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Aditya Akella The Performance Benefits of Multihoming Aditya Akella CMU With Bruce Maggs, Srini Seshan, Anees Shaikh and Ramesh Sitaraman.

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Presentation on theme: "Aditya Akella The Performance Benefits of Multihoming Aditya Akella CMU With Bruce Maggs, Srini Seshan, Anees Shaikh and Ramesh Sitaraman."— Presentation transcript:

1 Aditya Akella The Performance Benefits of Multihoming Aditya Akella CMU With Bruce Maggs, Srini Seshan, Anees Shaikh and Ramesh Sitaraman

2 Aditya Akella2 Multihoming Announce address space to both providers One announcement has longer AS path AS prepend; For backup Primary motivation: reliability AS 300AS 200 Internet AS 101 4.0.0.0/19 4.0.0.0/19 AS-path: 101 101 101 Destination 4.0.0.0/19 AS-path: 101

3 Aditya Akella3 Multihoming Announce address space to both providers One announcement has longer AS path AS prepend; For backup Primary motivation: reliability AS 300AS 200 Internet AS 101 AS-path: 101 101 101 Destination AS-path: 101

4 Aditya Akella4 Multihoming Announce address space to both providers One announcement has longer AS path AS prepend; For backup Primary motivation: reliability AS 300AS 200 Internet AS 101 Destination AS-path: 101 AS-path: 101 101 101

5 Aditya Akella5 Multihoming for Performance Intelligent “route control” products E.g., RouteScience Observation: Performance varies with providers, time Help stubs extract performance from their ISPs  Multihoming no longer employed just for resilience No quantitative analysis of performance benefits yet ISP2ISP1 Internet Destination Route-control Use ISP1 or 2?

6 Aditya Akella6 Our Goal Assuming perfect information, what is the maximum performance benefit from multihoming? How can multihomed networks realize these benefits in practice? For an enterprise or a content provider in a metro area…

7 Aditya Akella7 Two Distinct Perspectives Popular content providers Web server Primarily data consumers Goal: Optimize download performance Primarily data sources Goal: Optimize client-perceived download performance Enterprise Active clients

8 Aditya Akella8 Measurement Challenges In each metro area, need… Connections to multiple ISPs Akamai infrastructure satisfies this Widespread presence Many servers singly homed to different ISPs City#Providers Atlanta15 Boston10 Chicago23 Dallas21 Los Angeles32 New York39 San Francisco60 Seattle18 Washington DC29 Enterprise Multihoming

9 Aditya Akella9 Outline of the Talk Enterprise performance benefits Web server performance benefits Practical schemes Conclusion

10 Aditya Akella10 Enterprise Performance Use Akamai’s servers and monitoring set-up to emulate multihomed enterprises Two distinct data sets: 2-multihoming k-multihoming, k>2 Popular content providers Enterprise Primarily data consumers Goal: Optimize download performance

11 Aditya Akella11 Enterprise 2-Multihoming Monitors download object every 6 mins from origins Logs stats per download Four cities with two monitors Monitors attached to distinct, large ISPs perf monitor metro area ISP 1ISP 2 selected content providers P1P1 P 80

12 Aditya Akella12 Enterprise 2-Multihoming Monitors download object every 6 mins from origins Logs stats per download Four cities with two monitors Monitors attached to distinct, large ISPs Stand-ins for 2-multihomed enterprise metro area ISP 1ISP 2 selected content providers P1P1 P 80 perf monitor Enterprise

13 Aditya Akella13 Enterprise 2-Multihoming Monitors download object every 6 mins from origins Logs stats per download Four cities with two monitors Monitors attached to distinct, large ISPs Stand-ins for 2-multihomed enterprise Look at top 80 customer content providers Log turn-around time REQRESP Akamai node (perf monitor) origin server turnaround metro area ISP 1ISP 2 selected content providers P1P1 P 80 Enterprise

14 Aditya Akella14 Characterizing Performance Benefit Compare single ISP performance to 2-multihoming Best one used at any instant Assume full knowledge of the best provider at any instance Metric for ISP1 = average downloads turn-around time using ISP1 High metric  ISP1 has poor performance Metric = 1  ISP1 is always better than ISP2 turn-around time using best ISP average downloads

15 Aditya Akella15 Enterprise 2-Multihoming: Results Definite benefits… but to varying degrees Metric for each ISP

16 Aditya Akella16 2-Multihoming: Details Analyze the benefit of using two given large providers together May not be the best choice, but… Reflective of typical route-control deployment Still unanswered questions: What is the benefit from using the best providers? How to pick them? What is the benefit from using more providers?

