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A Hierarchical Characterization of a Live Streaming Media Workload

A Hierarchical Characterization of a Live Streaming Media Workload. IEEE/ACM Trans. Networking, Feb. 2006 Eveline Veloso, Virg í lio Almeida, Wagner Meira, Jr., Azer Bestavros, and Shudong Jin. Motivation. The characteristics of live media and stored media are different.

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A Hierarchical Characterization of a Live Streaming Media Workload

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  1. A Hierarchical Characterization of a Live Streaming Media Workload IEEE/ACM Trans. Networking, Feb. 2006 Eveline Veloso, Virgílio Almeida, Wagner Meira, Jr., Azer Bestavros, and Shudong Jin

  2. Motivation • The characteristics of live media and stored media are different. • Stored media object: user driven • Be directly influenced by user preferences • Live media object: content driven • Be directly influenced by aspects related to the nature of the object A Traffic Characterization of Popular On-Line Games: http://vc.cs.nthu.edu.tw/home/paper/codfiles/clchan/200507191203/A_Traffic_Characterization_of_Popular_On-Line_Games.ppt

  3. Basic statistics of the trace used in this paper Microsoft Media Server 7 Kbps 18 Kbps 32 Kbps 57 Kbps stream 1 48 different cameras … stream 2

  4. Characterization hierarchy • Client layer • Session layer • The interval of time during which the client is actively engaged in requesting live streams that are part of the same service such that the duration of any period of no transfers between the server and the client does not exceed a preset threshold Toff. • Transfer layer • In session ON time • During transfer ON time, a client is served one or more live streams. • Transfer OFF times correspond loosely to “think” times.

  5. Relationship between client activities and ON/OFF times

  6. Client layer characteristics • Topological and geographical distribution of client population • Zipf-like distribution • Most requests are issued from a few regions • Client concurrency profile • Client interarrival times • Client interest profile

  7. Client diversity: IP addresses over ASs Autonomous System (AS): the unit of router policy, either a single network or a group of networks that is controlled by a common network administrator

  8. Client diversity: transfers over ASs

  9. Client diversity: transfers over countries

  10. Client layer characteristics • Topological and geographical distribution of client population • Client concurrency profile • Periodic behavior • Client interarrival times • Client interest profile

  11. Cumulative distribution of number of active clients (cumulative)

  12. Temporal behavior of number of active clients: over entire trace

  13. Temporal behavior of number of active clients: daily Weekend have slightly higher clients than weekdays

  14. Temporal behavior of number of active clients: hourly

  15. Client layer characteristics • Topological and geographical distribution of client population • Client concurrency profile • Client interarrival times • Pareto distribution • Piece-wise-stationary Poisson process • Client interest profile

  16. Client interarrival times: frequency • What is the unit of frequency? • It might be • instance/second (x) • instance/request (?) • percentage (?)

  17. Client interarrival times: CCDF CCDF: Complementary Cumulative Distribution Function

  18. Discuss • The client arrival process is not stationary in that it is highly dependent on time. • It is natural to assume that over a very short time interval, such a process would be stationary, and may indeed be Poisson. • Piece-wise-stationary Poisson arrival • 15 min.

  19. Client interarrival times: piece-wise-stationary Poisson process

  20. Client layer characteristics • Topological and geographical distribution of client population • Client concurrency profile • Client interarrival times • Client interest profile • Characterizing live content popularity is not meaningful  characterizing the “interest” of a client in the live content is more appropriate • Zipf-like distribution • Most requests are issued from a few clients

  21. Client interest profile: client rank v.s. transfer frequency Rank: number of transfers for that client

  22. Client interest profile: client rank v.s. session frequency Rank: number of sessions for that client

  23. Session layer characteristics • Number of sessions • Threshold Toff • Session ON time • Session OFF time • Transfers per session • Interarrivals of session transfers

  24. Relationship between number of sessions and Toff 3600

  25. Session layer characteristics • Number of sessions • Session ON time • Lognormal distribution • Session OFF time • Transfers per session • Interarrivals of session transfers

  26. Distribution of session ON times

  27. Session layer characteristics • Number of sessions • Session ON time • Session OFF time • Exponential distribution • Transfers per session • Interarrivals of session transfers

  28. Distribution of session OFF times

  29. Session layer characteristics • Number of sessions • Session ON time • Session OFF time • Transfers per session • Pareto distribution • Interarrivals of session transfers

  30. Number of transfers per session: frequency

  31. Number of transfers per session: CCDF

  32. Session layer characteristics • Number of sessions • Session ON time • Session OFF time • Transfers per session • Interarrivals of session transfers • Lognormal distribution

  33. Session transfer interarrivals: frequency

  34. Transfer layer characteristics • Number of concurrent transfers • Exponential distribution • Transfer length and client stickiness • Transfer interarrivals • Transfer bandwidth

  35. Concurrent transfers over all sessions (cumulative)

  36. Transfer layer characteristics • Number of concurrent transfers • Transfer length and client stickiness • Lognormal distribution • The long tail of the transfer length distribution is due to the client’s willingness to “stick” to the live stream. • Transfer interarrivals • Transfer bandwidth

  37. Transfer lengths

  38. Transfer layer characteristics • Number of concurrent transfers • Transfer length and client stickiness • Transfer interarrivals • Like client arrivals • Pareto distribution • Transfer bandwidth

  39. Transfer interarrival times

  40. Temporal behavior of transfer interarrival times: over entire trace

  41. Temporal behavior of transfer interarrival times: daily Weekends have lower average interarrivals than weekdays (but more clients)  Due to channel browsing?

  42. Temporal behavior of transfer interarrival times: hourly

  43. Transfer layer characteristics • Number of concurrent transfers • Transfer length and client stickiness • Transfer interarrivals • Transfer bandwidth • Client-bound bandwidth • Congestion-bound bandwidth

  44. Aggregate bandwidth

  45. Frequency distributions of transfer bandwidth client: 58.6 Kbps 32.5 Kbps 17.6 Kbps 6.87 Kbps congestion

  46. Across multiple live media workloads • Another live streaming server for a “news and sports” radio station • The differences of two live streaming services • Client interarrival times • Session transfer interarrival times • Transfer interarrival times • These differences are due to the different interactions between clients and live streams in the workloads.

  47. Summary of the characteristics of the “Reality Show” and “News and Sports”

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