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A Hierarchical Characterization of a Live Streaming Media WorkloadE. Veloso, V. Almeida
W. Meira, A. Bestavros, S. Jin
Proceedings of Internet Measurement Workshop, ACM SIGCOMM, Nov. 2002
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Outline
Introduction Data Collection Client Layer Characteristics Session Layer Characteristics Transport Layer Characteristics Conclusions
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Introduction
Workload characterization is important forPerformance evaluation and predictio
nCapacity planning
Rejecting client for a live stream is not a viable solutionValue of live streams is the livenessLose paying customers
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Introduction
Only a small number of studies on characterizing pre-recorded streaming media workloads
This paper provides a characterization for live streams
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Introduction
Compare to Stored streams, Live streams exhibitStronger temporal patterns of workloa
dFewer possible VCR functionsLess correlations between different va
riables• Users are less likely to stop viewing when
QoS degrades
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Data Collection
A popular live show in Brazil “Reality TV Show” in early 2002, last for 90 days
The live streams provided feeds captured from one of the cameras embedded in the environment surrounding the contestants
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Data Collection
For each entry of the log, it contains Client identification—e.g., IP address, player ID Client environment specification—e.g., OS version, CPU Requested object identification—e.g., URI of stream Transfer statistics—e.g., loss rate, average bandwidth Server load statistics—e.g., server CPU utilization Other information—e.g., referer URI, HTTP status Timestamp in seconds of when log entry was generated.
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Client Layer Characteristics
Focus on the characteristics of the client population
Clients are identified by the unique player ID
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Client Topological and Geographical Distribution Follow a Zipf profile with parameter α=1.29, 1.49 and 5.
4 respectively
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Temporal behavior of number of active clients
Diurnal Effect on the live content Periodic Depends on the day of week
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Client Arrival Process
Client arrival process is not poisson Can be estimated by a sequence of pi
ece-wise-stationary Poisson arrival processes
Interarrival time of clients from logs
Interarrival time of a piece-wise-stationary Poiss
ion process
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Client Interest Profile
Using transfer frequency as a measure of client interest in the content
Client interest in a single object follows a Zipf distirbution
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Session Layer Characteristics Focus on individual client activity The trace does not explicitly identify the delimiters of a
session The authors choose a session timeout parameter Toff t
o determine the number of sessions Toff = 3600 seconds
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Session ON/OFF Time
ON times are highly variable Due to live content instead of temporal behaviors Lognormal
OFF times form ripples around specific values In multiple of days => revisting daily or every two days Exponential
Session ON Time vs Session starting time
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Transport Layer Characteristics Focus on individual unicast data transfers Temporal behavior of no. of concurrent tran
sfers Periodic over a weekly and daily period Similar to the temporal behavior of no. of acti
ve clients
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Temporal behavior of transfer interarrival times
Request arrival process is also periodic and non-stationary
Due to the diurnal behavior
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Transfer Length & Client Stickiness
Similar to the session ON time The long tail shows the willingness of
the client to “stick” to the live object
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Representativeness of Findings Compared the findings with another live show “Live
News & Sports” Sport news & soccer players interviews 28558 requests from 12867 distinct clients within 2
weeks
interarrival times depends on the content
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Conclusions
Client Layer Arrival process can be modeled by a piece-wise statio
nary Poisson process Identity of the client making a request can be modele
d by a Zipf distribution Session Layer
ON times follows Lognormal distribution OFF times follows exponential distribution
Transfer Layer Arrival process can be modeled by a piece-wise statio
nary Poisson process Transfer bandwidth is primarily determined by client c
onnection speed while 10% of transfers are being severely limited by congestion