Hossfeld qc man2015_context_monitoring_web

Prof. Dr. Tobias Hoßfeld
Chair of Modeling of
Adaptive Systems (MAS)
Institute for Computer
Science and Business
Information Systems (ICB)
University of Duisburg-Essen
www.mas.wiwi.uni-due.de
Can context monitoring improve
QoE? A case study of video flash
crowds in the Internet of Services
Hossfeld, Tobias; Skorin-Kapov, Lea;
Haddad, Yoram; Pocta, Peter; Siris,
Vasilios A.; Zgank, Andrej; Melvin, Hugh

Definition of Context and Context Influence Factors
• Context is any information that assists in determining
a situation(s) related to a user, network or device.
[A.K. Dey and G.D. Abowd. Toward a better understanding of context and
context-awareness, Technical Report Georgia Institute of Technology]
• Context refers to anything that can be used to
specify or clarify the meaning of an event.
[P. Reichl et al, Towards a comprehensive framework for QoE and user
behavior modelling, QoMEX 2015]
• Context influence factors are factors that embrace
any situational property to describe the user’s
environment in terms of physical, temporal, social,
economic, task, and technical characteristics.
[U. Reiter et al, Factors influencing quality of experience. In Quality of
Experience, pp. 55-72. Springer International Publishing, 2014.]
or system.
or system‘s

Context Monitoring and QoE Monitoring
mas.wiwi.uni-due.de 3
Context
monitoring
QoE
monitoring
e.g. device
capabilities
e.g. video
buffer status
e.g. user
expectations
e.g. predicted
traffic demands
e.g. available
resources
e.g. QoS
utilization
of data
Is context monitoring
more relevant than QoE monitoring
for managing QoE?

Context Factors
• Physical environment in which services and devices are used.
– home, office, commuting, and other places,
– indoors vs outdoors.
• Social environment
– service consumption e.g. alone, with an important person, with a group of friends,
or in a public place (consider gaming, watching video),
– popularity of contents.
• Economic context
– price for service consumption, tariff model: time, volume, flat
– costs
• System context
– load of system
– system offloading possible, e.g. wifi offloading
• Usage context
– Goal, task of service consumption, e.g. information retrieval vs. time killing
– background vs. foreground application
Examples
• Follow the moon:
temporal and
economic context
• Video streaming:
physical context
• Video flash crowds:
social context

Agenda
• Context monitoring and QoE monitoring
• Example use case: video flash crowds
• QoE model for HTTP adaptive streaming
• Numerical results
• Open issues: realization

HTTP Adaptive Streaming

Content Delivery with a CDN
Core
network
Access
network
Content
server
Clients
CDN
server

Edge Content Delivery Network
Global CDN Backbone
Access Provider
Access Provider
Transit
Provider
Point of
PresencePoint of
Presence
Point of
Presence
Point of
Presence
Edge Cache

Simulation Scenario: Video Flash Crowd
• Video player
– playout threshold of 6s
– video stalls for empty buffer
• Video contents
– Segment size of 2s
– Two quality layers
• Flash crowd arrivals
– 𝑁 = 30 users arrive
– Exponential distributed interarrival times with rate λ
– P(T<90s) = 99.27%, 𝑇~𝐸𝑟𝑙𝑎𝑛𝑔 𝑁, 𝜆
• HAS algorithm
• CDN load balancing
CDN 1
CDN 2
Flash
Crowd
ISP
bottleneck

HAS Algorithm and CDN Load Balancing
• CDN load balancing strategies
1. CDN directs the first 𝑲 users to CDN 1, subsequent
users are assigned to CDN 2. Second, the CDN.
2. Context monitoring based on information about the
flash crowd from a third party. Users are assigned to the
CDN with the lowest number of users.
• HTTP adaptation strategy
1. Actual buffer and throughput of last segment to
determine quality level of next segment
2. Additional context information on number of users and
capacity per CDN
3. Non-adaptive streaming algorithm: high quality level
Bit rate
Time
TCP throughput
Requested chunks

