A Modeling Framework to Understand the Tussle between ISPs and Peer-to-Peer File Sharing Users
IFIP Performance 2007
Michele Garetto – Univ. of TorinoDaniel Figueiredo – Fed. Univ. of Rio de JaneiroRossano Gaeta – Univ. of TorinoMatteo Sereno – Univ. of Torino
IFIP Performance 2007
An Internet Tale Once upon a time...
user unhappy (“world wide wait”) ISP unhappy (little revenue)
user
InternetISP
Then came broadband access...
user
InternetISP
And they lived happily ever after...
fast!
IFIP Performance 2007
The Villain Arrives P2P file sharing application
(Kazza, Bittorrent, Emule, etc)
user
users love it! good and free
content, overnight downloads
ISP
ISPs hate it! users using
their link Internet link
utilization gone wild
degrades all subscribers
more bandwidth costs money!
InternetISP
IFIP Performance 2007
Taking Care of The Villain Mafia
style!
user
seriously threaten application developers!
doesn't seem to work (Napster story)
Is it Really a Villain?
Users love it! Driving force for broadband adoption Increased revenue for ISPs
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Some Other Options
user
User unfriendly ideas increase
subscription cost volume based
pricing block / shape P2P
traffic
User friendly ideas acquire more
bandwidth network caching application-layer
redirection
What should the ISP do?
ISP
???
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The Real Thing (Data)
P2P represented 60% of InternetTraffic at the end of 2004!
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Our contribution Modeling framework to analyze
interactions between P2P file sharing users (their traffic) and ISP economic + performance models
Basic insights about system dynamics
Used to evaluate different strategies to manage P2P traffic
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Meet the Players generates
queries quality of service
expectations what's hot,
what's not
user
ISP goal: to make
money! sets subscription
price controls
bandwidth influences P2P
app. behavior P2P application locates object
Network
network architecture
protocols
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System Setting
User issues query
Bd constrained resource for ISP
Outside download consumes Bd
number of P2Pusers within ISP
number of P2Pusers outside ISP
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Simple System Model
object retrieval probability:
prob. P2P app.locates object
prob.object islocated inside ISP
Model for “Internet to ISP” link
aggregatequery rate
unconstraineddownloads fromwithin the ISP
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User Utility Function
Satisfaction model for user i
-1
-0.5
0
0.5
1
0 0.2 0.4 0.6 0.8 1σ
User utility
probability of successful object retrieval
shape parameter
subscription cost
Minimum service level for
user i
user
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ISP Utility Function Profit for ISP (revenue - costs)
fixed charge
cost per unit of external bandwidth
revenue from subscribers’ fee
The ISP starts service only if
ISP
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Modeling Traffic Locality Probability there exist at least one internal replica
of object replicated r times in the system Number of internal copies
Number of external copies
Probability to download from internal replica
localityparameter
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Analytical Results How much bandwidth should the ISP buy
to minimally satisfy the users?
Bmin=max [0,nq min−q r n/N ]
identical users and n >> N
Non-linear behavior (on n) more users, more locality, less BW needed can be zero if n large enough
May not yield profit too few users, too costly to satisfy them
Dependent on multiple parameters
IFIP Performance 2007
Impact of Object Replication (r)
0
1000
2000
3000
4000
5000
0 10000 20000 30000 40000
Bm
in (
ob
ject
s/d
ay)
Number of users, n
r = 500r = 1000
r = 1500
more replicas, better locality, lower Bmin
more bw neededto support largeruser population
less bw needed(users satisfied locally)
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Impact of Subscription Cost (c)
-10000
0
10000
20000
30000
40000
0 5000 10000 15000 20000 25000 30000
c = 0.25c = 1.0c = 1.4c = 1.6
Number of users, n
Uti
lity o
f IS
P
nmin
critical mass of users, nmin
lower cost, more profit earlier, less profit later
ISP doesnot provideservice!
