1. Evaluation of a Novel Two-Step Server Selection Metric Presented by Karthik Lakshminarayanan 11-26-2003

2. Problem statement Goal: Client wants to download content from the best of k servers, i.e. minimize total time to transfer a document Issues to consider: –Cost of choosing the target server Lightweight mechanisms preferable –Stability of ordering (over a period of time) More energy can be expended if stability is high –Nature of content and corresponding workloads Frequency of downloads, and size of documents

3. Outline Problem statement Proposed algorithm Existing/possible approaches Methodology Results

4. “Novel” two-step server selection Pick k best servers out of the entire set by using pings (k ~ 5) Retain the subset for a period of n days Choose servers from the subset of k servers –Choose from this subset randomly –Can choose from subset based on other metrics Call this Ping-twostep for convenience Main delay due to network delays, not server load

5. Selection metrics Dynamic metric (adapt to network condition) –Ping –Transfer of small files –Ping-twostep Static metric (oblivious to network condition) –Number of hops –Number of AS hops –Random Summary: Ping-twostep performs best!

6. Methodology Six client machines (USC, UNC, UCSC, Umass, UDel, Purdue) 193 servers in tucows.com mirror network Collected info continuously for 41 days Each “run” comprised –5 ICMP pings –Traceroute –Transfer times of files from 10KB – 1MB More extensive set of servers than previous work

7. Comparison –Ping metric RTT not always indication of transfer time –Not surprising! Some oddities experienced with –UNC –Purdue Relative positions between ping & 10k vary across nodes Do not care about the low end of the bw spectrum!

8. Comparison – Small file transfers Improved with size of transfer Low correlation between time for small transfer vs. time for large transfers

9. Comparison – Static selection Hop count –Mostly equivalent to random selection when used to estimate transfer time –Little correlation (restricted to USA and Canada)

10. Comparison – Static selection Hop count –Mostly equivalent to random selection when used to estimate transfer time –Little correlation (restricted to USA and Canada)

11. Comparison – Static selection Hop count –Mostly equivalent to random selection when used to estimate transfer time –Little correlation (restricted to USA and Canada) AS hop count –Does not work well for them –Global IP-Anycast (GIA) uses this Queried using BGP Small hop counts miss many servers, large hop counts would result in too much traffic

12. Stability of server ranking 70-98% of changes in rank are between zero and ten for top servers Average servers experience much higher change in rank Rankings of top servers is stable

13. Stability of server transfer times Consider different sizes of subsets of 193 hosts –Number of top servers in an n-subset is a small fraction of the size of subset (<10%) Little overlap of top servers across clients Consider a subset of servers How many of them were ever at the top in the 41-day period Caveat: they consider only the “top” server

14. Ping-random Motivation revisited: –Ping technique Low overhead Good performance –Top servers stable over time Choosing from the small subset: –Random – provides load-balance –Ping – use ping again among that set –Ping-best (for comparison)

15. Performance of Ping-Random Ping-ping >~ Ping-random > (10k, Ping) Ping-ping might not perform load-balance well

16. Effect of size of ping sets Influenced greatly by the size of ping sets chosen 40% of servers ever ranked first were in 20% of the pings

17. Effects of selection algorithms Load-balancing –Different clients have different top servers Oscillations –Respond to changing network conditions –“Fortunately, it is unlikely that many clients would be running tests at the same time” No quantitative results!

18. Discussion How do we use this in practice? –Useful for large file transfers –What about small web transfers? GNP, Geoping approaches might work –Set of servers is static? How can DHTs help in anycast? –DOLR network for proximity –Embed location information in Ids –Use longest-prefix matching tricks (like i3)