SIMPLE QOE MODEL FOR
HTTP ADAPTIVE STREAMING

What is the influence of stalling on Video QoE?
IQX-Hypothesis
Excellent
Good
Fair
Poor
Bad
5
4
3
2
1
Imperceptible
Perceptible
Slightly annoying
Annoying
Very annoying
• Small number of interruptions
strongly affect YouTube QoE
Provider (i.e. content and
network provider) must avoid
stalling
0 1 2 3 4 5 6
1
2
3
4
5
number of stallings
MOS
crowdsourcing
laboratory
QoE x = αe−βx + γ

Survey: Subjective Studies on HAS QoE
• Seufert, M.; Egger, S.; Slanina, M.; Zinner, T.; Hoßfeld, T.; Tran-Gia, P., "A Survey on
Quality of Experience of HTTP Adaptive Streaming," Communications Surveys & Tutorials,
IEEE , vol.17, no.1, pp.469,492, 2015
doi: 10.1109/COMST.2014.2360940
HTTP
Adaptive
Streaming
Video
Quality
Human
Computer
Interaction
Networking
etc.

Switching Frequency vs. Time on Layer
• In several works, switching frequency is reported to
influence QoE
• Often parameters „number/frequency of switches“ and „time
on layer“ are correlated and change simultaneously
• Keeping „time on layer“
constant  no influence of
switching frequency
could be found

Simple QoE Model for Two Quality Layers
• Simple QoE model based on two key influence factors
• IQX provides a very good fit to the data points (R²=0.98)
IQX-Hypothesis

Combined QoE Model
• Quality Adaptation Model
– Based on time t on high layer
– 𝑄1 𝑡 following IQX hypothesis
• Stalling Model
– Based on number 𝑥 of stalls
– 𝑄2 𝑥 following IQX hypothesis
• HTTP Adaptive Streaming Model
– 𝑄 𝑥, 𝑡 = 𝑄1 𝑡 ⋅ Q2(𝑥)
– Model still follows IQX hypothesis
IQX-Hypothesis

NUMERICAL RESULTS:
VIDEO FLASH CROWDS

Simulation Results
• No context information is used
– CDN load balancing strategy: K=13
– HAS quality
adaptation
mechanism.
• CDN1 can serve
13 / 35 users in
high / low quality
• CDN2 can serve
10 / 26 users in
high / low quality
• Reaction too slow

CDN Load Balancing Strategy
• Static assignment cannot achieve optimum
• Reactive approach
based on context
information improves
QoE for all users

Summary of Results: CDN and HAS
Bit rate
Time
TCP throughput
Requested chunks

Conclusions
• Context monitoring complements QoE monitoring
– Utilization of additional information
– Different types of context may be monitored
• Example of video flash crowds
– Performance and QoE gain significantly improves
– Technical realization needs to be developed
• Realization of context monitoring

Realization of Context Monitoring using Social Data
• Accessing data from third party:
Internet of Services
• Social data has to be monitored
– Scale (single user, selected users,
all users)
– Period (every hour, once a day, …)
– Source (Online Social Networks OSNs,
Services, Service Providers, ISPs,…)
online
social network
www.mas.wiwi.uni-due.de 24
http://www.smartenit.eu
Content
ProviderISPs
CDNs
$$$
$$$
$$$
$$$
Ads
Data
analysis
…

Reseach Qestions: Social Data Monitoring
• Example: How to access data from OSNs?
• Design questions
– Identification of relevant social data
– Access method
– Sampling strategy (scale, period, source,…)
– Incentives (if necessary)
Method Information Prediction
OSN collaboration All information Global, Detailed
End user grants
access to his data
Private information
about end user and
shared information
about friends
Local, Detailed
Crawling/Sampling Public information Global, Vague
www.mas.wiwi.uni-due.de 25

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