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Kuan-Ta Chen
Institute of Information Science
Academia Sinica
Games on Demand:
Are We There Yet?
Academia Sinica
31 research institutes in 3 major divisions
1) mathematics, physics, and applied sciences;
2) life science...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 3
Institute of Information Science
Members
40 Principal Investigators
40 post-d...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 4
Multimedia Networking and Systems Lab
Research Areas
Multimedia Systems
Quali...
Area 1: Multimedia Systems
5
Area 2: Quality of Experience
Using physiological measurements to predict the
market performance of online games
6
[1] Jin...
Area 3: Computation Social Science
“The emerging intersection of the social and computational
sciences, an intersection th...
Area 3: CSS (cont.)
Area 3: CSS (cont.)
Help people reduce weight by providing visual
incentives
lost 5 kg lost 4 kg
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 10
GAMES ON DEMAND
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 11
Tough Life of Gamers
Games are becoming way too complex
The overhead of sett...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 12
On-demand services
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 13
Games on Demand: Approaches
Painless game
installation
e.g., on Xbox 360
Clo...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 14
Cloud Gaming: File Streaming
Instant game play supported by a minimal, playa...
Cloud Gaming: Video Streaming
Video-based remote desktop specialized for
Games running in cloud
High-definition real-time ...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 16
The Selling Points
Gamers’ perspectives
Frees gamers from indefinitely upgra...
Cloud gaming is expected to lead the future
growth of computer games: 9 times in 6 years
Cloud Gaming is Hot
[CGR] http://...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 18
Challenges
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 20
Challenge #1
Unavoidable extra delays
Video encoding at the server
Video dec...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 21
Challenge #1 (cont.)
OnLive dictates a server rendering/processing latency o...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 22
Challenge #2
For a regular x264 zerolatency implementation,
3--5 Mbps is req...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 23
Challenge #3
Investing thousands of cloud servers was partly the
reason for ...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 24
Challenge #3 (cont.)
The state of the practice
OnLive Sony NVIDIA ODM
Specif...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 25
Outline
An Open-Source Cloud Gaming Testbed
Quantifying the Susceptibility o...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 26
An Open-Source Implementation
Researchers have tons of ideas to improve clou...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 29
System Architecture
The client and the server, with many
components
Implemen...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 30
Process Video Frames in Parallel
Suppose the targeted inter-frame delay is ∆...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 31
Video Playout Buffering
The 1-frame buffering strategy
Based on the RTP mark...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 32
GA Has Lower Response Delay
Low response delay * network delay has been excl...
GA Provides (Relatively) Better
Video Quality
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 34
http://gaminganywhere.org/
56k+ visitors, 100k+ downloads since April 2013
Visitor Distribution
Geo-distribution
/ Day
July
2015
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 36
Outline
An Open-Source Cloud Gaming Testbed
Quantifying the Susceptibility o...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 37
The Question
Are games equally
susceptible to
latency?
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 38
Definition
Real-time strictness (RS)
The degree a game’s QoE degrades when t...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 39
Selected Games
ACT
LEGO Batman (Batman)
Devil May Cry (DMC)
Sangoku Musou 5 ...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 40
Facial EMG approach
1. Continuous emotion measures (can be at a rate of 1000...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 41
Facial EMG Measurement Setup
The corrugator
supercilii muscle
Negative emoti...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 42
Measurement devices
PowerLab 16/30
Electrodes
Wires
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 43
During game play…
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 45
Trace Summary
Subjects
Trace
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 46
Overall EMG potentials
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 47
EMG Potentials for each game
1. Diverse baseline EMG potentials for each gam...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 48
Deriving real-time strictness (RS)
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 49
RS of the studied games
In general, FPS > RPG > ACT in terms of RS
Game pace...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 50
Our conjecture
How a game responds to players’ commands is
associated with i...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 51
Illustrations for “light” commands
https://www.youtube.com/watch?v=ycYDDBKrv...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 52
Illustrations for “heavy” commands
https://www.youtube.com/watch?v=GGm1YNJNW...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 53
RS prediction
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 54
Application #1: Balance games’ QoE
degradation due to latency
Scenario
N use...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 55
Application #2: Co-optimizing data center
cost and gaming experience
Scenari...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 56
Outline
An Open-Source Cloud Gaming Testbed
Quantifying the Susceptibility o...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 57
Mobile games are !
in 2011, 59% smartphone users played mobile games [1]
by ...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 59
Testbed for User Studies
Nintendo 64 Limbo
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 60
Demo
Online
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 61
Questions
Is mobile cloud gaming energy efficient?
