Use of Mobile Network Analytics for Application Performance Design

Use of Mobile Network Analytics for Application Performance Design

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Description: We present a solution that uses the radio parameters measured by a mobile terminal to determine the best Application Protocol (APPP) for a service. Necessity to correlate instantaneous parameters measured by the terminal with averaged parameters perceived by the user. NApplytics is designed to: Be an Android library.

Select the most appropriated APPP depending on the network conditions experienced by the user. Be transparent for both: APP users and APP developers.

 
Author: Irene Alepuz, Jorge Cabrejas, Jose F. Monserrat, Alvaro G. Perez, Gonzalo Pajares, Roberto Gimenez   | Visits: 255 | Page Views: 411
Domain:  High Tech Category: Mobile 
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Contents:
Thank you for coming!
Use of Mobile Network Analytics for
Enjoy the stay
Application Performance Design
Irene Alepuz, Jorge Cabrejas, Jose F. Monserrat,
Alvaro G. Perez, Gonzalo Pajares, Roberto Gimenez
MNM’17 Dublin – June 2017

Summary



Introduction



NApplytics description



Results and discussions



Conclusions



Future work

2

Introduction
• The 5G will lead to an increase of:
– The data traffic.
– The number of connected devices.

• The mobile phones will be cornerstones in our daily live
– It is critical to understand the mobile network performance, to:
• Provide a superior user experience.
• Determine the success of an application (APP).

3

Introduction
• This study:
– Is part of the MONROE Project.
– Is done under the collaboration of:

– We present a solution that uses the radio parameters
measured by a mobile terminal to determine the best
Application Protocol (APPP) for a service.

4

Introduction
• Motivation:
– Necessity to correlate instantaneous parameters measured by
the terminal with averaged parameters perceived by the user.

5

NApplytics description

• NApplytics is designed to:
– Be an Android library.
– Select the most appropriated APPP depending on the network
conditions experienced by the user.
– Be transparent for both: APP users and APP developers.

6

NApplytics description
• So, we need models to capture the relationship between
the QoS and the QoE.
• We present a customer Satisfaction Index (SI) that:
– Provides the actual customer perception (QoE).
– Identifies the most appropriated APPP.

• This study focuses on three APPP:
– HTTP1.1
– HTTP2
– HTTP1.1 TLS

• We give only details for the Long Term Evolution (LTE)
technology.
7

NApplytics description
• SI procedure:

8

NApplytics description
• SI procedure:
1. Metrics definition
• The networks metrics used are:
– Reference Signal Received Power (RSRP)
– Received Signal Strength Indicator (RSSI)
– Downlink Throughput (Th)

2. Attributes calculation
• Each attribute has five levels of quality:
Score

User experience quality

5

Excellent

4

Good

3

Fair

2

Poor

1

Bad
9

NApplytics description
• SI procedure:
1. Metrics definition
2. Attributes calculation
• The attribute for the RSRP metric in dBm is:
1,
𝑓𝑜𝑟 𝑅𝑆𝑅𝑃 ≤ −109
𝑓1 𝑅𝑆𝑅𝑃 , 𝑓𝑜𝑟 𝑅𝑆𝑅𝑃 ∈ −109, −103
𝑓2 𝑅𝑆𝑅𝑃 ,
𝑓𝑜𝑟 𝑅𝑆𝑅𝑃 ∈ −103, −97
𝑆𝐼 𝑅𝑆𝑅𝑃 =
𝑓3 𝑅𝑆𝑅𝑃 ,
𝑓𝑜𝑟 𝑅𝑆𝑅𝑃 ∈ −97, −92
𝑓4 𝑅𝑆𝑅𝑃 ,
𝑓𝑜𝑟 𝑅𝑆𝑅𝑃 ∈ −92, −88
5,
𝑓𝑜𝑟 𝑅𝑆𝑅𝑃 > −88
where 𝑓 𝑋𝑅𝑆𝑅𝑃 are linear functions inside each interval. For example:
𝑓1

𝑅𝑆𝑅𝑃

𝑅𝑆𝑅𝑃 + 109
=1+
6
10

NApplytics description
• SI procedure:
1. Metrics definition
2. Attributes calculation
3. SI calculation
• SI is defined as:
𝑆𝐼 = 𝑤1 𝑆𝐼 𝑅𝑆𝑅𝑃 + 𝑤2 𝑆𝐼 𝑅𝑆𝑆𝐼 + 𝑤3 𝑆𝐼 𝑇ℎ
• We measure the QoE by means of utility functions. For the web
browsing service the utility function (U) is defined by [1]:
𝑈 =5−

578

22.61 2
1 + 11.77 +
𝑑
where d is the service respond time measured in seconds.
[1] P. Ameigeiras, J. J. Ramos-Munoz, J. Navarro-Ortiz, and P. Monensen. QoE Oriented Cross-layer Design of
a Resource Allocation Algorithm in Beyond 3G Systems, Computer Communications, 33(5), 571-582, 2010.
11

NApplytics description
• SI procedure:
1. Metrics definition
2. Attributes calculation
3. SI calculation
• The weights are calculated by minimizing the Mean Square Error (MSE):
min

𝑤1 ,𝑤2 ,𝑤3

𝑈 − 𝑆𝐼

2

• This process will be executed for each APPPs.

