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Cellular Network Performance Measurement - PPT Presentation

Class Presentation for CS 234 Advanced Networks b y Pramit Choudary Balaji Raao amp Ravindra Bhanot Group 18 Instructor Professor Nalini Venkatasubramanian 05102012 ID: 337464

subscriber traffic dynamics base traffic subscriber base dynamics subscribers paper network load stations station mobility day nat cellular fig

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Slide1

Cellular Network Performance Measurement

Class Presentation for CS 234 - Advanced Networks

b

y

Pramit

Choudary

,

Balaji

Raao

&

Ravindra

Bhanot

(Group 18)

Instructor: Professor

Nalini

Venkatasubramanian

05/10/2012Slide2

Papers considered

Paper 1: Understanding Traffic Dynamics in Cellular Data Networks by U. Paul, A. Subramanian, M.

Buddhikot

, S. Das, IEEE INFOCOM 2011, Shanghai, ChinaPaper 2: An Untold Story of Middleboxes in Cellular Networks, SIGCOMM 2011, Toronto, Ontario, Canada (NOTE: Please refer to the relevant papers listed above in place of ‘paper 1’ or ‘paper 2’ found in the presentation slides.)

2Slide3

Each claim to cater different data rates, ranges in operation, needs of end user/application, energy savings, etc using different protocol designs, business strategies, network deployments and many more.

Background - Internet/Data Access?

Dial-up connection

Broadband (DSL, Cable Internet, Fiber Optics)

Wi-Fi (IEEE 802.11 standard) &

WiMAX

(IEEE 802.16 standard)

Mobile Broadband using 2.5G, 3G, 4G technologies

3Slide4

Background - Cellular Networks and interconnecting subsystems

4

4G: Fourth generation of cell phone mobile communications standard

3G: Third generation of cell phone mobile communications standard

Femtocell

: Small cellular base station designed for use in a home or small business

IMS: IP Multimedia Subsystem, used to provide mobile and fixed multimedia services

Image courtesy: radisys.comSlide5

Background - Broadband Cellular Networks

E.g. HSPA

- Mobile telephony protocols used in 3G cellular networks for mobile data access.Broadband cellular access becoming most common and pervasive world-wide.Fueled by introduction of user-friendly smart phones, notebooks, tablets, eBook readers.5Slide6

Background - A look at

smartphone

technology

6

Courtesy: Technology Review, Published by MIT, May 9

th

2012Slide7

Background - Broadband Cellular Networks

Has led to innovative & flashy mobile applications like gaming, video streaming, social networking, etc.

Use of several and various types of

middleboxes to manage the scarce resources (because same resources are shared mostly) in the network and to protect them e.g. Network Address Translation (NAT) boxes, firewalls, etc.

7Slide8

Background - On usage of

middleboxes

8

Many times, cellular network

middleboxes

(deployed by carriers like AT&T, T-Mobile) and mobile applications (application developers) – managed independently.

Knowledge mismatch -> End-to-end performance degradation, Increase in energy consumption, Introduce security vulnerabilities.

E.g. Carrier setting aggressive timeout for inactive TCP connections in the firewall and disrupting long lived and occasionally idle connections maintained by applications like instant messaging, push-based email, etc.

Need for understanding the effects of

middleboxes

in cellular network.

Paper 2 specifically focuses on NAT boxes, their policies & firewall and its policies.Slide9

Background - Broadband Cellular Networks (Contd.)

Expectations in increase in the volume of data seen exponentially.

Supporting such an increase requires good understanding of traffic dynamics and its impact on resource allocation on the service provider’s network.

Leading to better resource planning, network designs, spectrum allocation and energy savings.

9Slide10

Background - Broadband Cellular Networks (Contd.)

For some exciting numbers, refer to a white paper by Cisco on global mobile data traffic forecast for 2011-2016:

http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-520862.html

10Slide11

Paper 1 - Short Summary

Discuss traffic dynamics specific to 3G cellular networks.

End user perspective: Study subscriber traffic patterns, number of distinct base stations visited by subscribers, relate mobility and traffic, subscriber temporal activity & relate subscriber activity and traffic.

