[PPT] - Mobile Network Performance from User Devices: A Longitudinal, PowerPoint Presentation

SLIDE 1

Mobile Network Performance from User Devices:

A Longitudinal, Multidimensional Analysis Ashkan Nikravesh, David R. Choffnes, Ethan Katz-Bassett

Z. Morley Mao, Matt Welsh

SLIDE 2

Problem

Mobile Network Performance: ✘ Poor visibility into user perceived performance. ✘ It is difficult to capture a view of network performance.

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 2 / 16

SLIDE 3

Problem

Mobile Network Performance: ✘ Poor visibility into user perceived performance. ✘ It is difficult to capture a view of network performance. Why is that difficult?

Performance depends on many factors
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 2 / 16

SLIDE 4

Problem

Mobile Network Performance: ✘ Poor visibility into user perceived performance. ✘ It is difficult to capture a view of network performance. Why is that difficult?

Performance depends on many factors

How to improve the visibility?

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 2 / 16

SLIDE 5

Problem

Mobile Network Performance: ✘ Poor visibility into user perceived performance. ✘ It is difficult to capture a view of network performance. Why is that difficult?

Performance depends on many factors

How to improve the visibility?

Pervasive network monitoring is needed:
Continuous
Large-scale
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 2 / 16

SLIDE 6

Problem

Mobile Network Performance: ✘ Poor visibility into user perceived performance. ✘ It is difficult to capture a view of network performance. Why is that difficult?

Performance depends on many factors

How to improve the visibility?

Pervasive network monitoring is needed:
Continuous
Large-scale
Sampling performance of devices across:
Carriers
Access Technologies
Location
Time
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 2 / 16

SLIDE 7

Previous Works

Their Limitations:

Passively collected from cellular network infrastructure (e.g.

GGSN)

One month of data
Limited to a single carrier
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 3 / 16

SLIDE 8

Previous Works

Their Limitations:

Passively collected from cellular network infrastructure (e.g.

GGSN)

One month of data
Limited to a single carrier
Collected from mobile devices, but not continuously
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 3 / 16

SLIDE 9

Previous Works

Their Limitations:

Passively collected from cellular network infrastructure (e.g.

GGSN)

One month of data
Limited to a single carrier
Collected from mobile devices, but not continuously

Our work differs from previous related work:

Longitudinal
Continuous
Gathered from mobile devices using controlled experiments.
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 3 / 16

SLIDE 10

Data Analysis

✔ Analyzing the data collected from:

✔ 144 carriers ✔ 17 months ✔ 11 cellular networks

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 4 / 16

SLIDE 11

Data Analysis

✔ Analyzing the data collected from:

✔ 144 carriers ✔ 17 months ✔ 11 cellular networks

✔ Identify patterns, trends, anomalies, and evolution of cellular networks’ performance.

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 4 / 16

SLIDE 12

Data Analysis

✔ Analyzing the data collected from:

✔ 144 carriers ✔ 17 months ✔ 11 cellular networks

✔ Identify patterns, trends, anomalies, and evolution of cellular networks’ performance. We find:

Significant variance in end-to-end performance for all carriers.
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 4 / 16

SLIDE 13

Data Analysis

✔ Analyzing the data collected from:

✔ 144 carriers ✔ 17 months ✔ 11 cellular networks

✔ Identify patterns, trends, anomalies, and evolution of cellular networks’ performance. We find:

Significant variance in end-to-end performance for all carriers.
Part of the high variability is due to the geographic and temporal

properties of network.

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 4 / 16

SLIDE 14

Data Analysis

✔ Analyzing the data collected from:

✔ 144 carriers ✔ 17 months ✔ 11 cellular networks

✔ Identify patterns, trends, anomalies, and evolution of cellular networks’ performance. We find:

Significant variance in end-to-end performance for all carriers.
Part of the high variability is due to the geographic and temporal

properties of network.

Routing and signal strength are potential sources of performance

variability.

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 4 / 16

SLIDE 15

Data Analysis

✔ Analyzing the data collected from:

✔ 144 carriers ✔ 17 months ✔ 11 cellular networks

✔ Identify patterns, trends, anomalies, and evolution of cellular networks’ performance. We find:

Significant variance in end-to-end performance for all carriers.
Part of the high variability is due to the geographic and temporal

properties of network.

Routing and signal strength are potential sources of performance

variability.

