Measurement Modeling and Analysis of a Peer2 Peer

Measurement, Modeling, and Analysis of a Peer-2 -Peer File -Sharing Workload Presented For Cs 294 -4 Fall 2003 By Jon Hess

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload • Goal - Overview – Determine if the Ka. Za. A search space is queried in such a way that a group of 25, 000 clients can satisfy most of their own requests.

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload • Goals - Details – Capture an extensive trace – Utilize that trace to understand filesharing traffic flows – Model user and object activity – Determine inefficiencies in the distribution model – Propose solutions to inefficiencies

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload • Motivations – Beginning in 1999 -2000 file-sharing traffic began to exceed HTTP traffic in terms of aggregate bandwidth consumed – File-sharing traffic is much less understood than HTTP traffic even though it represents such a large segment of bandwidth usage – Bandwidth is expensive 2000 HTTP Traffic 2002 P 2 P Traffic

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload • The Trace – 2 Machines – 203 days 5 hours and 6 minutes – 22. 7 TB of Ka. Za. A file transfer traffic – Captured seasonal variations • End of spring • Summer • Fall semester

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload • Trace Conclusions – Users are patient • 30 minutes to retrieve a small object • Up to 1 week to retrieve a large object – Users consume less as they age

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload • Trace Conclusions – Users machines are not very active • A session is an unbroken length of time where a client has one or more file transfers in progress. • Average sessions are only 2 minutes – 90 th percentile 28 minutes • Over the life of a client, it is only active 5. 54% of the time or 0. 20% of the trace period – 90 th percentile clients are active most of their life, and 4. 15% of the trace – Without control traffic analysis, is this meaningful?

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload Session Lengths Transfer A Transfer B Transfer D 3 Minutes Transfer E 2 Minutes

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload • Trace Conclusions – Objects – Most requests are for small objects – 91% – Most bytes transferred are part of large objects – 65% – There are many small objects – There are few large objects – Small Objects’ popularity is subject to heavy churn • • No small object was in the top 10 for all 6 months Only 1 large object lived in the top 10 for 6 months 44 large files remained in the top 100 for 6 months The most popular small objects are new objects – Most requests are for old objects

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload • Fetch-at-most-once – Once a Ka. Za. A user obtains an object, they will not need to retrieve another copy • 94% of Objects are fetched once per user • 99% are fetched less than twice per user – Stems from the fact that media files are immutable and never ‘stale’ • You may refresh ‘slashdot. org’ three times a day, but there is no point download ‘thriller. mpeg’ seventeen times. – This keeps Ka. Za. A workload from following a Zipf curve even though object popularity does.

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload • Workload Modeling – Create a set of objects and give them popularity based on a zipf distribution – Create a set of clients that requests objects in proportion to there popularity – Have each client ‘fetch-at-most-once’ – Measure the distribution of transfers • Does it follow a zipf curve • How many big-object requests can a population of size N satisfy

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload Popular objects are not requested as curve would predict

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload • Would a proxy cache help? – At first the proxy will cache the popular objects and succeed. – But as ‘fetch-at-most-once’ draws clients away from the Zipf curve and the proxy begins to fail. – What happens if we increase density of popularity? • Curve starts higher and falls faster

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload • Previous model did not insert new objects. – New popular objects tend to ‘correct’ the work load. – Through providing locality • New clients however do not help, they contribute to keeping old object’s popular and destroy locality

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload • Validating The Model – Capture parameters that are inputs to the model from the trace • • • Number of clients Number of objects User request rate Probability user requests given file - Guess Probability of popularity of new objects - Guess Object arrival rates – Guess – Run simulation with harvested parameters – See if simulation predicts what actually happened

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload Simulation seems to successfully predict reality. But with three free variables used to tune results, is this fair?

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload • What inefficiencies can we eliminate? – Analysis against the trace shows • 86% of object transfers were from external sources when an internal source possessed the object. • A traditional proxy, given the resources, could cut bandwidth utilization by 86% – Would have to host pirated data • Could use a proxy redirector instead. Must know the availability of the objects – Control traffic is obfuscated • Build locality into the protocol – Does this sacrifice anonymity?

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload • How successful would a locality aware protocol be? – Assume that a client is available for periods the trace shows it as active • During a file transfer - extremely conservative

Measurement, Modeling, and Analysis of a Peer-2 -Peer File-Sharing Workload • Questions? Will increasing efficiency decrease load as the authors would like? Or simply increase work achieved per dollar? Do clients have insatiable appetites? Are you worried that a large number of queries might have already been locally satisfied?