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Critical Mass of Users, nmin
0
20000
40000
60000
80000
100000
120000
140000
0 200 400 600 800 1000
nm
in
Average object replication, r
β2 = 6β2 = 5β2 = 4β2 = 3
Cost per unit of bandwidthfor ISP
higher bw cost for ISP, higher critical mass large influence of number of replicas
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Model Refinements Simple model
users' access bandwidth are unconstrained
object replication is a parameter
all objects are identical (no popularity)
users availability identical
Refined model relax these assumptions propose object
popularity and replication model
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Object Popularity and Replication Model Temporal evolution of object popularity Objects' popularities evolve differently Objects continuously introduced and
removed by users
Analytical technique based on Poisson shot noise process
Number of replicas of an object at time t?
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Exampleob
ject
p
opula
rity
time
A video from the news
A popular song
t1 t2
object request
at request time, both have same popularity, but news has more replicas
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Limited Bandwidth Refinements
users BW consumptionis limited to b
d
each user within ISP modeledseparately
users upload bandwidthlimited to b
u
rate of downloadrequests to user i
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Results from Refined Model
Degenerate to simple model when parameters
set appropriately
Other interesting insights influence of limited upload bandwidth upload/download bandwidth asymmetry object popularity and replication influence of user impatience
0
1000
2000
3000
4000
5000
6000
7000
0 5000 15000 25000
bd = 2000
Bm
in
Number of users, n
bd = 1000bd = 500bd = 100bd = 10
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Impact of asymmetric access bandwidths
0
2000
4000
6000
8000
10000
12000
0 500 1000 1500 2000 2500 3000 3500 4000
Upload bandwidth, bu
(for fixed number of users = 20000)
bd = bu
bd = 2 bu
bd = 3 bu
bd = 4 bu
Bm
in (
ob
ject
s/d
ay)
cost for ISP increases as ratio increases better if upload BW is greater than download
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Conclusions Development of simple analytical model
economics + performance interaction between P2P users (their traffic)
and ISP insights into strategy for ISP to manage its
traffic Model for object popularity and
replication of independent interest
Future work Multiple ISPs competing with each other
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THE END Thank you! Questions? Comments?
CacheLogic Advanced Solutions for P2P Networks
Presentation | WCW 2005
Impact on Service Providers
P2P is thethe dominant protocol In excess of 92% of P2P traffic crosses transit/peering links P2P protocols will aggressively consumeaggressively consume any available bandwidth
capacity Due to P2P’s symmetrical nature on average 80% of upstream
capacity is consumed by P2P P2P affects QoS levels for ALL subscribers Service Providers can not afford to block or restrict P2P ISPs must intelligently manage P2P - blocking and shaping
doesn’t work
P2P is driving consumer broadband uptakeP2P is driving consumer broadband uptake……and broadband is driving P2P uptakeand broadband is driving P2P uptake
HTTP
Other Non P2P
Other P2PFastTrack
eDonkey
BitTorrent
RecognisingGnutella
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The ISP perspective vs P2P:threat or opportunity ?
P2P traffic: friend or foe ? friend: driving force for adoption of
broadband access by the users foe: overwhelming amount of traffic
What is the best strategy to manage P2P traffic in my network ? Try to kill it ? Do nothing ? Educate it ? How ?
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Strategies to manage P2P traffic Acquire more bandwidth Block P2P traffic Traffic shaping (e.g., priority to non-P2P) Pricing schemes based on user traffic
volumes / bandwidth caps Network caching / customized P2P
application within ISP Application-layer redirection of P2P
traffic
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Assumption: n identical users N = 50 millions request rate by user (object/day)
introduction of new contents by
user (object/day)
Results
Minimum required external bandwidth:
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The impact of efficacy in exploiting traffic locality ( )
0
20000
40000
60000
80000
100000
0 100000 300000 500000 700000
= 0.25
= 0.5= 0.75
= 1.0
Number of users, n
Minimumrequired bandwidth Bmin
(objects/day)
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Model refinements Impact of finite bandwidth of the users
In case of constant traffic load, the cost for the ISP increases if it provides more download bandwidth to the users (!)
The system performance is strongly affected by the upload bandwidth of the users, that should be larger than or equal to the download bandwidth (contrary to common practices, e.g., ADSL lines !)
The impact of bd
0
1000
2000
3000
4000
5000
6000
7000
0 5000 10000 15000 20000 25000 30000
bd = 2000
Bm
in
Number of users, n
bd = 1000bd = 500bd = 100bd = 10