17 Aditya Akella17 Enterprise k-multihoming New data set emulates a different form of multihoming Best ISP used each hour vs. 2-multihoming dataset  best ISP each transfer  Analysis of this data gives lower bound on actual benefits Metric for k-multihoming: turn-around time using best set of k ISPs Best ISP known beforehand average hours turn-around time using all ISPs

18 Aditya Akella18 Enterprise k-Multihoming Performance k-multihoming Performance Beyond k=4, marginal benefit is minimal

19 Aditya Akella19 Enterprise k-Multihoming Performance Best set of k vs. set of best k (NYC) ISPIndividual Rank 1-multi perf ISP 111.72 ISP 221.93 ISP 392.61 ISP 432.05 ISP 542.29 ISP 6193.16 ISP 7173.03 ISP 8132.93 Beyond k=4, marginal benefit is minimal Cannot just pick top k individual performers k-multihoming Performance

20 Aditya Akella20 Outline of the Talk Enterprise performance benefits Web server performance benefits Practical schemes Conclusion

21 Aditya Akella21 Web server k-Multihoming Use Akamai servers to emulate multihomed data centers and their active clients Web server Active clients Primarily data sources Goal: Optimize client-perceived download performance

22 Aditya Akella22 Web server Multihoming: Data CDN servers metro areas In 5 metro areas, pick servers attached to unique ISPs

23 Aditya Akella23 Web server Multihoming: Data CDN servers metro areas In 5 metro areas, pick servers attached to unique ISPs Stand-ins for multihomed web server Web server

24 Aditya Akella24 Web server Multihoming: Data CDN servers metro areas In 5 metro areas, pick servers attached to unique ISPs Stand-ins for multihomed web server Select nodes in other cities Stand-ins for clients For each metro area… The client stand-ins pull a 50K object from servers in the area Every 6 minutes Log turn around time Metric for comparison: same as with enterprises Web server

25 Aditya Akella25 Web server k-Multihoming: Results Not much benefit beyond k=4 providers Choice of providers must be made carefully k-multihoming PerformanceAverage of Random Choice

26 Aditya Akella26 Outline of the Talk Enterprise performance benefits Web server performance benefits Practical schemes Conclusion

27 Aditya Akella27 Simple Practical Solution In practice, subscriber must use history and a reasonable time-scale to make decisions Monitor performance across all providers Keep EWMA(  ) of performance to each destination across all ISPs Lower   more weight to fresh samples Every T minutes, choose ISP with best EWMA Evaluate effectiveness using Web server data Data still has 6-minute granularity

28 Aditya Akella28 Web Server: Practical Solution Need timely and accurate samples Recent samples should get a lot of weight (lower  )  =1, T=30 minutes  =10, T=30 minutes

29 Aditya Akella29 Conclusion Multihoming helps, at least 20% improvement on average But not much beyond 4 providers Careful choice necessary Cannot just pick top individual performers Performance can be hit by >50% for a poor choice In practice, need accurate, timely samples Higher preference to fresh samples

30 Aditya Akella30 Future Work Reasons for observed performance benefit Impact of ISP cost structure

31 Aditya Akella31 Extra slides Extra

32 Aditya Akella32 Performance Benefits: Other Questions Does performance improve with additional providers? Diminishing returns? How carefully should a subscriber choose providers to multihome to? Top individual ISPs? Random vs. informed?

33 Aditya Akella33 Enterprise 2-Multihoming: Results Definite benefits… but to varying degrees Longer turn-around times benefit more 90 t h 50 t h 10 t h Metric for each ISP Performance from 2-multihoming

34 Aditya Akella34 Enterprise k-Multihoming, k > 2 Servers download objects from origins Cache misses Log average turn-around times across all origins Averaged per hour CDN servers metro area ISP 1 all origin servers ISP 2ISP 3ISP K

35 Aditya Akella35 Enterprise k-Multihoming, k > 2 Servers download objects from origins Cache misses Log average turn-around times across all origins Averaged per hour Servers in metro area  stand-in for k-multihomed enterprise metro area ISP 1 all origin servers ISP 2ISP 3ISP K

36 Aditya Akella36 Enterprise k-Multihoming, k > 2 Servers download objects from origins Cache misses Log average turn-around times across all origins Averaged per hour Servers in metro area  stand-in for k-multihomed enterprise Form of multihoming where all traffic received via best upstream, per hour Finer control (per destination, per flow) would perform better  Lower bound on actual benefits metro area ISP 1 all origin servers ISP 2ISP 3ISP K

37 Aditya Akella37 Enterprise k-Multihoming Performance Beyond k=4, marginal benefit is minimal Contribution to overall benefit not always proportional to usage k-multihoming PerformanceRelative usage of ISPs (New York)

38 Aditya Akella38 Practical Multihoming Solution So far… Assume accurate, timely knowledge Pick best provider link for each transfer Assume we can switch arbitrarily often Optimal, but not necessarily realizable How do these limitations impact the practical implementation? How close to optimal can we get in practice?

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