Network component perspective: Study aggregated load at base stations, base station load distributions, spatial characteristics, temporal characteristics and spatio-temporal characteristics of network load at base stations.

11Slide12

Paper 1 - Short Summary (Contd.)

Provide implications on the measurements and observations made.

Test conducted in

2007 for a week over a US nation-wide network with thousands of base stations and with entire subscriber base (order of hundreds of thousands i.e. close to a million).Performed measurements on all generated data packet headers (not including payloads) and on signaling & accounting packets.

12Slide13

Paper 1 - Subscriber Traffic Dynamics

Subscriber Traffic Distribution:

13

KEY OBSERVATIONS

Heavy users: Users who generate as high as 10GB of traffic per day (10^5 times median).

Light users: Users who generate less than 1KB per day.

CDF shifts left over weekends.

INFERENCE

Less traffic on weekends relative to traffic on working days.

Fig. CDF of total traffic volume per subscriber per day.Slide14

Paper 1 - Subscriber Traffic Dynamics

Subscriber Traffic Distribution (Contd.):

14

KEY OBSERVATIONS

1% of the subscribers create more than 60% of the daily network traffic.

10% of subscribers create 90% of the daily network traffic.

INFERENCE

Imbalance in network usage with few subscribers (10%) using much of the network resources.

Fig. CDF of normalized traffic volume over the percentage of subscribers per day.Slide15

Paper 1 - Subscriber Traffic Dynamics

Implications of Subscriber Traffic Distribution

:

An unlimited data plan with flat rate pricing is not efficient both from the carrier’s perspective and subscriber’s perspective.CDF graphs shown in previous two slides can be used to create a ‘tiered’ rate plan for data.Tiered rate plan deals with providing different pricing options based on data usage.

15Slide16

Paper 1 - Subscriber Traffic Dynamics

Implications of Subscriber Traffic Distribution (Contd.)

:

4) To alleviate the problem of high volume subscribers creating poor experience for other subscribers, high volume subscribers can be provided with some incentives.5) Paper doesn’t consider optimal pricing schemes based on subscriber usage and network capacity. It only provides heuristic implications for subscriber traffic distribution.

16Slide17

Paper 1 - Subscriber Traffic Dynamics

Subscriber Mobility (i.e. Base Stations Visited):

17

KEY OBSERVATIONS

Distribution similar on weekdays and different on weekends.

60% of users are stationary (i.e. constrained within a cell) and over 95% of users travel across less than 10 base stations in a day.

Highly mobile users (who visit more than 50 distinct base stations in a day) are about 0.01%.

Fig. CDF of number of distinct base stations visited by a subscriber each day.Slide18

Paper 1 - Subscriber Traffic Dynamics

Subscriber Mobility (i.e. Base Stations Visited):

18

INFERENCE

Tendency of lesser degree of mobility on weekends.

I

n terms of the number of distinct base stations visited, the

o

verall mobility is low.

Fig. CDF of number of distinct base stations visited by a subscriber each day.Slide19

Paper 1 - Subscriber Traffic Dynamics

Subscriber Mobility (Radius of Gyration):

19

Fig. CDF of radius of gyration.

Radius of Gyration is the linear size occupied by a subscriber’s trajectory. Requires certain duration of time (t) for computation from subscriber’s trajectory.

It is basically a root mean square value.

Calculated with respect to the center of mass point of the user’s trajectory.Slide20

Paper 1 - Subscriber Traffic Dynamics

Subscriber Mobility (Radius of Gyration):

20

Fig. CDF of radius of gyration.

INFERENCE:

Shows the low level of mobility of majority of subscribers (half of them).

KEY OBSERVATIONS:

53% of subscribers are practically static and almost 98% of the subscribers have radius of gyration less than 100 miles.Slide21

Paper 1 - Subscriber Traffic Dynamics

Subscriber Mobility (Radius of Gyration):

21

Fig. Radius of gyration versus duration of computation for subscribers categorized into 4 groups according to their final

rg

at the end of seven-day period.

