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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Data Collection

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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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Transfer Bandwidth

Bounded by client connection speed

Bounded by congestion

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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

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Comments

Piece-wise Poisson ProcessA good way to model the client arrival

processBut we need a priori knowledge of the

average client arrival rate with a number of short period

The client arrival pattern also depends on the content

Hard to be used