Technical Paper Review:Are All Games Equally Cloud-Gaming-Friendly? An

Electromyographic Approach

Kumar Gaurav

CS300

October 21, 2014

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Overview

1 Introduction

2 Related Work

3 Approach

4 Results

5 Model

6 Applications

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Introduction

Introduction

What is cloud gaming and how is it different from online gaming?

Why cloud gaming?

Does it depend on the type of game?

Need to develop a model to predict game’s real-time strictness

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Introduction

What is cloud gaming?

Cloud gaming, also known as Games-on-Demand, is a technology whichoffloads the tasks of graphics rendering, in addition to computation andstorage needs, into clouds.

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Introduction

Why cloud gaming?

Cloud gaming technology makes any computer game playable on a thinclient without previous worries of hardware requirements. Consequently,there are no software installation overheads or compatibility issues ifplayers wish to try a game because all the hardware and software isprovided in the data centers by the game operators.

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Introduction

Does it depend on the type of game?

The investigation is based on the observation that some games seem moreplayable than others in the cloud despite the inevitable latency, which iscaused by network delay and processing delays that occur at both theserver and client.The players negative emotion against the latency isquantified based on the electrical potentials produced by their corrugatorsupercilii muscle.

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Introduction

Need to develop a model to predict game’s real-timestrictness

With the proposed model, one can easily assess a cloud games sensibilityto latency without conducting costly QoE measurement experiments.

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Related Work

Related Work

In M. Jarschel, D. Schlosser, S. Scheuring, and T. Hossfeld, An evaluationof QoE in cloud gaming based on subjective tests, in Workshop on FutureInternet and Next Generation Networks, Jun 2011, Jarschel conducted userstudies to measure and model the QoE of OnLive during game play. Theyproposed a regression model to predict OnLives QoE according to networkperformance metrics and players experience and attitude towards thegames. This work complements it by answering the following unexploredquestions:

Is the inevitable response latency due to cloud gaming architecturegame-dependent in terms of user experience?

If so, how can a games design affect its susceptibility to latency andhow can we utilize such knowledge to improve the gaming experienceand system resource allocation?

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Approach

Quantifying the Real-Time strictness

Nine games of the three genres of ACT, FPS, and RPG were chosen.

ACT games: LEGO Batman, Devil May Cry, and Sangoku Musou 57

FPS games: COD: World at War, F.E.A.R 2, and Unreal Tournament3

RPG games: Ys Origin, Loki: Heroes of Mythology, and Torchlight

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Approach

QoE measurement

Facial electromyography(fEMG) was used to measure player’s experience.Thus,

RS = ∆Q/L

where ∆Q is the degraded QoE when certain degree of Latency L wasintroduced.

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Approach

Latency Emulation

WH KEYBOARD LL and WH MOUSE LL hooks (by calling theSetWindowsHookEx API) in Windows was used to delay all keyboard andmouse inputs made by subjects.

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Results

Figure : The relationship between latency and average fEMG potentials.

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Results

Figure : The real-time strictness of the studied games.

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Model

Assuming that we have p subjects, each of which recorded a game playvideo containing f frames, and the jth frame of the video recorded by theith subject have the motion vectors {mcij1,mcij2, ...,mcijm}, we computethe games screen dynamics as

p∑i=1

f∑j=1

sd(|mcij1|, |mcij2|, ..., |mcijm|)f · p

/inter -frame-time

Here sd() is the standard deviation and the final term inter-frame-time isused to normalize the screen dynamics factor so that it is comparableacross games with different frame rates.

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Model

Figure : A screen shot of Ys with its motion vectors overlaid. The motion vectorsreveal how the macroblocks shifted since the last P- or I-frame in the recordedH.264 video. Because the avatar was moving downward, the motion vectors ofthe frame were generally pointing toward the top center on the screen.

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Model

The command heaviness is given by:

command heaviness = screen dynamics/input rate

A regression based predictive model is given by:

realtime-time strictness = 1.8− 0.28× command heaviness

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Model

Figure : (left) The scatter plot of the actual and predicted RS of the 9 games.(right) The relative error of RS prediction in cross validation.

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Applications

Applications

One can infer players having a worse gaming experience than others,and accordingly prioritize the servers resources, such as CPU andGPU, to reduce those players latencies and thereby mitigate QoEdegradation they would otherwise experience

One can estimate the total utility (by considering both operation costand players satisfaction) and assign to each player a data centerwhich provides a just good enough gaming experience while saving asmuch operation cost as possible.

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Applications

Thank you!

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