How to tune video paramet...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 62
Cloud gaming is energy efficient
Independent of game genres Energy saving
(5...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 63
Energy consumptions
Impact of tunable parameters
Frame rate > Bit rate > Res...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 64
Comparison on Gaming Experience
PCs have many
physical keys
Implementations ...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 65
Why Mobile Performs Better in Graphics?
First, subjects may have lower expec...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 66
Outline
An Open-Source Cloud Gaming Testbed
Quantifying the Susceptibility o...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 67
The need for auto reconfiguration
The provided QoE is normally poor when our...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 68
Our Goal
Assuming N users playing different games
A mechanism to select the ...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 69
Crowdsourced user study
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 70
QoE vs. QoS factors
Our intuitions
Bitrate , frame rate  graphics quality
F...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 71
Game Genre Matters
Action
Game
Car Racing
Game
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 72
Many cloud gaming users share
a bottleneck link to a data center
Maximize av...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 73
The proposed system
A passive bandwidth estimator for 802.11 network
A quadr...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 74
Achieved Performance
(Efficiency = MOS score / bandwidth consumed)
(Running ...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 75
Outline
An Open-Source Cloud Gaming Testbed
Quantifying the Susceptibility o...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 76
The Research Problem
Assuming each VM handles one game session
Consolidating...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 77
Notations
• Frame per Second:
• Processing Delay:
• Network Latency:
• CPU U...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 78
Problem Formulation:
Provider Version
Objective Function: Maximize Profits
C...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 79
Quality-Driven Heuristic (QDH):
Provider Version
Intuition: put as many VMs ...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 80
QDH’: Gamer Version
 A similar formulation but here we minimize QoE
degrada...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 81
Our Testbed
Physical Servers
CPU: i5
GPU: NVIDIA Quadro 6000
Memory: 16GB
Br...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 82
Baseline Algorithm
Location Based Placement (LBP) algorithm
places each VM o...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 83
QDH Increases Profits
 Save money (by shutting down more servers and
reloca...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 84
QDH’ Improves QoE
Outperforms LBP algo. by providing much higher QoE
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 85
Both Algorithms Run in Real Time
Both algorithms terminate in < 2.5 sec on a...
Outline
An Open-Source Cloud Gaming Testbed
Quantifying the Susceptibility of Games to Latency
Quantifying User Satisfacti...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 88
Technical Reasons
Technical Reason #1
Explore possible next states
Render possible frames and send to user
User chooses one based on input
M...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 90
Technical Reason #2
Objects that are far away or near
peripheral vision can ...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 91
Technical Reason #3
GPU virtualization is getting more mature
NVIDIA and AMD...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 92
Marketing Reasons
As a complement, rather than a replacement solution
E.g., ...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 93
Marketing Reasons (cont.)
B2B2C business model
e.g., G-cluster Global provid...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 94
Game Integration
Video Codec
Virtualization
User Interface
QoE Measurement a...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 95
Conclusion
Cloud gaming shares similar fundamental
problems with many intere...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 96
My special thanks to…
GamingAnywhere team
Dr. Chun-Ying Huang Dr. Cheng-Hsin...
Games on Demand / Sheng-Wei “Kuan-Ta” Chen 97
Kuan-Ta Chen
Academia Sinica
cloud gaming
rocks!
Thank You!
http://www.iis.s...
Games on Demand: Are We There Yet?
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Games on Demand: Are We There Yet?

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Games on demand, a.k.a., cloud gaming, refers to a new way to deliver computer games to users, where computationally complex games are executed and rendered on powerful cloud servers rather than local computing devices. In this talk, I will give an overview of the challenges in developing cloud gaming systems, what we have done, and what remains to do. I will start from GamingAnywhere, an open-source cloud gaming system, followed by a number of studies based on the system. Finally I will conclude the talk with open issues in providing highly real-time and high-definition audio/visual quality multimedia experience (e.g., in the form of gaming and virtual reality).

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Games on Demand: Are We There Yet?

  1. 1. Kuan-Ta Chen Institute of Information Science Academia Sinica Games on Demand: Are We There Yet?