KEY OBSERVATIONS

:

Radius of gyration on an average comes to a saturation point in just few days (based on no. of hours).

Saturation indicates that some sort of boundary in the movement area has been reached. Quick saturation measured in terms of ‘return probability’ in next slide.

Users with larger radius of gyration need longer time to saturate.Slide22

Paper 1 - Subscriber Traffic Dynamics

Subscriber Mobility (Radius of Gyration):

22

Fig. Probability distribution of time to returning to the same location.

KEY OBSERVATIONS

:

Distribution has peaks at 24

th

, 48

th

and 72

nd

hours.

INFERENCE

:

Periodic nature of human mobility with a 24 hour period (like coming back home) and tendency to return to the same location periodically. This infers the saturation of radius of gyration.Slide23

Paper 1 - Subscriber Traffic Dynamics

Subscriber Mobility (Radius of Gyration):

23

Fig. Probability of finding a subscriber at different locations that are ranked on the basis of their frequency of visits. Shows four categories of subscribers who visit 5, 10, 30 and 50 distinct base stations.

KEY OBSERVATIONS

:

Location with rank, L = 1 indicates the most visited base station for a subscriber.

Subscribers spend 30% of their time in the top two preferred locations.

INFERENCE

:

Subscribers are found at their favorite location with high probability even there is high mobility among them.Slide24

Paper 1 - Subscriber Traffic Dynamics

Inferences on Subscriber Mobility so far

Large fraction of subscribers have limited mobility (roughly half of them are static moving within just 1 mile).

Subscriber mobility also exhibits periodic behavior with high probability of returning to same base station at same time of the day.Overall mobility is predictable.

More mobile users tend to generate more traffic.

24Slide25

Paper 1 - Subscriber Traffic Dynamics

Implications on Subscriber Mobility

Idea of caching content and delivering it to subscribers who exhibit a predictable mobility behavior - Innovative cloud-based content delivery applications.

Optimizing the location based services and targeted ad-services through predictable mobility pattern.

25Slide26

Paper 1 - Subscriber Traffic Dynamics

Relating subscriber mobility and traffic they generate:

26

Fig. CDF of traffic generated per day by subscribers

based on number of locations (base stations) visited

in a day.

Fig. CDF of traffic generated per day by subscribers

based on radius of gyration

.Slide27

Paper 1 - Subscriber Traffic Dynamics

Relating subscriber mobility and traffic they generate:

27

KEY OBSERVATIONS FROM PREVIOUS SLIDE

:

Though the plot lines appear similar, they differ in traffic volume for different number of base stations visited and traffic volume for different radii of gyration.

INFERENCE

:

More traffic is generated by more subscribers.

Median traffic generated by subscribers in the highest mobility category is roughly twice that of the subscribers in the lowest mobility category.Slide28

Paper 1 - Subscriber Traffic Dynamics

Implications relating to subscriber mobility and traffic they generate

:

1) Planning resources dynamically based on traffic generated by subscribers specific to subscriber timings of movements.2) Spectrum management based on timings of traffic generated and in different cells.

28Slide29

Paper 1 - Subscriber Traffic Dynamics

Subscriber Temporal Activity:

29

KEY OBSERVATIONS

About 28% of the subscribers generate traffic only in single hour during the peak hours.

A typical subscriber (i.e. median) is active in the 4 different hours during the peak hours. (Consider a straight line -50% line- across the graph)

Fig. CDF of number of hours among peak hours (8 AM to 8 PM) subscribers generate traffic.

It is the number of days (or hours) in a week (or in a day), subscribers generate traffic.Slide30

Paper 1 - Subscriber Traffic Dynamics

Subscriber Temporal Activity:

30

INFERENCE:

Large fraction of subscribers generate traffic only in few hours within a day.

That is, more of number of subscribers generating traffic is for a lesser duration of time (for the week / for a day).

Fig. CDF of number of hours among peak hours (8 AM to 8 PM) subscribers generate traffic.Slide31

Paper 1 - Subscriber Traffic Dynamics

Subscriber Temporal Activity:

31

KEY OBSERVATIONS:

Median usage is about 100 sec.