• NApplytics selects the APPP in execution time that fits:
max 𝑆𝐼 𝐴𝑃𝑃𝑃1 , 𝑆𝐼 𝐴𝑃𝑃𝑃2 , … , 𝑆𝐼 𝐴𝑃𝑃𝑃 𝑀
12

NApplytics description
• Overall calculation of the different SI in the training
phase:

13

Results and discussions
• Service analyzed: web browsing
• Protocols: HTTP1.1, HTTP2 and HTTP1.1 TLS
• Experiments realized:
• More than 2500 experiments
• Combination of headlessbrowsing and http_download

14

Results and discussions: HTTP1.1 Protocol
• 887 experiments done
• Distribution of the user experience quality:

Distribution by the SI

Distribution by the U

15

Results and discussions
• Detractor experiments are those who fulfill that:
𝑆𝐼 − 𝑈 ≥ 1.5
• In contrast, promoter experiments are those who fulfill
that:
𝑆𝐼 − 𝑈 ≤ −1.5

16

Results and discussions

Detractor experiments

Promoter experiments

17

Results and discussions
• Weight percentage of each parameter:
Parameter

HTTP1.1

HTTP2

HTTP1.1 TLS

RSRP

30.77%

33.04%

32.57%

RSSI

28.05%

28.06%

27.8%

DL Throughput

41.19%

38.9%

39.63%

• Percentage of detractors and promoters:
Protocol

Detractors

Promoters

HTTP1.1

3.04%

1.47%

HTTP2

1.32%

1.59%

HTTP1.1 TLS

1.8%

1.94%

18

Results and discussions
• Correlation with and without detractors and
promotes experiments:
Protocol

Correlation

Correlation without D&P

HTTP1.1

86.58%

90.67%

HTTP2

86.9%

89.51%

HTTP1.1 TLS

83.72%

86.98%

• Mean Square Error:
Protocol

MSE

HTTP1.1

0.5

HTTP2

0.53

HTTP1.1 TLS

0.59

19

Conclusions
• A new approach to correlate network KPIs with user
experience while using an APP is designed.
• This will help software APP developers to understand
the user’s experience during the APP execution and
act accordingly (e.g. changing the APPP).
• We obtain a high correlation for the web browsing
service.

20

Future work
• Implement more services:
– Video streaming (e.g. YouTube)
– Voice over IP
–…

• Implement more protocols:
– RTP
– UDP
–…

21

Thank you for coming!
Enjoy the stay
Thank you for your attention!
Alvaro G. Perez (aperez@eurob.com)
MNM’17 Dublin – June 2017

NApplytics description
• Distribution accordingly to RSRP values:
60000

Number of samples

50000
40000
30000

20000
10000

-50

-52

-54

-56

-58

-60

-62

-64

-66

-68

-70

-72

-74

-76

-78

-80

-82

-84

-86

-88

-90

-92

-94

-96

-98

-100

-102

-104

-106

-108

-110

-112

-114

-116

-118

-120

-122

-124

-126

-128

0

RSRP [dBm]

• Each level has been chosen with
the aim of having a similar number
of samples.
• 696,111 samples from MONROE
database.

SI Level RSRP Range # Samples
1
1-2
2-3
3-4
4-5
5

≤ −109
]−109, −103]
]−103, −97]
]−97, −92]
]−92, −88]
> −88

99,217
111,028
128,332
125,451
134,775
97,308

23

NApplytics description
• For the training phase the same type of experiments
that for the testing phase has been realized.
• The weights are calculated by minimizing the Mean
Square Error (MSE):
min

𝑤1 ,𝑤2 ,𝑤3

𝑈 − 𝑆𝐼

2

• This problem was solved using the Generalized Reduce
Gradient (GRG):
– Is a generalization of the reduced gradient method by allowing
nonlinear constraints and arbitrary bounds on the variables.
24

NApplytics metrics
• We considered to use the following metrics:





RSRP (Reference Signal Receive Power)
RSRQ (Reference Signal Receive Quality)
RSSI (Received Signal Strength Indicator)
Downlink Throughput

• But RSRQ depends on RSRP and RSSI:
RSRQ = (N_RB * RSRP) / RSSI

, being N_RB the number of resource blocks.
• In case there is no noise and no interferences:
RSSI = 12 * N * RSRP
25