  2. 2. Academia Sinica 31 research institutes in 3 major divisions 1) mathematics, physics, and applied sciences; 2) life sciences; 3) humanities and social sciences. 1000 tenure-tracked researchers 5,000 research associates and technicians
  3. 3. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 3 Institute of Information Science Members 40 Principal Investigators 40 post-doctoral researchers 300 technicians and RAs Research Areas •Bioinformatics •Network System and Service •Data Management and Information Discovery •MultimediaTechnologies •Natural Language and Knowledge Processing •Computer System •Programming Languages and Formal Methods •ComputationTheory and Algorithms Multimedia Networking and Systems Lab
  4. 4. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 4 Multimedia Networking and Systems Lab Research Areas Multimedia Systems Quality of Experience Management Computational Social Science http://mmnet.iis.sinica.edu.tw
  5. 5. Area 1: Multimedia Systems 5
  6. 6. Area 2: Quality of Experience Using physiological measurements to predict the market performance of online games 6 [1] Jing-Kai Lou, Kuan-Ta Chen, Hwai-Jung Hsu, and Chin-Laung Lei, Forecasting Online Game Addictiveness, IEEE/ACM NetGames 2012.
  7. 7. Area 3: Computation Social Science “The emerging intersection of the social and computational sciences, an intersection that includes analysis of web-scale observational data, virtual lab–style experiments, and computational modeling” [1]. [1] Duncan J.Watts, Computational Social Science Exciting Progress and Future Directions, Frontiers of Engineering, Winter 2013.
  8. 8. Area 3: CSS (cont.)
  9. 9. Area 3: CSS (cont.) Help people reduce weight by providing visual incentives lost 5 kg lost 4 kg
  10. 10. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 10 GAMES ON DEMAND
  11. 11. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 11 Tough Life of Gamers Games are becoming way too complex The overhead of setting up a game is significant Often locked on a specific computer Games may not be incompatible with some software/hardware Computer hardware constantly demands upgrading
  12. 12. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 12 On-demand services
  13. 13. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 13 Games on Demand: Approaches Painless game installation e.g., on Xbox 360 Cloud gaming Cloud-supported instant game play
  14. 14. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 14 Cloud Gaming: File Streaming Instant game play supported by a minimal, playable code base (~ 5%) Progressive downloading of game code and data during game play 3D mesh streaming can be seen a special instance (Figure courtesy of WeiTsang Ooi from “ScalableView-Dependent Progressive Mesh Streaming”)
  15. 15. Cloud Gaming: Video Streaming Video-based remote desktop specialized for Games running in cloud High-definition real-time game play Game servers Internet Streaming Streaming Streaming PC Laptop Mobile
  16. 16. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 16 The Selling Points Gamers’ perspectives Frees gamers from indefinitely upgrading their computers Enables gamers to play games anywhere, anytime Game manufacturers’ perspectives Allows developers to support more platforms Reduces the production cost Prevents pirating
  17. 17. Cloud gaming is expected to lead the future growth of computer games: 9 times in 6 years Cloud Gaming is Hot [CGR] http://www.cgconfusa.com/report/documents/Content-5minCloudGamingReportHighlights.pdf
  18. 18. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 18 Challenges
  19. 19. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 20 Challenge #1 Unavoidable extra delays Video encoding at the server Video decoding and playout buffering at the client Less opportunities for delay compensation Game states (e.g., game objects’ positions and velocity) sare not available at the client side A Comparison with “Traditional” Online Games
  20. 20. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 21 Challenge #1 (cont.) OnLive dictates a server rendering/processing latency of nearly 100 ms, and partially copes with it by setting up 7 data centers merely in North America Only people who live in 1000 mile radius from a data center are encouraged to play Similarly, Sony/Gaikai has 8 data centers in NA (Figure courtesy of Mark Claypool from “Latency and Player Actions in Online Games)
  21. 21. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 22 Challenge #2 For a regular x264 zerolatency implementation, 3--5 Mbps is required for a quality 720p cloud gaming session (on desktop / TV) Playout buffering is commonly used to absorb packet delivery disorders (loss, re-orders)  not applied here as short latency is critical
  22. 22. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 23 Challenge #3 Investing thousands of cloud servers was partly the reason for OnLive’s bankruptcy in 2012. GPU virtualization is getting more mature, but the degree of multiplexity is still around 10—20 i.e., to support 10000 current users, 500—1000 servers are required
  23. 23. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 24 Challenge #3 (cont.) The state of the practice OnLive Sony NVIDIA ODM Specification 2 MB in 2U 4 PS4 MB in 1U 2U with 6 Graphic cards 2 MB in 1U # GPU 2 4 12 8 GPU/U 1 4 6 8
  24. 24. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 25 Outline An Open-Source Cloud Gaming Testbed Quantifying the Susceptibility of Games to Latency Quantifying User Satisfaction in Mobile Cloud Games QoE-aware Auto-Reconfiguration Placing Virtual Machines to Optimize Cloud Games Future Perspectives
  25. 25. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 26 An Open-Source Implementation Researchers have tons of ideas to improve cloud gaming services, but all existing cloud gaming systems are proprietary and closed
  26. 26. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 29 System Architecture The client and the server, with many components Implement by leveraging open-source packages
  27. 27. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 30 Process Video Frames in Parallel Suppose the targeted inter-frame delay is ∆t The response delay may greater than ∆t frame capture + color space conversion + encoding It could degrade encoding bitrate Process in parallel