Performance is inherently unstable.
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 4 / 16

SLIDE 16

Methodology

User perceived performance:
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 5 / 16

SLIDE 17

Methodology

User perceived performance:
HTTP GET Throughput
Ping RTT
DNS Lookup time
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 5 / 16

SLIDE 18

Methodology

User perceived performance:
HTTP GET Throughput
Ping RTT
DNS Lookup time

✔ Traceroute ✔ Carrier + Cellular network technology ✔ Signal strength ✔ Location ✔ Timestamp

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 5 / 16

SLIDE 19

Methodology

User perceived performance:
HTTP GET Throughput
Ping RTT
DNS Lookup time

✔ Traceroute ✔ Carrier + Cellular network technology ✔ Signal strength ✔ Location ✔ Timestamp To identify and isolate the performance impact of each factor

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 5 / 16

SLIDE 20

Methodology

User perceived performance:
HTTP GET Throughput
Ping RTT
DNS Lookup time

✔ Traceroute ✔ Carrier + Cellular network technology ✔ Signal strength ✔ Location ✔ Timestamp To identify and isolate the performance impact of each factor

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 5 / 16

SLIDE 21

Dataset

Mainly collected by Speedometer:
2011-10 to 2013-2 (17 months)
Internal android app developed by Google
Anonymized data
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 6 / 16

SLIDE 22

Dataset

Mainly collected by Speedometer:
2011-10 to 2013-2 (17 months)
Internal android app developed by Google
Anonymized data
Mobiperf
11 months
Only used for our signal strength analysis
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 6 / 16

SLIDE 23

Dataset

Mainly collected by Speedometer:
2011-10 to 2013-2 (17 months)
Internal android app developed by Google
Anonymized data
Mobiperf
11 months
Only used for our signal strength analysis
Controlled experiments

Speedometer dataset: 4-5 measurements per minute

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 6 / 16

SLIDE 24

Dataset

Mainly collected by Speedometer:
2011-10 to 2013-2 (17 months)
Internal android app developed by Google
Anonymized data
Mobiperf
11 months
Only used for our signal strength analysis
Controlled experiments

Speedometer dataset: 4-5 measurements per minute

Code is open source and data is publicly available
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 6 / 16

SLIDE 25

Performance across Carriers

How observed performance matches with the expectations across access technologies?

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 7 / 16

SLIDE 26

Performance across Carriers

How observed performance matches with the expectations across access technologies?

Ping RTT Latency

100 1000 T

M
b

i l e A T & T Y e s O p t u s S w i s s c

m

V

d

a f

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C
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e l s t r a S F R S K T e l e c

m

E m

b

i l e Ping RTT (ms) GPRS EDGE UMTS HSDPA HSPA

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 7 / 16

SLIDE 27

Performance across Carriers

How observed performance matches with the expectations across access technologies?

Ping RTT Latency

1 Latency varies significantly across carriers and access technologies

100 1000 T

M
b

i l e A T & T Y e s O p t u s S w i s s c

m

V

d

a f

n

e ( D E ) V

d

a f

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e ( N L ) V

d

a f

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e ( I E ) V

d

a f

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e ( U K ) O 2 ( U K ) A i r t e l T e l k

m

s e l R

g

e r s S i n g T e l N T T D

C
M
T

e l s t r a S F R S K T e l e c

m

E m

b

i l e Ping RTT (ms) GPRS EDGE UMTS HSDPA HSPA

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 7 / 16

SLIDE 28

Performance across Carriers

How observed performance matches with the expectations across access technologies?

Ping RTT Latency

1 Latency varies significantly across carriers and access technologies 2 Same performance for different access technologies

100 1000 T

M
b

i l e A T & T Y e s O p t u s S w i s s c

m

V

d

a f

n

e ( D E ) V

d

a f

n

e ( N L ) V

d

a f

n

e ( I E ) V

d

a f

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e ( U K ) O 2 ( U K ) A i r t e l T e l k

