Mar 1, 2004 Multi-path Routing CSE 525 Course Presentation Dhanashri Kelkar Department of Computer Science and Engineering OGI School of Science and Engineering

1 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Multi-path Routing A. Akella, B. Maggs, S. Seshan, A. Shaikh, R. Sitaraman, "A Measurement-Based Analysis of Multihoming", ACM SIGCOMM D. Andersen, A. Snoeren, H. Balakrishnan, "Best-Path v. Multi-Path Overlay Routing", IMC 2003.

2 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Multihoming Advantages – The Gist A study of multihoming performance and reliability ‣ Data collected from Akamai content distribution network ‣ High-volume content providers ‣ Enterprises that mainly receive data Analysis: ‣ Improve performance and reliability ‣ Choosing right set of providers important

3 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Multihoming Technique to achieve resilience to service interruptions Customer network having more than one external link, either to single ISP or to different providers Mainly used for reliability

4 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering K-Multihoming Customer network multihomed to K (K≥2) service providers Expect incremental performance

5 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Multihoming – Two Models Enterprise perspective: ‣ Route data being downloaded through appropriate ISP Web server perspective: ‣ Route data being provided through appropriate ISP Does smart routing improve performance? Does choice of ISPs matter?

6 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Data Collection – Enterprise Perspective 2-Multihoming Data set A1 ‣ 27 monitoring nodes ‣ Two nodes per city connected to different ISP ‣ Every 6 min. nodes download objects from Akamai customers ‣ Log turnaround time for request Akamai Customer ISP1 ISP2 Monitor 1 2 Enterprise Stand-in

7 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Data Collection – Enterprise Perspective K-Multihoming (K>2) Data set H1 ‣ Multiple Akamai servers per city ‣ Each server connected to different ISP ‣ Servers download from customers periodically ‣ Log avg turnaround time each hour

8 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Performance – 2-multihoming Use best provider for each download instead of single provider for all downloads Performance metric: Measures how much each ISP loses compared to multihoming solution (≥1)

9 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Performance – K-Multihoming Performance metric: particular K-multihoming solution Best multihoming obtained if we choose best of all ISPs

10 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Enterprise 2-Multihoming: Results 2-multihoming shows performance benefits but to varying degrees

11 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Enterprise K-Multihoming Performance Each line represents different city No significant improvement after 4 or 5 Knowing best ISP in advance is important

12 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Data Collection – Web Server Perspective

13 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Web Server Perspective – Cont’d Data set A2: ‣ In 5 metro areas, pick servers attached to distinct upstream ISPs ‣ Every 6 min. each server downloads 50 KB object from other Akamai servers ‣ Turnaround time for request

14 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Web Server K-Multihoming Use Akamai servers to emulate multihomed data centers and their active clients Metric for comparison: same as with enterprises Not much benefit beyond K=4

15 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Reliability Data set containing traceroute measurements from nodes of keynote systems to Akamai servers ‣ 50 geographically diverse keynote nodes, 2 per city ‣ 20 Akamai servers per city (top 20 ISP) Information about IP-level connectivity Robustness to IP-level failures

16 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Reliability Metrics Fraction of total path diversity captured by solution ‣ Higher value shows better performance Degree of overlap in paths ‣ Lower value shows better performance

17 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Reliability Analysis For both metrics, significant difference in optimal, average, and worse solution ‣ Difference about 80% Choosing ISPs very crucial

18 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Conclusion Multihoming helps, at least 20% improvement on average ‣ But not much beyond 4 providers Careful choice necessary ‣ Cannot just pick top individual performers ‣ Poor choice can affect performance significantly

19 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Best-path vs. Multi-path Routing Analysis of performance of reactive and mesh routing Reactive routing: measure path quality using probes and send on best path Mesh routing: send redundant duplicates

20 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Design Probe-based reactive overlay routing ‣ Periodic probes for availability, latency, loss rate ‣ Best path performance Redundant multi-path routing ‣ Sends redundant data to multiple paths ‣ Path independence

21 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Routing Methods ‣ DirectSingle packet, direct path ‣ Direct direct2 packets, direct, no spacing ‣ DD 10ms2 packets, direct, 10ms spacing ‣ DD 20ms 2 packets, direct, 20ms spacing ‣ Lat Reactive routing, min latency ‣ Loss Reactive routing, min loss ‣ Direct Rand 2 pkts, Redundant routing ‣ Lat Loss2 pkts, Redundant multi-path

22 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Duplication Reduces Loss Rate Type Loss % direct 0.42 direct direct 0.30 dd 10ms 0.27 dd 20ms 0.27 Lat 0.43 Loss 0.33 Direct Rand 0.26 Lat Loss 0.23

23 Mar 1, 2004Dhanashri Kelkar – OGI School of Science and Engineering Measurement Summary Redundant beats reactive for low loss Reactive finds specific good paths ‣ Latency improvements ‣ Low loss paths