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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”