QoE Driven Server Selection for VoD in the Cloud Chen Wang 1,2 , Hyong Kim 1 , Ricardo Morla 2 1 Department of ECE, Carnegie Mellon University 2 Faculdade de Engenharia de Universidade do Porto IEEE CLOUD 2015, New York, USA 1
Challenges • Cloud for large-scale VoD: Elasticity, Scalability, Flexibility • Performance impact due to VM interference – The performance of video server in a VM varies – The user experience on the video server varies 64-bit OS 64-bit OS 64-bit OS Virtualization Layer Hardware （ CPU, disk, memory, network ） 2
Problem Statement Which video server in the Cloud can provide the best Quality-of-experience (QoE) for a user request? 3
Our Objectives • Select a server providing the Best QoE – What is the criteria to select server • Existing System : the lowest network latency/server load • The Best Server Performance Metric ≠ The best user QoE – When to select server • Existing system : before the start of streaming • The QoE at the start of streaming ≠ The QoE in 10 min – Who selects server • Existing system : local DNS server • Client himself knows better. • Neighboring clients might know better. • Scalability – millions of users, thousands of servers. 4
Our Proposed System • Best QoE – What: QoE gives the best perception of server performance • QoE based Server Monitoring & Server Selection – When: before the downloading of each video chunk • Adaptive Server Selection per chunk – Who: clients and their neighbors. • Cooperation among nearby clients on QoE based server monitoring • Scalability --- Agent based System – Agents perform distributed control . – Serve user requests locally . 5
System Design Cache Agent • Discover K candidate servers • K servers to client S 3 S 2 Production Cloud Environment C 6 S 1 S 4 S 5 C 5 C 1 C 2 Cooperation C 4 Client Agent C 3 • Monitor client’s QoE on Candidate Servers • Adaptive server selection 6 • Cooperative clients share QoE of Servers
System Operation ★ Videos ★ S 3 S 2 1. Location aware overlay of cache S 4 agents 2.Multi-Candidate Content Discovery, CST S 1 3.Connect to the local cache agent. ★ 4.K candidate servers for a video request. CST(S 5 ) Srv 1 Srv 2 S 5 S 3 S 5 5.QoE driven Adaptive server Selection S 3 S 4 ★ 6.Cooperation among client agents. S 5 S 2 7
Multi-Candidate Content Discovery (MCCD) ★ Videos ★ CST(S 3 ) Cand 1 Cand 2 S 3 S 3 S 2 S 2 S 3 ★ S 4 CST(S 2 ) S 5 Cand 1 Cand 2 S 2 S 1 S 2 S 3 S 3 ★ CST(S 5 ) ★ ★ Cand 1 Cand 2 S 2 S 5 S 5 S 5 S 5 S 3 S 3 ★ S 5 S 2 8
QoE Model • Streaming Scheme: DASH • Factors impacting QoE per video Chunk r – Bitrate of chunk: t – Freezing time: • Existing QoE Model Q video_quality ( r ) = a 1 ln a 2 r – Logarithm Law : r max – Logistic Model : a 1 , a 2 , c 1 c 3 ( ) Q t freezing c 1 5 t 0 are positive fitted coefficients. c 3 c 2 1 t 9 5 t 0
Our Chunk based QoE Model • Chunk based QoE Model Freezing Decreasing Bitrate 10
Q oE driven Server S election • What: Criterion of Server Selection Candidate Server 1 Candidate Server 2 Low Latency Server High Interference Load Network Others Latency QoE Can 1 Can 2 Latest QoE 11
Adaptive Server S election • When: Adaptive Server Selection per Chunk – Dynamic Interference Low Latency Candidate Server 1 Candidate Server 2 Can 1 Can 2 Chunk 1 Dynamic Interference Chunk 2 Chunk 3 12
Cooperative Server S election • Who: Neighbors know better Low Latency Candidate Server 1 Candidate Server 2 Can 1 Can 2 Can 1 Can 2 Latest Latest QoE QoE 13
Comparison Methods • Client streaming from 2 candidates – DASH : Streaming from the closest server • DNS based Server Selection + DASH streaming – QAS-DASH : QoE + Adaptive + DASH – CQAS-DASH : QoE + Adaptive + Cooperative + DASH 14
Google Cloud Experiment Cache Agent Client Agent Client Agent attached to Cache Agent Location Aware Cache Agent Overlay 15
Google Cloud Experiment 80% < 3.4 QoE DASH 10% 3.5 QAS- <1% 3.62 DASH CQAS- 0% 3.68 DASH Session QoE: The average chunk QoE in a video session. 16
Simulation • Simulation in Simgrid Video Servers Clients 17
Simulation Results 90% < 3 QoE DASH > 40% 2.9216 QAS- 0% 3.1822 DASH CQAS- 0% 3.5004 DASH CQAS Improves >20% 90% QoE 18
Conclusion • QoE + Adaptability – QoE : a good indicator of server performance. – Adaptability: improve user experience in Cloud environment – Closest DASH QAS-DASH: • Google Cloud: ~3.5 >3.6 (~80 th percentile session QoE) • Simulation: 2.9216 3.1822 (8.92% in 90 th percentile session QoE) • Cooperation – Cooperation effectively help server selection in clients – CQAS-DASH (QoE + Adaptability + Cooperation) • Google Cloud: Doubled bitrate for 80% video sessions • Simulation: >20% in 90 th percentile session QoE 19
Netflix Titles • http://netflixcanadavsusa.blogspot.mx/ – Netflix Canada: 4499 movies/shows – Netflix USA: 8791 movies/shows 20
Capacity Limit • Google Cloud – We throttle the maximum bandwidth of each server to 4 Mbps to emulate the server overloading that would happen in real systems. • Simulation – Link to server: 50Mbps – Backbone link: 250Mbps 21
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