For all 24 hrs (86,400 sec), very few i.e. less than 1% of subscribers use the radio channel.

Weekend usage again lower compared to weekday usage.

Fig. CDF of airtime among subscribers.

Airtime

: Amount of time a subscriber holds onto a radio channel regardless of whether it communicates or not.Slide32

Paper 1 - Subscriber Traffic Dynamics

Relating subscriber temporal activity and traffic they generate:

32

KEY OBSERVATIONS:

A typical heavy user appears in 4 to 6 different hours during peak hours in the days they generate traffic.

Fig. CDF of occurrence for heavy users (within top 5000 in

atleast

one day in the week with regard to traffic) in peak hours.

INFERENCE

:

Most heavy users are actually quite sporadic in traffic generation and not habitual.Slide33

Paper 1 - Subscriber Traffic Dynamics

Relating subscriber temporal activity and traffic they generate:

33

KEY OBSERVATIONS:

Subscribers generating less traffic (<= 30 KB) have poorer effective bit rate compared to more traffic ones. May be due to the kind of application they use (next slide).

Fig. CDF of effective bit rate for subscribers categorized by traffic generated per day.

Effective bit rate is the ratio of amount of actual traffic generated by the subscribers to the airtime. Metric for efficient radio channel use.Slide34

Paper 1 - Subscriber Traffic Dynamics

Relating subscriber temporal activity and traffic they generate:

34

KEY OBSERVATIONS:

P2P and http:yahoo have the best channel efficiencies.

VPN, https and http for Google, Microsoft have poorest efficiencies.

Fig. Effective bit rate for popular TCP based applications.

INFERENCE:

Enterprise applications generate less traffic compared to other applications for the same airtime.

All applications have significantly poorer effective bit rates compared to nominal rates (

phy

channel).Slide35

Paper 1 - Subscriber Traffic Dynamics

Relating subscriber temporal activity and traffic they generate:

35

REASONING for INFERENCE:

Enterprise applications (VPN) tend to use network sporadically like keep-alive messages and typically not high throughput applications.

Considering dormancy/sleep modes, effective bit rate is poor for VPN-like applications.

High throughput applications like P2P use the channel better.

Fig. Effective bit rate for popular TCP based applications.Slide36

Paper 1 - Subscriber Traffic Dynamics

Implications on effective bit rates

:

Inefficiency in the usage of the radio channel airtime drives the need for an innovative protocol to use wireless channel efficiently.Inefficiency arises because of wired-internet protocols used to access wireless channel and hence better network protocols need to be designed.

36Slide37

BASE

STATION TRAFFIC DYNAMICS

Aggregate

LoadBase Station Load DistributionSpatial CharacteristicsTemporal CharacteristicsLoadAuto-correlationSpatiotemporal Characteristics

37

We focus on network behavior as

a whole or in terms of network components (base

stations) instead

of focusing on subscribers.Slide38

BASE STATION TRAFFIC

DYNAMICS - Contd.

T

otal traffic split into upload and download for each day of the week.Favorite weekends see a lesser loadDownloads dominate relative to uploads with more than 75% of

daily load

coming from download

traffic

38

Aggregate Load

:Slide39

BASE STATION TRAFFIC DYNAMICS- Contd.

39

Aggregate Load

:

L

oad

on

the network

is relatively low in the early morning hours,

and roughly

similar during the day and the evening.Slide40

40

BASE STATION TRAFFIC

DYNAMICS- Contd.

Base Station Load

Distribution:

Volume

of daily traffic load for each

base station

80

% of the base stations are loaded in the range of

1- 100MB

per day and 10% of the base stations are highly

loaded (more

than 100MB per day).

shows the CDF

of daily

base station loads normalized by the total network load.

10% of the base stations experience

roughly about

50-60% of the aggregate traffic load.

In both

cases, weekend

behavior is slightly different than weekday

behavior. The

load imbalance seems more pronounced in weekends.

