IFIP – Performance 2007 A Modeling Framework to Understand the Tussle between ISPs and Peer-to-Peer File Sharing Users Michele Garetto - unito Daniel R. Figueiredo - EPFL Rossano Gaeta - unito Matteo Sereno - unito

P2P is starting to dominate Internet Traffic P2P represented 60% of Internet Traffic at the end of 2004

Impact on Service Providers P2P is driving consumer broadband uptake …and broadband is driving P2P uptake P2P is the dominant protocol In excess of 92% of P2P traffic crosses transit/peering links P2P protocols will aggressively 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

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 ?

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

Our contribution Simple model to analyze the impact of P2P traffic on an ISP network Basic insights about system dynamics Performance evaluation tool to evaluate different strategies to manage P2P traffic and guide ISP choices

Network scenario Exploitation of “traffic locality” User issues a query Solved inside  does not use bandwidth Bd Solved outside  consumes bandwidth Bd Exploitation of “traffic locality”

System model object retrieval probability:

User Utility Function We express the utility of user i as: σ probability of successful object retrieval shape parameter subscription cost User utility 1 Minimum service level for user i 0.5 σ 0.2 0.4 0.6 0.8 1 -0.5 -1

ISP Utility Function We express the utility of the ISP as: cost per unit of external bandwidth fixed charge profit from subscribers’ fee The ISP starts the service only if

Modeling traffic locality Probability that there exist at least one internal copy of an object replicated r times in the system Number of internal copies Number of external copies Probability to download from internal (existing) copy (i.e., exploit traffic locality) :

Modeling object replication We need to compute the number r of replicas of an object at the time the object is requested, taking into account: Different popularity of contents Temporal evolution of object popularity Introduction of new contents Cancellation of replicas by the users We have developed an analytical technique based on Poisson shot noise processes

Modeling object replication Example: (2 objects) popularity A video from the news A popular song time t1 t2 request

Results Assumption: n identical users N = 50 millions request rate by user (object/day) introduction of new contents by user (object/day) Minimum required external bandwidth:

The impact of object replication (r) Minimum required bandwidth Bmin (objects/day) 5000 r = 500 r = 1000 4000 r = 1500 3000 2000 1000 10000 20000 30000 40000 Number of users, n

The impact of efficacy in exploiting traffic locality ( ) Minimum required bandwidth Bmin (objects/day) 100000 = 0.25 = 0.5 80000 = 0.75 = 1.0 60000 40000 20000 100000 300000 500000 700000 Number of users, n

Impact of subscription cost, c 40000 c = 0.25 UISP Utility of ISP c = 1.0 30000 nmin c = 1.4 20000 c = 1.6 10000 -10000 5000 10000 15000 20000 25000 30000 Number of users, n

Critical mass of users, nmin 140000 β2 = 6 nmin 120000 β2 = 5 Cost per unit of bandwidth β2 = 4 100000 β2 = 3 80000 60000 40000 20000 200 400 600 800 1000 Average object replication, r

Model refinements Impact of finite bandwidth of the users

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 !)

Impact of asymmetric access bandwidths (for fixed number of users = 20000) 12000 Minimum required bandwidth Bmin (objects/day) bd = 0.1 bu bd = bu 10000 bd = 2 bu bd = 3 bu 8000 bd = 4 bu 6000 4000 2000 500 1000 1500 2000 2500 3000 3500 4000 Upload bandwidth, bu

The impact of bd Number of users, n bd = 2000 bd = 1000 bd = 500 7000 bd = 2000 Minimum required bandwidth Bmin (objects/day) bd = 1000 6000 bd = 500 bd = 100 5000 bd = 10 4000 3000 2000 1000 5000 10000 15000 20000 25000 30000 Number of users, n

Conclusions We have developed a simple analytical model to: analyze the interaction between P2P traffic and ISP understand the impact of several parameters guide the selection of the optimal strategy to manage P2P traffic Future work More model refinements Multiple ISPs competing with each other

Questions ? Comments ?