  28. 28. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 31 Video Playout Buffering The 1-frame buffering strategy Based on the RTP marker bit An H.264 frame can be split into different numbers of packets The marker bit (with a value of 1) indicates the last packet of a frame
  29. 29. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 32 GA Has Lower Response Delay Low response delay * network delay has been excluded for FAIR comparisons
  30. 30. GA Provides (Relatively) Better Video Quality
  31. 31. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 34 http://gaminganywhere.org/ 56k+ visitors, 100k+ downloads since April 2013
  32. 32. Visitor Distribution Geo-distribution / Day July 2015
  33. 33. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 36 Outline An Open-Source Cloud Gaming Testbed Quantifying the Susceptibility of Games to Latency Quantifying User Satisfaction in Mobile Cloud Games QoE-aware Auto-Reconfiguration Placing Virtual Machines to Optimize Cloud Games Future Perspectives
  34. 34. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 37 The Question Are games equally susceptible to latency?
  35. 35. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 38 Definition Real-time strictness (RS) The degree a game’s QoE degrades when the latency is higher Cloud-gaming friendliness A cloud game’s susceptibility to latency in terms of its QoE
  36. 36. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 39 Selected Games ACT LEGO Batman (Batman) Devil May Cry (DMC) Sangoku Musou 5 (Dynasty Warriors 6) (SM5) FPS Call of Duty: World at War (COD) F.E.A.R 2 (FEAR) Unreal Tournament 3 (Unreal) RPG Ys Origin (Ys) Loki: Heroes of Mythology (Loki) Torchlight (Torch)
  37. 37. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 40 Facial EMG approach 1. Continuous emotion measures (can be at a rate of 1000 Hz or even higher) 2. Does not disturb game play 3. Objective since the emotional indicators are directly measured rather than told by subjects (EMG: Electromyography)
  38. 38. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 41 Facial EMG Measurement Setup The corrugator supercilii muscle Negative emotions The amount of annoyance caused by latency
  39. 39. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 42 Measurement devices PowerLab 16/30 Electrodes Wires
  40. 40. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 43 During game play…
  41. 41. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 45 Trace Summary Subjects Trace
  42. 42. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 46 Overall EMG potentials
  43. 43. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 47 EMG Potentials for each game 1. Diverse baseline EMG potentials for each game 2. The increasing rates of EMG potential are game-dependent as well
  44. 44. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 48 Deriving real-time strictness (RS)
  45. 45. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 49 RS of the studied games In general, FPS > RPG > ACT in terms of RS Game pace↑, RS↑, latency-critical↑
  46. 46. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 50 Our conjecture How a game responds to players’ commands is associated with its real-time strictness If its commands are “lightweight” Simple, fast, local moves Timing is important  higher RS If its commands are “heavy” Associated with long and large amounts of animations Timing is not critical  lower RS
  47. 47. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 51 Illustrations for “light” commands https://www.youtube.com/watch?v=ycYDDBKrv4I
  48. 48. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 52 Illustrations for “heavy” commands https://www.youtube.com/watch?v=GGm1YNJNWbo
  49. 49. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 53 RS prediction
  50. 50. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 54 Application #1: Balance games’ QoE degradation due to latency Scenario N users are playing different games at the same time Users experience different latencies and games have different RS  Each player experiences different levels of QoE degradation Usage Use the model to infer which players are having a worse gaming experience than others Prioritize the server’s resources, such as CPU and GPU, to reduce those players’ latencies and thereby mitigate QoE degradation they would otherwise experience
  51. 51. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 55 Application #2: Co-optimizing data center cost and gaming experience Scenario N data centers, each has distinct operation cost (electricity and labor) Whenever a user signs in, we need to assign a data center to him for real-time game play Question: Which data center should we assign to the player? Usage Use the model to predict users’ QoE in all the cases and choose the data center which provide a “just good enough” gaming experience Data center A: Lower cost, longer delay Data center B: Higher cost, shorter delay
  52. 52. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 56 Outline An Open-Source Cloud Gaming Testbed Quantifying the Susceptibility of Games to Latency Quantifying User Satisfaction in Mobile Cloud Games QoE-aware Auto-Reconfiguration Placing Virtual Machines to Optimize Cloud Games Future Perspectives
  53. 53. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 57 Mobile games are ! in 2011, 59% smartphone users played mobile games [1] by 2016, mobile game market will grow to 16 billion USD [2] Mobile games are less visually appealing, because of the limitations on CPU/GPU power memory space/speed battery capacity Possible solution: mobile cloud gaming Mobile Games [1] http://www.infosolutionsgroup.com/popcapmobile2012.pdf [2] https://www.abiresearch.com/research/product/1006313-mobile-gaming
  54. 54. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 59 Testbed for User Studies Nintendo 64 Limbo
  55. 55. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 60 Demo Online
  56. 56. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 61 Questions Is mobile cloud gaming energy efficient? How to tune video parameters in an energy- conserving way? What components are energy-hungry? Mobile gaming experience comparable to PC?
