Hossfeld qc man2015_context_monitoring_web
-
Upload
tobias-hossfeld -
Category
Science
-
view
339 -
download
0
Transcript of 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
QoEmonitoring
e.g. devicecapabilities 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
mas.wiwi.uni-due.de 4
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
SIMULATION MODEL
HTTP Adaptive Streaming
mas.wiwi.uni-due.de 7
mas.wiwi.uni-due.de 9
Content Delivery with a CDN
Core network
Accessnetwork
Contentserver
ClientsCDNserver
Edge Content Delivery Network
mas.wiwi.uni-due.de 10
$$$
Global CDN Backbone$$$
Access Provider
Access Provider
TransitProvider
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– users arrive– Exponential distributed interarrival times with rate – P(T<90s) = 99.27%,
• HAS algorithm• CDN load balancing
mas.wiwi.uni-due.de 11
CDN 1
CDN 2
Flash Crowd
ISP
bottleneck
HAS Algorithm and CDN Load Balancing
• CDN load balancing strategies1. CDN directs the first 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 strategy1. Actual buffer and throughput of last segment to
determine quality level of next segment2. Additional context information on number of users and
capacity per CDN3. Non-adaptive streaming algorithm: high quality level
mas.wiwi.uni-due.de 12
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
ExcellentGoodFairPoorBad
54321
ImperceptiblePerceptible
Slightly annoyingAnnoying
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 61
2
3
4
5
number of stallings
MO
S
crowdsourcinglaboratory
QoE ( x )=α e−βx+γ
mas.wiwi.uni-due.de 14
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, 2015doi: 10.1109/COMST.2014.2360940
mas.wiwi.uni-due.de 15
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
mas.wiwi.uni-due.de 16
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)
mas.wiwi.uni-due.de 17
IQX-Hypothesis
Combined QoE Model
• Quality Adaptation Model– Based on time t on high layer– following IQX hypothesis
• Stalling Model– Based on number of stalls– following IQX hypothesis
• HTTP Adaptive Streaming Model
– Model still follows IQX hypothesis
mas.wiwi.uni-due.de 18
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 serve13 / 35 users in high / low quality
• CDN2 can serve10 / 26 users inhigh / low quality
• Reaction too slow
mas.wiwi.uni-due.de 20
CDN Load Balancing Strategy
• Static assignment cannot achieve optimum
• Reactive approachbased on contextinformation improvesQoE for all users
mas.wiwi.uni-due.de 21
Summary of Results: CDN and HAS
mas.wiwi.uni-due.de 22
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
mas.wiwi.uni-due.de 23
www.mas.wiwi.uni-due.de 24
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
http://www.smartenit.eu
Content ProviderISPs
CDNs
$$$
$$$ $$$
$$$
AdsData
analysis
…
www.mas.wiwi.uni-due.de 25
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 PredictionOSN collaboration All information Global, DetailedEnd user grants access to his data
Private information about end user and shared information about friends
Local, Detailed
Crawling/Sampling Public information Global, Vague
THANKS