to tackle this challenge?

without making invasive changes to existing network infrastructure

Predict user performance to know if and why user QoE will deteriorate before it happens

Coordinate across cells in real timeto proactively balance load, manage interference …

SoftRAN’s Network OS: ForeCData-driven control substrate

in dense mobile networks

How to use data driven control?

Network effects

Network state

Network actions

change causes

Networks  require  the  ability  to  monitor  and  forecast  network  effects  per  user/cell,  per  second,  potentially  based  on  network  actions

MONITORRight now, what is the

avg throughput of user/cell?resource utilization of user/cell?contention faced by user/cell?

FORECASTIn the next 1s, what will be the:

avg throughput of user/cell?resource utilization of user/cell?contention faced by user/cell?

PREDICT IMPACTIn the next 1s, what if:

handover users?admit/reject new users?

increase/decrease tx power?

throughputresource utilization

contention

handover useradmit/reject userinc/dec tx power

cell: bandwidth, #users, demand etc.user: serving cell, link quality etc.

ForeC: Analytics on ‘after-the-fact’ logs to monitor & forecast network effects

ForeC

user session logs

queries responses

Network control

Cells already expose usage stats per user session

Ericsson CTR, ALU PCMD

One report per event of the session, logged after the

eventsetup report, release report, traffic report,

radio measurements report, mobility report etc.

ForeC has been tested in diverse deployments

Football Stadium Metro Area

How does ForeC work?The internals of ForeC

What can ForeC do?and how well?

How can a network use ForeC?Load management in a stadium

How does ForeC work?The internals of ForeC

What can ForeC do?and how well?

How can a network use ForeC?Load management in Levi’s Stadium

futurepast now

EFFECTEFFECT

ACTION

How does ForeC forecast network effects?

STATE 2STATE 2STATE 1STATE 1

forecast causes predict

effect

ForeC’s approach:  Forecast  the  causes,  predict  the  effectDecoupling  simplifies  design  and  implementation,  also  works  quite  well!

What will be throughput of user A if handed over from cell 1 to 2

over the next 10 seconds?

forecast effect

decompose

The internals of ForeC

ANALYTICS LAYER

MODELING ENGINE

DATA LAYER: Data sanitization and organization

QUERY LAYER: Query parsing and response

FORECAST ENGINE

MONITORING ENGINE

PREDICTION ENGINE

Future effects

Future states

Currentstates

Currenteffects

Currentstates

OFFLINEPATH

ONLINEPATH

Current data

Past and current data

user session logs

queries responses

Proposed state change

How do we decompose an effect into its constituent causes?

ANALYTICS LAYER

MODELING ENGINE

DATA LAYER: Data sanitization and organization

QUERY LAYER: Query parsing and response

FORECAST ENGINE

MONITORING ENGINE

PREDICTION ENGINE

Future effect

s

Future states

Currentstates

Current

effectsCurrent

states

OFFLINEPATH

ONLINEPATH

Current data

Past and current data

user session logs

queries responses

Proposed state

change

Use  domain  knowledge  to  formulate/hypothesize  causes,  use  learning  tools  to  test  significance  and  discover  relationships

Spectral efficiencybits per resource element

Throughputbits per time

Cellbandwidth

Cell contention

User&totaldemand

Resource allocation rateresource elements per time

?

?Σ Active

users

Σ Pktrate, size

Rank

MCSCQI

X

0 20 40 60 80 100

-6-4

-20

24

6

USER SESSION #0 20 40 60 80 100

-6-4

-20

24

6

USER SESSION #0 20 40 60 80 100

-6-4

-20

24

6

USER SESSION #

58% error 40% error 12% error

00:00 01:00 02:00 03:00 04:00 05:00

1520

2530

3540

00:00 01:00 02:00 03:00 04:00 05:00

1520

2530

3540

How do we forecast the dynamic causes?

ANALYTICS LAYER

MODELING ENGINE

DATA LAYER: Data sanitization and organization

QUERY LAYER: Query parsing and response

FORECAST ENGINE

MONITORING ENGINE

PREDICTION ENGINE

Future effect

s

Future states

Currentstates

Current

effectsCurrent

states

OFFLINEPATH

ONLINEPATH

Current data

Past and current data

user session logs

queries responses

Proposed state

change

Test  for  seasonality  and  trend,  use  appropriate  version  of  ARIMA  time-­‐series  forecasting

Number of users on a cell

-20

2060

data

-20

2060

seasonal

-20

2060

trend

-20

2060

0 20 40 60 80 100

remainder

time

ActualForecast

4% error

How does ForeC work?The internals of ForeC

What can ForeC do?and how well?

How can a network use ForeC?Load management in Levi’s Stadium

ForeC can predict network effects per user, per second, aggregates

Primitive Example  query Medianerror

Basic  primitivesPREDICT  effect  per  userbased  on  (changes  in)  network  state  

Throughput  of  user  A  if it  were  on  cell  2  instead  of  1? 8.5  %

PREDICT  effect  per  user  per  second Throughput  of user  A  over  the  next  1s? 13.0  %

Extensions  to aggregates  over  space  and  time

PREDICT  effect per cell,  sector,  network… Average  throughput  of  cell  1  in  the  next  1s? 4.0  %

PREDICT  effect  per  10s  of seconds,  minutes… Average  throughput  of  cell  1  in  the  next  5s? 1.7  %

Network  effects:  throughput,   resource  utilization,   contention…&  any  function   of  themNetwork  actions:  handover  user,  admit  new  user,   increase/decrease  transmit  power

How does ForeC work?The internals of ForeC

What can ForeC do?and how well?

How can a network use ForeC?Load management in a stadium

ForeC lets operator monitor, predict and optimize network KPIs

[SWITCH  TO  TABLEAU]MONITOR  – FORECAST  – OPTIMIZE

Dash  1:  Operators  can  monitor  network  KPIs  at  a  fine  resolution  in  space  and  time  (show  “ACTUAL”  KPI  for  Band  700,  1900,  2100)[can  also  define  new  KPIs  to  monitor]

Dash  2:  Operators  can  predict  these  KPIs  to  manage  load  proactively  (show  “ACTUAL”  KPI  and  “FORECAST”  KPI  for  a  cell)

Dash  3:  Operators  can  choose  the  best  load  management  to  optimize  these  KPIs  (show  “ACTUAL”  and  “OPTIMIZED”  KPI)

Summary & Takeaways

• ForeC enables  data-­‐driven  network  optimization– fine-­‐grained  analytics  can  enable  a  host  of  novel  applications  and  services  – app  delivery,  spot  bandwidth…

• Just  the  beginning  …– SDN  is  helpful  to  open  up  the  interfaces,  but  how  do  we  use  the  interfaces?

– Modern  networks  are  too  complex  for  humans  to  figure  out  how  to  control  &  optimize  them

– Data  driven  networks  that  automatically  optimize  user  experience

Research Questions

• Can  we  track  and  predict  the  QoE of  each  user  in  the  network  in  real  time?– Tailored  to  each  application?

• Can  we  learn  the  complex  dependencies  for  applications  across  the  various  elements  of  the  network?– Radio,  packet  core,  compute  cloud  …

• Can  we  build  what-­‐if  engines  for  networks?– Emulate  accurately  the  impact  of  any  control  decision  before  taking  it?

• Can  we  automatically  learn  the  right  control  to  apply?