  57. 57. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 62 Cloud gaming is energy efficient Independent of game genres Energy saving (50% in CPU and 30% in energy)
  58. 58. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 63 Energy consumptions Impact of tunable parameters Frame rate > Bit rate > Resolution 3G consumes 30%--45% more energy than WiFi Input event processing incurs non-trivial energy consumption
  59. 59. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 64 Comparison on Gaming Experience PCs have many physical keys Implementations are efficient Really? Mobile is better?
  60. 60. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 65 Why Mobile Performs Better in Graphics? First, subjects may have lower expectation on graphics of mobile devices Second, smaller screen sizes make graphics imperfection less noticeable Observation: The satisfaction levels are based on observed flaws than absolute quality!
  61. 61. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 66 Outline An Open-Source Cloud Gaming Testbed Quantifying the Susceptibility of Games to Latency Quantifying User Satisfaction in Mobile Cloud Games QoE-aware Auto-Reconfiguration Placing Virtual Machines to Optimize Cloud Games Future Perspectives
  62. 62. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 67 The need for auto reconfiguration The provided QoE is normally poor when our video packets experience loss events  We will have to voluntarily reduce bandwidth usage when network is (temporarily) overloaded Due to network dynamics, the provisioning of network bandwidth may vary in sub-seconds  An automatic reconfiguration mechanism is required that can respond to changes in run time
  63. 63. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 68 Our Goal Assuming N users playing different games A mechanism to select the best (bitrate, frame rate) configuration for each user given the current game he/she is playing Two explicit objectives Maximize the average gaming experience (i.e., utilitarian) Maximize the worst gaming experience (i.e., fairness)
  64. 64. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 69 Crowdsourced user study
  65. 65. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 70 QoE vs. QoS factors Our intuitions Bitrate , frame rate  graphics quality Frame rate  interactivity
  66. 66. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 71 Game Genre Matters Action Game Car Racing Game
  67. 67. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 72 Many cloud gaming users share a bottleneck link to a data center Maximize average MOS by choosing bitrate and frame rate for each user Problem Formulation
  68. 68. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 73 The proposed system A passive bandwidth estimator for 802.11 network A quadratic QoE model for each game An approximate algorithm for solving the optimization problem efficiently
  69. 69. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 74 Achieved Performance (Efficiency = MOS score / bandwidth consumed) (Running time in seconds)
  70. 70. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 75 Outline An Open-Source Cloud Gaming Testbed Quantifying the Susceptibility of Games to Latency Quantifying User Satisfaction in Mobile Cloud Games QoE-aware Auto-Reconfiguration Placing Virtual Machines to Optimize Cloud Games Future Perspectives
  71. 71. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 76 The Research Problem Assuming each VM handles one game session Consolidating VMs in different ways results in different profits and gaming quality For example, different data centers have different prices and offer different quality of service Hence, we propose VM placement policies to maximize the profits or gamer QoE
  72. 72. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 77 Notations • Frame per Second: • Processing Delay: • Network Latency: • CPU Utilization: • GPU Utilization: • Hourly fee: • Operational Cost: • Memory of Server: • Uplink of Datacenter:
  73. 73. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 78 Problem Formulation: Provider Version Objective Function: Maximize Profits Constraint: QoE Degradation Frame Per Second Delay  Decision variable: ……
  74. 74. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 79 Quality-Driven Heuristic (QDH): Provider Version Intuition: put as many VMs on a server as possible Condition: Do not exceed the user-specified maximal tolerable QoE degradation
  75. 75. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 80 QDH’: Gamer Version  A similar formulation but here we minimize QoE degradation as possible  Objective function:
