A Hierarchical Characterization of a Live Streaming Media Workload IEEE/ACM Trans. Networking, Feb 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. 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: 203/A_Traffic_Characterization_of_Popular_On-Line_Games.ppt

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

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 T off.  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.

Relationship between client activities and ON/OFF times

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

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

Client diversity: transfers over ASs

Client diversity: transfers over countries

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

Cumulative distribution of number of active clients (cumulative)

Temporal behavior of number of active clients: over entire trace

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

Temporal behavior of number of active clients: hourly

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

Client interarrival times: frequency What is the unit of frequency? It might be 1.instance/second (x) 2.instance/request (?) 3.percentage (?)

Client interarrival times: CCDF CCDF: Complementary Cumulative Distribution Function

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.

Client interarrival times: piece-wise- stationary Poisson process

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

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

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

Session layer characteristics  Number of sessions Threshold T off  Session ON time  Session OFF time  Transfers per session  Interarrivals of session transfers

Relationship between number of sessions and T off 3600

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

Distribution of session ON times

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

Distribution of session OFF times

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

Number of transfers per session: frequency

Number of transfers per session: CCDF

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

Session transfer interarrivals: frequency

Transfer layer characteristics  Number of concurrent transfers Exponential distribution  Transfer length and client stickiness  Transfer interarrivals  Transfer bandwidth

Concurrent transfers over all sessions (cumulative)

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

Transfer lengths

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

Transfer interarrival times

Temporal behavior of transfer interarrival times: over entire trace

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

Temporal behavior of transfer interarrival times: hourly

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

Aggregate bandwidth

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

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.

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