m

s e l R

g

e r s S i n g T e l N T T D

C
M
T

e l s t r a S F R S K T e l e c

m

E m

b

i l e Ping RTT (ms) GPRS EDGE UMTS HSDPA HSPA

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 7 / 16

SLIDE 29

Performance across Carriers

HTTP GET Throughput

10 100 1000 T

M
b

i l e A T & T Y e s O p t u s S w i s s c

m

V

d

a f

n

e ( D E ) V

d

a f

n

e ( N L ) V

d

a f

n

e ( I E ) V

d

a f

n

e ( U K ) O 2 ( U K ) A i r t e l T e l k

m

s e l R

g

e r s S i n g T e l N T T D

C
M
T

e l s t r a S F R S K T e l e c

m

E m

b

i l e HTTP Throughput (Kbps) GPRS EDGE UMTS HSDPA HSPA

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 8 / 16

SLIDE 30

Performance across Carriers

HTTP GET Throughput

1 Relatively smaller difference between the carriers

10 100 1000 T

M
b

i l e A T & T Y e s O p t u s S w i s s c

m

V

d

a f

n

e ( D E ) V

d

a f

n

e ( N L ) V

d

a f

n

e ( I E ) V

d

a f

n

e ( U K ) O 2 ( U K ) A i r t e l T e l k

m

s e l R

g

e r s S i n g T e l N T T D

C
M
T

e l s t r a S F R S K T e l e c

m

E m

b

i l e HTTP Throughput (Kbps) GPRS EDGE UMTS HSDPA HSPA

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 8 / 16

SLIDE 31

Performance across Carriers

HTTP GET Throughput

1 Relatively smaller difference between the carriers 2 Download size (224KB) in not sufficiently large

10 100 1000 T

M
b

i l e A T & T Y e s O p t u s S w i s s c

m

V

d

a f

n

e ( D E ) V

d

a f

n

e ( N L ) V

d

a f

n

e ( I E ) V

d

a f

n

e ( U K ) O 2 ( U K ) A i r t e l T e l k

m

s e l R

g

e r s S i n g T e l N T T D

C
M
T

e l s t r a S F R S K T e l e c

m

E m

b

i l e HTTP Throughput (Kbps) GPRS EDGE UMTS HSDPA HSPA

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 8 / 16

SLIDE 32

Performance across Carriers

HTTP GET Throughput

1 Relatively smaller difference between the carriers 2 Download size (224KB) in not sufficiently large

10 100 1000 T

M
b

i l e A T & T Y e s O p t u s S w i s s c

m

V

d

a f

n

e ( D E ) V

d

a f

n

e ( N L ) V

d

a f

n

e ( I E ) V

d

a f

n

e ( U K ) O 2 ( U K ) A i r t e l T e l k

m

s e l R

g

e r s S i n g T e l N T T D

C
M
T

e l s t r a S F R S K T e l e c

m

E m

b

i l e HTTP Throughput (Kbps) GPRS EDGE UMTS HSDPA HSPA

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 8 / 16

SLIDE 33

Performance across Carriers

HTTP GET Throughput

1 Relatively smaller difference between the carriers 2 Download size (224KB) in not sufficiently large 3 Lower latency is generally correlated with higher throughput

10 100 1000 T

M
b

i l e A T & T Y e s O p t u s S w i s s c

m

V

d

a f

n

e ( D E ) V

d

a f

n

e ( N L ) V

d

a f

n

e ( I E ) V

d

a f

n

e ( U K ) O 2 ( U K ) A i r t e l T e l k

m

s e l R

g

e r s S i n g T e l N T T D

C
M
T

e l s t r a S F R S K T e l e c

m

E m

b

i l e HTTP Throughput (Kbps) GPRS EDGE UMTS HSDPA HSPA

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 8 / 16

SLIDE 34

Performance across different Locations

Different topologies in different regions
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 9 / 16

SLIDE 35

Performance across different Locations

Different topologies in different regions
New York, Bay Area, and Seattle
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 9 / 16

SLIDE 36

Performance across different Locations

Different topologies in different regions
New York, Bay Area, and Seattle

20 30 40 50 60 70 80 90 100 110 Oct 30 Nov 19 Dec 09 Dec 29 Jan 18 Feb 07 Feb 27 Mar 18 Apr 07 Apr 27 May 17 Jun 06 Ping RTT (ms) Mean and Standard Error Time (Days) 2011-2012 Bay Area Seattle New York

Verizon LTE

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 9 / 16

SLIDE 37

Performance over Time

How much performance depends on time?
time of the day
stability
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 10 / 16

SLIDE 38

Performance over Time

How much performance depends on time?
time of the day
stability
When to measure the network?
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 10 / 16

SLIDE 39

Performance over Time

How much performance depends on time?
time of the day
stability
When to measure the network?

