A Hierarchical Characterization of a Live Streaming Media Workload

IEEE/ACM Trans. Networking, Feb. 2006Eveline Veloso, Virgílio 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:

http://vc.cs.nthu.edu.tw/home/paper/codfiles/clchan/200507191203/A_Traffic_Characterization_of_Popular_On-Line_Games.ppt

Basic statistics of the trace used in this paper

MicrosoftMediaServer…

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 Toff.

Transfer layer In session ON time During transfer ON time, a client is served one or more liv

e streams. Transfer OFF times correspond loosely to “think” time

s.

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 Toff

Session ON time Session OFF time Transfers per session Interarrivals of session transfers

Relationship between number of sessions and Toff

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 distributio

n 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 Kbpscongestion

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”