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Performance Characteristics of Mirror Servers on the Internet

This study examines the performance characteristics of mirror servers on the internet and their implications for server selection. The results show that server performance can vary widely, but clients can achieve near-optimal performance by considering only a few servers. The study also explores the relationship between transfer time, rank changes, and document choice.

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Performance Characteristics of Mirror Servers on the Internet

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  1. Performance Characteristics of Mirror Servers on the Internet • Andy Meyers • Peter Dinda • Hui Zhang {acm,pdinda,hzhang}@cs.cmu.edu Carnegie Mellon University Presented by: Herain Oberoi Internet Services 18-845

  2. Purpose • Growing number of Mirror servers on the web • Clients need to determine which mirror will offer the best performance • Measuring a relatively large data-set might help in understanding actual behavior of Mirror Servers • Understanding behavior may help design an effective algorithm for server selection

  3. Topics Covered • Introduction • Definitions • Five main Results • Data Collection and Characteristics • Time Scale of Changes • Transfer Time and Rank Changes • Small Server Sets • Server and Document Choice • Implications and Conclusions • Summary • Discussion Questions

  4. Introduction • Four Categories of Works • Network Layer Selection • Application Layer Selection • Metric Evaluation • Measurement Studies • Data Accumulation • Analysis

  5. Definitions • Mirror: A replica of another Server. • Group of Fetches: Process of visiting all the servers and collecting all the documents. • Collision: When two clients visit a server at the same time. • Rank: Indicates the order of server preference for each data set in each group of fetches. • Optimum Performance: A server delivers a document no longer than 10% more than the best transfer time. • Server Set: The minimum subset of servers for which a client will get optimum performance.

  6. 5 Main Results • Performance can vary widely from one server to another. • Clients can achieve near-optimal performance by considering only a few servers from a group of mirrors. • The probability of any server’s rank change depends very little on the time scale over which the rank changes. • There is a weak but detectable link between a server’s change in transfer time and rank. • Server choice is independent of document choice in most instances.

  7. Data Collection Methodology • Server Setup • 47 Mirror Servers • 3 Sets of Mirrored web sites • Mars 20, Apache 11, News 16 • 11 documents (2Kb – 1.3Mb) • Mars 5, Apache 5, News 1

  8. Data Collection Methodology • Client Setup • 9 Clients from different Univ. • Perl Script using Lynx, run by ‘cron’ • Sequential requests • Sleep for a random time after a ‘group of fetches’

  9. Average response times • Average response time: • Mars 17mins, Apache 13mins, News under 1min

  10. Time Scale of Rank Changes • Good servers remain good for long periods of time. The cumulative probability of rank changes over increasing time scales.

  11. Transfer time and Rank Changes • Client can only observe transfer time • Need correlation between transfer time and rank change Cumulative probability of rank changes over increasing changes in observed transfer times.

  12. Small Server Sets • A client need evaluate only a small number of servers to achieve near-optimal performance.

  13. Server and Document Choice • Document choice has a weak effect on server choice. • Document size may have more dependence. • Actual penalties are on the order of hundreds of milliseconds.

  14. Compromises Made • Lynx instead of Netscape or IE. • 5 minute request timeout. • Unable to map network conditions to results. • Sequential requests under-represent periods of network congestion. • News site not a true Mirror. • Rank computed by assuming fetch times generated under identical conditions. • Selections made in the absence of a selection algorithm, which may alter server performance.

  15. Summary • Final results: • Time Scale and Server performance are not good indicators of server rank. • Large performance drops are good indicators that a servers rank is likely to change. • Document content does not affect server selection. • Future Work: Longer traces, more Mirror sets • Implications?

  16. Discussion Questions • How would another browser affect results? • How do concurrent clients affect server load? • Are three Mirrors enough? • What if clients used selection algorithms? • Content based selection, could content caching help? (ideas from the LARD algorithm)

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