Time of the day

600 700 800 900 1000 1100 1200 1300 1400 2 4 6 8 10 12 14 16 18 20 22 24 HTTP Throughput (Kbps), Mean and Standard Error Local Time from 0 (00:00) to 24 (24:00) AT&T HSDPA Sprint EVDO_A T-Mobile HSDPA Verizon EVDO_A

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 10 / 16

SLIDE 40

Performance over Time

How much performance depends on time?
time of the day
stability
When to measure the network?

Time of the day

600 700 800 900 1000 1100 1200 1300 1400 2 4 6 8 10 12 14 16 18 20 22 24 HTTP Throughput (Kbps), Mean and Standard Error Local Time from 0 (00:00) to 24 (24:00) AT&T HSDPA Sprint EVDO_A T-Mobile HSDPA Verizon EVDO_A

1 Throughput decreases during

the busy hours of usage

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 10 / 16

SLIDE 41

Performance over Time

How much performance depends on time?
time of the day
stability
When to measure the network?

Time of the day

600 700 800 900 1000 1100 1200 1300 1400 2 4 6 8 10 12 14 16 18 20 22 24 HTTP Throughput (Kbps), Mean and Standard Error Local Time from 0 (00:00) to 24 (24:00) AT&T HSDPA Sprint EVDO_A T-Mobile HSDPA Verizon EVDO_A

1 Throughput decreases during

the busy hours of usage

2 Carriers experience minimum

throughput at different times

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 10 / 16

SLIDE 42

Performance over Time

How much performance depends on time?
time of the day
stability
When to measure the network?

Time of the day

600 700 800 900 1000 1100 1200 1300 1400 2 4 6 8 10 12 14 16 18 20 22 24 HTTP Throughput (Kbps), Mean and Standard Error Local Time from 0 (00:00) to 24 (24:00) AT&T HSDPA Sprint EVDO_A T-Mobile HSDPA Verizon EVDO_A

1 Throughput decreases during

the busy hours of usage

2 Carriers experience minimum

throughput at different times

3 Carriers experience different

variation in performance during the busy hours

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 10 / 16

SLIDE 43

Performance over Time

Stability of Performance

Users want a stable network!
Measurements are expensive!
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 11 / 16

SLIDE 44

Performance over Time

Stability of Performance

Users want a stable network!
Measurements are expensive!

Two metrics:

Auto-Correlation
Weighted moving average
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 11 / 16

SLIDE 45

Performance over Time

Stability of Performance

Users want a stable network!
Measurements are expensive!

Two metrics:

Auto-Correlation
Weighted moving average

1PM 2PM 3PM 4PM 5PM

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 11 / 16

SLIDE 46

Performance over Time

Stability of Performance

Users want a stable network!
Measurements are expensive!

Two metrics:

Auto-Correlation
Weighted moving average

1PM 2PM 3PM 4PM 5PM 5PM Window size: 2 Sampling Period: 2hrs w1 Error w2

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 11 / 16

SLIDE 47

Performance over Time Stability of Performance (Weighted Moving Average)

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 3 6 9 12 15 18 21 24 27 30 33 36 Error (%) Sampling Period (hour) Verizon, LTE, BayArea Sprint, EVDOA, Seattle Sprint, EVDOA, BayArea T-Mobile, HSDPA, BayArea

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 12 / 16

SLIDE 48

Performance over Time Stability of Performance (Weighted Moving Average)

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 3 6 9 12 15 18 21 24 27 30 33 36 Error (%) Sampling Period (hour) Verizon, LTE, BayArea Sprint, EVDOA, Seattle Sprint, EVDOA, BayArea T-Mobile, HSDPA, BayArea

1 Prediction accuracy varies

significantly by carriers

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 12 / 16

SLIDE 49

Performance over Time Stability of Performance (Weighted Moving Average)

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 3 6 9 12 15 18 21 24 27 30 33 36 Error (%) Sampling Period (hour) Verizon, LTE, BayArea Sprint, EVDOA, Seattle Sprint, EVDOA, BayArea T-Mobile, HSDPA, BayArea

1 Prediction accuracy varies

significantly by carriers

2 Prediction error increases with

longer sampling periods

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 12 / 16

SLIDE 50

Performance over Time Stability of Performance (Weighted Moving Average)

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 3 6 9 12 15 18 21 24 27 30 33 36 Error (%) Sampling Period (hour) Verizon, LTE, BayArea Sprint, EVDOA, Seattle Sprint, EVDOA, BayArea T-Mobile, HSDPA, BayArea

