1. An Efficient Approach for Content Delivery in Overlay Networks Mohammad Malli Chadi Barakat, Walid Dabbous Planete Project To appear in proceedings of IEEE CCNC’05

2. December 9, 2004 2 Outline ò Introduction ò Proposed solutions for server selection ò Our approach for content delivery ò Transfer time prediction function ò Prediction evaluation with real measures ò Conclusions and ongoing work

3. December 9, 2004 3 Introduction ò Service replication consists of providing the same service from many mirror servers distributed geographically å a service can be downloading : software, movies, mp3, etc. å can be replicated in CDN (e.g., Akamai), P2P (e.g., Kazaa) ò A client request must be served by one server among a set of replicated servers with certain QoS criterias Which criterias must be considered to choose this server ?

4. December 9, 2004 4 Related works ò IP anycasting : best effort delivery of an anycast datagram å deployment constraints ò DNS : distribute the IP addresses with a round robin algorithm ò Offering the addresses of all the mirror servers in a web page and let the client choose ò Other famous propositions : å client - server distance : geographic proximity, nb of hops, RTT,... å binning strategy : client must measure the RTT with a set of landmark points, å 2 tier strategy : using client proxies to estimate the performance on the path with the servers and register the servers load, å...

5. December 9, 2004 5 Our scheme concepts ò Application-layer anycasting : å more efficient and feasible to be deployed ò The best server must be characterized transparently to clients ò Performance on the path server - client must be considered : å available bandwidth, packet loss rate, RTT ò Server load and buffering capacity must be considered : å request waiting time, maximum congestion window imposed by server or client buffer limitation

6. December 9, 2004 6 2 main Goals ò Localize the best server in order to minimize the time required to serve a client å We consider a service that consists in client downloading files from a set of replicated servers using the TCP protocol å QoS provided to clients if the transfer time ò Avoid network and server congestion by considering the load on the network paths and on the servers

7. December 9, 2004 7 Our scheme ò each server, among a chosen set, predicts the content transfer time ò the central server ranks the servers from the best one to the worst one ò The client is served by the best server or from a set of best servers in parallel

8. December 9, 2004 8 Transfer time prediction ò Transfer time of a content transmitted using TCP : å E[Ts] is the mean request waiting time in the server å E[Lss] is the transfer time passed during the slow start phase å E[Lca] is the transfer time passed during the congestion avoidance phase ò We evaluate E[T s ] by modeling the socket’s arrival queue of a server and its associated threads (of number c) as an M/M/c queue

9. December 9, 2004 9 Transfer time prediction ò Max. sending rate that can be reached at the end of the slow start phase : ò R max_w imposed by TCP buffering limitation at sender or receiver ò R max_p imposed by packet loss on the network path å  = the rate of exponential growth of TCP CW during the slow start phase ò Transfer time spent in the slow start phase : å r = nb of slow start rounds

10. December 9, 2004 10 Transfer time prediction ò Transfer time spent in the congestion avoidance phase : å S = content size, E[S ss ] = nb of bytes transmitted in the slow start phase å R ca = TCP average throughput in the congestion avoidance phase : å R p = TCP throughput imposed by the packet loss rate P [Towsley et al.] We obtain an average ratio : PTT over ReTT equal to 96 %

11. December 9, 2004 11 Prediction Evaluation Transfer time prediction for small size contents

12. December 9, 2004 12 Prediction Evaluation Berlin -> Sophia Antipolis is better than Paris -> Sophia Antipolis due to the higher available bandwidth on the first path Transfer time prediction for a large content of size 75 MB when A limits the download rate

13. December 9, 2004 13 Prediction Evaluation Transfer time prediction when W max = 45 packets limits the download rate to 12 Mbps, while A = 24Mbps, P = 0 : Transfer time prediction when P limits the download rate to R p, while A and R w are much bigger :

14. December 9, 2004 14 Conclusions ò Service replication is necessary due to the increasing number of Internet users and the desire to improve the QoS ò We lack for approaches providing accurate best server selection ò We propose an efficient scheme : å ranking the servers from best to worst based on a metric which can predict accurately a content transfer time å our metric considers the limitations of the critical performance parameters in the network (available bandwidth, packet loss rate, RTT) along with the load on the servers ò Experimental results shows the accuracy of our best server selection and the big impact of our considered parameters ò Ongoing work : ò implementing the whole scheme in Planetlab ò examining both cases: point-to-point download and parallel download

15. December 9, 2004 15 Thank you Q & A Mohammad.Malli@sophia.inria.fr