Measuring The Latency of Cloud Gaming Systems

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Cloud gaming, i.e., real-time game playing via thin clients, relieves players from the need to constantly upgrade their computers and deal with compatibility issues when playing games. As a result, cloud gaming is generating a great deal of interest among entrepreneurs and the public. However, given the large design space, it is not yet known which platforms deliver the best quality of service and which design elements constitute a good cloud gaming system. This study is motivated by the question: How good is the real-timeliness of current cloud gaming systems? To address the question, we analyze the response latency of two cloud gaming platforms, namely, OnLive and StreamMyGame. Our results show that the streaming latency of OnLive is reasonable for real-time cloud gaming, while that of StreamMyGame is almost twice the former when the StreamMyGame server is provisioned using an Intel Core i7-920 PC. We believe that our measurement approach can be generally applied to PC-based cloud gaming platforms, and that it will further the understanding of such systems and lead to improvements.

Tecnologia

Kuan-Ta Chen1, Yu-Chun Chang12, Po-Han Tseng1, Chun-Ying Huang3, and Chin-Laung Lei2
1Academia Sinica 2National Taiwan University 3National Taiwan Ocean University
Measuring The Latency of Cloud Gaming Systems
Conclusion
 We proposed a general methodology to measure the latency
components of cloud gaming systems
 OnLive provides shorter latency for real-time gaming than
SteamMyGame
 Future work
 Improve the proposed methodology
 Apply the methodology to more platforms and derive general
guidelines for designing quality cloud gaming systems
Cloud Gaming
Motivation
 Game software becomes more complex
 The overhead of setting up a game is significant
 Games have software and hardware compatibility with computers
 Real-time game playing via thin clients
 Advantages of cloud gaming
 No set up overheads or compatibility issues
 People can play games anywhere, anytime
 Cloud gaming services
 OnLive, StreamMyGame, GAIKAI, G-cluster Global, OTOY, T5-Labs
 Which platform provides the best quality of service?
Measurement Methodology
 Response delay : In each experiment, send ESC key in the game and see
when the corresponding MENU frame will be displayed
 Response delay (RD) =
Network delay (ND) + Processing delay (PD) + Playout delay (OD)
Scatter Plot of tblock_succeed and tblock_failed
Measurement Results
How to determine the occurrence of t3
 Blocking is successful : add tblock to the set tblock_succeeded
 Blocking is failed : add tmenu to the set tblock_failed
 We then estimate t3 as the point that yield the minimum sum of the
two density functions formed by tblock_succeeded and tblock_failed
 Blocking : Block the incoming data on the client
Blocking is successful
Blocking is failed
Experiment Setup
• Game:
Lego Batman: The VideoGame (Batman)
Warhammer 40,000: Dawn of War (DOW)
F.E.A.R. 2: Project Origin (FEAR)
• Platform:
OnLive (OnLive)
StreamMyGame (SMG)
Network delay (ND) = network RTT
Processing delay (PD) = t3 – t0 – ND
Playout delay (OD) = t4 – t3
tblock tblock+1 Timetmenu
Menu screen appears
tblock tblock+1 Timetmenu
Internet
LAN
LAN
Switch
Client (Intel Core i7-920)
Server (Intel Core i7-920)
OnLive Server
t0 t1 t2 t4
(controllable)
t3
(probable) (observable)
Processing delay Playout delay
Probing range
Time
Academia Sinica
ServerClient
Game Servers
PC
Laptop
Mobile
Streaming
Streaming
Streaming
Internet
t0 (Key event sent) t1 (Key event received)
t2 (Frame sent)t3 (Frame received)
Menu frame
Menu screen shown
t4 (Frame displayed)

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Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

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Measuring The Latency of Cloud Gaming Systems

1. Kuan-Ta Chen1, Yu-Chun Chang12, Po-Han Tseng1, Chun-Ying Huang3, and Chin-Laung Lei2 1Academia Sinica 2National Taiwan University 3National Taiwan Ocean University Measuring The Latency of Cloud Gaming Systems Conclusion  We proposed a general methodology to measure the latency components of cloud gaming systems  OnLive provides shorter latency for real-time gaming than SteamMyGame  Future work  Improve the proposed methodology  Apply the methodology to more platforms and derive general guidelines for designing quality cloud gaming systems Cloud Gaming Motivation  Game software becomes more complex  The overhead of setting up a game is significant  Games have software and hardware compatibility with computers  Real-time game playing via thin clients  Advantages of cloud gaming  No set up overheads or compatibility issues  People can play games anywhere, anytime  Cloud gaming services  OnLive, StreamMyGame, GAIKAI, G-cluster Global, OTOY, T5-Labs  Which platform provides the best quality of service? Measurement Methodology  Response delay : In each experiment, send ESC key in the game and see when the corresponding MENU frame will be displayed  Response delay (RD) = Network delay (ND) + Processing delay (PD) + Playout delay (OD) Scatter Plot of tblock_succeed and tblock_failed Measurement Results How to determine the occurrence of t3  Blocking is successful : add tblock to the set tblock_succeeded  Blocking is failed : add tmenu to the set tblock_failed  We then estimate t3 as the point that yield the minimum sum of the two density functions formed by tblock_succeeded and tblock_failed  Blocking : Block the incoming data on the client Blocking is successful Blocking is failed Experiment Setup • Game: Lego Batman: The VideoGame (Batman) Warhammer 40,000: Dawn of War (DOW) F.E.A.R. 2: Project Origin (FEAR) • Platform: OnLive (OnLive) StreamMyGame (SMG) Network delay (ND) = network RTT Processing delay (PD) = t3 – t0 – ND Playout delay (OD) = t4 – t3 tblock tblock+1 Timetmenu Menu screen appears tblock tblock+1 Timetmenu Internet LAN LAN Switch Client (Intel Core i7-920) Server (Intel Core i7-920) OnLive Server t0 t1 t2 t4 (controllable) t3 (probable) (observable) Processing delay Playout delay Probing range Time Academia Sinica ServerClient Game Servers PC Laptop Mobile Streaming Streaming Streaming Internet t0 (Key event sent) t1 (Key event received) t2 (Frame sent)t3 (Frame received) Menu frame Menu screen shown t4 (Frame displayed)

Measuring The Latency of Cloud Gaming Systems

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Measuring The Latency of Cloud Gaming Systems