1 Prediction accuracy varies

significantly by carriers

2 Prediction error increases with

longer sampling periods with the exception of 24hr sampling periods

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 12 / 16

SLIDE 51

Performance Degradation: Root Causes

Focus on the cases where persistent performance degradations were :

observed in consecutive days
affects both latency and throughput
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 13 / 16

SLIDE 52

Performance Degradation: Root Causes

Focus on the cases where persistent performance degradations were :

observed in consecutive days
affects both latency and throughput

Inefficient Paths

Time evolution
Impact on the performance
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 13 / 16

SLIDE 53

Performance Degradation: Root Causes

Focus on the cases where persistent performance degradations were :

observed in consecutive days
affects both latency and throughput

Inefficient Paths

Time evolution
Impact on the performance

40 60 80 100 120 140 160 11 12 13 14 16 17 18 19 20 21 22 Median Ping RTT (ms) Time (day) - Feb 2012

Seattle (25-50-75) Los Angeles (25-50-75)

(a) T-Mobile HSDPA (Seattle)

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 13 / 16

SLIDE 54

Performance Degradation: Root Causes

Focus on the cases where persistent performance degradations were :

observed in consecutive days
affects both latency and throughput

Inefficient Paths

Time evolution
Impact on the performance

40 60 80 100 120 140 160 11 12 13 14 16 17 18 19 20 21 22 Median Ping RTT (ms) Time (day) - Feb 2012

Seattle (25-50-75) Los Angeles (25-50-75)

(a) T-Mobile HSDPA (Seattle)

11 12 13 14 15 16 17 18

05 Nov 12 Nov 19 Nov 26 Nov 03 Dec 10 Dec 17 Dec 24 Dec 31 Dec 07 Jan

30 40 50 60 70 80 # of Hops Median Ping RTT (ms) Time (day) - 2011 Ping RTT Hops

(b) Verizon LTE (Bay Area)

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 13 / 16

SLIDE 55

Performance Degradation: Root Causes Signal Strength

How much it can affect performance?

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 14 / 16

SLIDE 56

Performance Degradation: Root Causes Signal Strength

How much it can affect performance?

0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 0.26

5 10 15 20 25 30

400 600 800 1000 1200 1400 1600 1800 Mean Packet Loss (%) Mean Ping RTT (ms) ASU Ping RTT Packet Loss

(a) Ping RTT and Packet Loss

150 200 250 300 350 400 450 500 550

5 10 15 20 25 30

Mean HTTP Throughput (Kbps) ASU HTTP Throughput

(b) HTTP Throughput

AT&T HSDPA (Seattle), Arbitrary Strength Units (ASU)

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 14 / 16

SLIDE 57

Performance Degradation: Root Causes Signal Strength

How much it can affect performance?

0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 0.26

5 10 15 20 25 30

400 600 800 1000 1200 1400 1600 1800 Mean Packet Loss (%) Mean Ping RTT (ms) ASU Ping RTT Packet Loss

(a) Ping RTT and Packet Loss

150 200 250 300 350 400 450 500 550

5 10 15 20 25 30

Mean HTTP Throughput (Kbps) ASU HTTP Throughput

(b) HTTP Throughput

AT&T HSDPA (Seattle), Arbitrary Strength Units (ASU)

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 14 / 16

SLIDE 58

Performance Degradation: Root Causes Signal Strength

How much it can affect performance?

0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 0.26

5 10 15 20 25 30

400 600 800 1000 1200 1400 1600 1800 Mean Packet Loss (%) Mean Ping RTT (ms) ASU Ping RTT Packet Loss

(a) Ping RTT and Packet Loss

150 200 250 300 350 400 450 500 550

5 10 15 20 25 30

Mean HTTP Throughput (Kbps) ASU HTTP Throughput

(b) HTTP Throughput

AT&T HSDPA (Seattle), Arbitrary Strength Units (ASU)

◮ Accounting for signal strength is important for interpreting

measurement results.

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 14 / 16

SLIDE 59

Future Work

We need for more monitoring and diagnosis.
Data is difficult to get.
A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 15 / 16

SLIDE 60

Future Work

We need for more monitoring and diagnosis.
Data is difficult to get.

Mobilyzer

An Open Platform for Mobile Network Measurement A comprehensive codebase for issuing measurements for researchers and developers

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 15 / 16

SLIDE 61

Any Questions?

Thank You!

A. Nikravesh, D. R. Choffnes, E. Katz-Bassett, Z. M. Mao, M. Welsh

PAM 2014 16 / 16