  76. 76. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 81 Our Testbed Physical Servers CPU: i5 GPU: NVIDIA Quadro 6000 Memory: 16GB Broker CPU: i7 3.2 GHz Memory: 16GB Clients CPU: i5 Memory: 4 GB
  77. 77. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 82 Baseline Algorithm Location Based Placement (LBP) algorithm places each VM on a random game server that is not fully loaded and the data center geographically closest to the gamer
  78. 78. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 83 QDH Increases Profits  Save money (by shutting down more servers and relocating servers to a less expensive data center)  Always satisfy the specified QoE requirement
  79. 79. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 84 QDH’ Improves QoE Outperforms LBP algo. by providing much higher QoE
  80. 80. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 85 Both Algorithms Run in Real Time Both algorithms terminate in < 2.5 sec on a commodity PC even for large services with 20,000 servers and 40,000 gamers
  81. 81. Outline An Open-Source Cloud Gaming Testbed Quantifying the Susceptibility of Games to Latency Quantifying User Satisfaction in Mobile Cloud Games QoE-aware Auto-Reconfiguration Placing Virtual Machines to Optimize Cloud Games Are We There Yet?
  82. 82. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 88 Technical Reasons
  83. 83. Technical Reason #1 Explore possible next states Render possible frames and send to user User chooses one based on input Manage to hide latency up to 384 ms at the cost of 4.5x higher bandwidth (and extra computation/rendering cost) [1] Chu, K. L. D., Cuervo, E., Kopf, J., Grizan, S.,Wolman, A., & Flinn, J. Outatime: Using Speculation to Enable Low-Latency Continuous Interaction for Cloud Gaming,ACM MobiSys 2015. Pre-render future frames seems possible
  84. 84. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 90 Technical Reason #2 Objects that are far away or near peripheral vision can be coded with fewer bits Leads to ~50% bit rate reduction with 4.75% MOS reduction [1] Ahmadi, H., Khoshnood, S., Hashemi, M. R., & Shirmohammadi, S., Efficient bitrate reduction using a Game Attention Model in cloud gaming. In IEEE HAVE 2013. Game info (e.g., camera and object positions) can be used to better encode
  85. 85. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 91 Technical Reason #3 GPU virtualization is getting more mature NVIDIA and AMD design specialized GPUs and drivers for cloud gaming Cloud-gaming-friendly game engines would further boost the scalability (by planned GPU & VRAM sharing, etc) Degree of multiplexity keeps increasing
  86. 86. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 92 Marketing Reasons As a complement, rather than a replacement solution E.g., Playstation Now uses cloud gaming to provide backward compatibility and cross-platform support As a playable ad Startups such as mNectar, Agawi, Voxel, provide playable ad services (mainly for mobile apps)
  87. 87. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 93 Marketing Reasons (cont.) B2B2C business model e.g., G-cluster Global provide turnkey solutions to telecom operators around the world to solution providers: almost risk-free and more scalable to local service providers: low-cost investment as they can use existing infrastructures Seems a sustainable model which is key to success
  88. 88. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 94 Game Integration Video Codec Virtualization User Interface QoE Measurement and Modeling Server Selection Parameter Adaptation Resource Scheduling [1] Kuan-Ta Chen, Chung-Ying Huang, and Cheng-Hsin Hsu, "Cloud Gaming Onward: Research Opportunities and Outlook," Proceedings of IEEE C-Game 2014, July 2014.
  89. 89. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 95 Conclusion Cloud gaming shares similar fundamental problems with many interesting applications Screencasting Mobile smart lens Tele medicine Immersive remote communications Thus, cloud gaming seems a rewarding entrance to fundamental multimedia system challenges!
  90. 90. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 96 My special thanks to… GamingAnywhere team Dr. Chun-Ying Huang Dr. Cheng-Hsin Hsu Chih-Fang Hsu Hua-Jun Hong Ching-Ling Fang Tsung-HanTsai
  91. 91. Games on Demand / Sheng-Wei “Kuan-Ta” Chen 97 Kuan-Ta Chen Academia Sinica cloud gaming rocks! Thank You! http://www.iis.sinica.edu.tw/~swc

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