Great

imbalance of the base station loads indicates that a

more careful

cell planning is possibly needed. Network providers

may use smaller cells or microcells at the hotspots to

even out

the imbalance.Slide41

41

BASE STATION TRAFFIC

DYNAMICS- Contd.

Spatial Characteristics

Goal is to identify

whether or how

much spatially

correlated the network load is.

E

stimates can potentially

help the provider to allocate resources appropriately

.

Use of Voronoi cells to conduct the experiments

Voronoi

cell corresponds to the geographic region

of each

base station’s coverage

.

E.g. 10

shops in a flat city and their Voronoi cellsSlide42

42

BASE STATION TRAFFIC DYNAMICS- Contd.

More on Voronoi cells:

Region1

Region2

Voronoi cells in certain areas (city centers) signifying some degree of cell planning.

We can readily see again that the cells are not uniformly loaded in space. The load differentials can extend several orders of magnitude.

There

does appear to

be some

degree of negative correlation between the Voronoi

cell

size

and load.

Large

Voronoi cells

mean sparsely

located base stations, implying sparer

population density

. No significant spatial correlation between

adjacent cells

is observed via visual inspection of similar plots for

all days

.Slide43

43

BASE STATION TRAFFIC DYNAMICS- Contd.

Temporal

Characteristics:

correlation or predictable relationship between

signals

observed at different moments in time.

1. Load

:

H

ourly

aggregate load of the entire network and

highly

loaded base stations.

Aggregate

network

load exhibits

a nice periodic behavior with relatively high

loads

during the day and the lowest load during

midnight.

Individual

base station loads do not show that

much periodicity.

load curve varies significantly

among individual

base stations with their peaks occurring at

different times

of the day

.Slide44

44

BASE STATION TRAFFIC DYNAMICS- Contd.

Auto-correlation

:

Rigorous

analysis of the

periodic behavior

describing the network load is done using temporal correlation

for a load metric

.

Helps in understanding the

underlying trends and seasonal variations better.

Auto-correlation function of the

time

series

at

different lags.

Notice the plot shows a high degree

of

temporal

correlation

.

High

peaks occur

at 24

hour intervals and low peaks at 12 hour intervals.

Isn’t this consistent

with diurnal human activity patterns

.

Slide45

45

BASE STATION TRAFFIC DYNAMICS- Contd.

Spatiotemporal

Characteristics:

Use of Moran I to investigate spatial behavior.

Moran's

I is a measure of 

spatial auto correction.

 Spatial autocorrelation is

characterized by a correlation in a signal among nearby locations in space. Spatial autocorrelation is more complex

than one-dimensional 

autocorrelation because

spatial correlation is multi-dimensional (i.e. 2 or 3 dimensions of space) and multi-directional

.

It’s defined as

𝑥

is the is the hourly load on a

base station(random variable).

--

𝑥(x bar)

mean of x

𝑥𝑖

’s

are the observations.

𝑤𝑖𝑗

is the weight

associated with

each pair (

𝑥𝑖, 𝑥𝑗

)

𝑁

is the number of observations

.Slide46

46

BASE STATION TRAFFIC DYNAMICS- Contd.

More on Moran I:

Binary weights

:

𝑤𝑖𝑗

= 1, when the base stations are in close

proximity (a

threshold of 2 miles is used), else

𝑤𝑖𝑗

=

0.

Moran’s

I metric

is plotted for

hourly loads of all base stations

in

the

network on a temporal scale.

Periodic behavior with

a diurnal cycle is interesting

.

Appears

that

while temporal

usage patterns of base stations may be very

different

and might even miss periodicity there is a

general

tendency for proximate base

station

loads to be

more correlated

when

the

loads are high

.

Correlation is fairly

small, rarely exceeding 0.15.

Min close

to zero, showing almost independent loading

behavior around

midnights when generally the loads are small.Slide47

47

Implication of variability in Base station Load

High degree

of variability in base station loads has

important implication

on spectrum allocation and energy

saving schemes

in the network.

A

daptively

turning on/off certain carriers or radios in

base stations

based on the load experienced need to be developed

.

