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
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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.