P

eak

hours of

different cells

vary a

lot

Dynamic

allocation

of spectrum

resources to highly loaded cells during their

peak hours

Future Work:

model the

demand characteristics

on different cells in cellular data

networks based

on measurements for a long period of time and feed

the model

as inputs to dynamic spectrum allocation

algorithms. Study the observationSlide48

48

Paper 2 –

NetPiculet

– Untold Story of middleboxes

Cellular networks becoming more and more ubiquitous and \

pervasive.

Two major players involved in such networks –

-

Network providers

-

Application developers

Cellular Networks also face problems similar to their Internet counterparts such as IP address space depletion and security loopholes

Moreover cellular networks have limited resources

To make best use of their limited resources, number of

middleboxes

deployed by providers to enforce policiesSlide49

49

NetPiculet

An Android Application opened to

mret

place in January 2011

in order to record policies

Major policies tested are NAT and Firewall

Tested over 6 continents and 107 different carriers.

Made lucrative by making the user know his network shortcomings and loopholesSlide50

50

NetPiculet - System Architecture Slide51

51

NAT traversal

NAT

traversal is a general term for

techniques that establish and

maintain

 

Internet protocol

connections

traversing 

a

 

NAT

gateway

IPv4 address space depleted and

number of users increasing.

Also allows hiding of end clients behind NAT routers and thus increases security

Many filtering policies implemented at NAT gateways which was the aim of

NetPiculet

to find out.Slide52

52

NAT mapping schemes

NAT

middlebox

maps an external endpoint based on the TCP 5

tuple

 

(protocol, local-

addr

, local-process, foreign-

addr

,

foreign-

process)

Mapping can be any one of the following:-

- Independent :- external endpoint remains the same

- Address and Port(delta) – external endpoint changes when

destination endpoint changes

- Connection(delta) – External endpoint changes for each new

connection

[delta – increment in external por

t number for every new connection]

Port number predicted in order to test with stream of packets for

new connections. Slide53

53

NAT Policies

Nat properties:-

- End point filtering

- TCP state tracking - Filtering Response

- Packet mangling

NAT characteristics:-

- Time dependent NAT mapping – has advantages as well as

disadvantages and hence a compromised value has to be

decided depending upon tradeoff

- Multiple NAT boxes – system complexity increasesSlide54

54

Summary of results of

NAT Policies

Discovered a previously unknown NAT mapping scheme and

implemented a corresponding traversal scheme which succeeds

with high probability.

A single client may encounter multiple NAT boxes due to load

balancing and hence care should be taken to maintain mapping

during the traversal.

Some of the carriers assign random ports for connections which is

worst for NAT mapping and traversal. Birthday paradox used to

resolve the mapping but for P2P applications, it is better to use a

consistent mapping scheme. Slide55

55

Firewall

Required to protect end users from malicious attacks such as

DoS

,

Battery drain-out, etc

Implemented at

middleboxes

inline with NAT.

Methodology used for testing:-

- Testing IP spoofing

- Testing

stateful

Firewall

- Testing TCP connection timeout

- Testing Out-of-order Packet Buffering Slide56

56

Firewall Policies

Slide57

57

Firewall Policies – Implications and Recommendations

Energy impact of TCP connection timeout

Performance and Energy impact of buffering-

- Disabling TCP fast retransmit

- Bad interaction with Protect against wrapped sequence

- Bad interaction with TCP Forward- RTO recovery

Exploiting large sequence number window

Flaws with closing TCP connectionsSlide58

58

Firewall Policies – Effect on Download time

Slide59

59

Firewall Policies – Effect on Energy Consumption

Slide60

60

Summary of Firewall Policies

4 out of 60 cellular networks allow IP spoofing making the user

vulnerable

Nearly 15 % of carriers set TCP timeout less than 10 minutes

increasing energy consumption. SDK suggested to be used by

developers to maintain uniformity.

TCP out of order buffering causes degraded performance and energy

waste in some cases. So a tradeoff has to be decided between

performance and security.