Pictor - sprite.utsa.edu

PictorA BenchmarkingFramework(ResearchFlatform)

for CloudGaming

Tianyi Liu*SenHe*Sunzhou Huang*DannyTsang*Lingjia Tang

JasonMarsWeiWang*

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The University of Texas at San Antonio*University of Michigan, Ann Arbor

Modern-DayComputerGames

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FutureComputerGames

ComputerGames

Modern-Day Computer Games

• If we would like to play video games, what should we do first?

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Download & Install


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Fast-EvolutionaryComputerGames

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FutureComputerGames:CLOUDGAMING

ComputerGames

What does Cloud Gaming look like?

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Cloud3DSystemOverview

2D/3D Libs

𝑰𝒎𝒂𝒈𝒆𝒔

Client proxyServerproxy

𝐼𝑛𝑝𝑢𝑡𝑠

Internet

Server Client

𝑰𝒎𝒂𝒈𝒆𝐼𝑛𝑝𝑢𝑡

𝐶𝑎𝑙𝑙𝑠

𝐶𝑚𝑑𝑠

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3D applications

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• Problem: 3D applications are becoming a major type of workload for cloud,However, there is no standard benchmarking infrastructure or platform in

this area to support public research.

• Goal: build a cloud 3D benchmarking platform.

Motivation&Goal

Outline

1.Background:Cloud3DSystemBasics2.ThePictorFramework

IntelligentClient

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3. Evaluation4.Cloud Security

RemoteDesktopBenchmarkSuite

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RemoteDesktop&BenchmarkSuite• SystemSoftwarePlatform• TurboVNC andVirtualGL 2.6

• Cloud3DBenchmarkSuite• Four3Dapplications• TwoVirtualReality(VR)applications• Allapplicationsbelongtodifferentgamegenres

Application Area BenchmarkGame: Racing SuperTuxKart (STK)Game: Real-time Strategy 0A.D. (0AD)Game: First-person Shoot Red Eclipse (RE)Game: Online Battle Arena DoTA2 (D2)VR: Education/Game InMind VR (IM)VR: Health IMHOTEP (ITP)

Cloud3DSystemOverview

2D/3D Libs


Client proxyServerproxy


Internet

Server Client



𝐶𝑚𝑑𝑠

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3D applications

IntelligentClientCV

Intelligent Client


RNN

𝑨𝒄𝒕𝒊𝒐𝒏𝒔


Human Client

Human Player

Intelligent Client

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VS

Interactiveapps

2D/3D Libs


CV

RNNClient ProxyIntelligent Client

ServerProxy


𝑨𝒄𝒕𝒊𝒐𝒏𝒔Internet

Server Client



𝐶𝑚𝑑𝑠

Overview:Cloud3DSystemwithIntelligentClient

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Outline


IntelligentClient

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Evaluation:IntelligentClient• Accuracy

• Intelligentclienthashighaccuracyandcanmimichumanplayerfaithfully.• TheaverageerrorofRTTdistributionis1.6%,andDota2(D2)hasmaxerrorof3.2%

Fig3.1 Round-Trip Time distribution(RTT) of Intelligent client(IC) and human players(H) got from 6 benchmarks.

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Evaluation:IntelligentClient• Performance• CVtimeisabout80ms,andInputgenerationtimeisabout2ms.• Intelligentclientcangenerate800actionsperminute,whichisfastenoughtosimulateprofessionalgameplayers (300actionsperminuteatmaximum).

Fig3.2 Computer vision time (left side) and input generation time(right side).

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PictorinourLAB:• YouTubeVideos:

• https://www.youtube.com/watch?v=4VG0KgFgc_c• https://www.youtube.com/watch?v=-BnYlKonxJI• https://www.youtube.com/watch?v=mgz5tWt2_rc

https://www.youtube.com/watch?v=4VG0KgFgc_c

https://www.youtube.com/watch?v=-BnYlKonxJI

https://www.youtube.com/watch?v=mgz5tWt2_rc

Outline


IntelligentClient

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Side Channel Attack

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Side Channel Attack Example

• Spectre• https://spectreattack.com/spectre.pdf• https://meltdownattack.com• https://www.youtube.com/watch?v=Phmt8UrofDY

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https://meltdownattack.com/

https://meltdownattack.com/

https://www.youtube.com/watch?v=Phmt8UrofDY

Take-HomeMessages:

• Cloud Gaming is a promising future gaming format.• You can play game anytime and anywhere without installation.• Cloud Gaming works well with Cellphone, tablet, laptop, and desktop. • Our Cloud Gaming research platform can make computers play games

by themselves.• Cloud Gaming also has some security issues, but the attack is difficult

to realize in practice.

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PictorA BenchmarkingFramework(ResearchFlatform)

for CloudGaming

Tianyi Liu*SenHe* Sunzhou Huang* DannyTsang* Lingjia Tang

JasonMarsWeiWang*

23

The University of Texas at San Antonio*University of Michigan, Ann Arbor

This presentation and recording belong to the authors. Contact: [email protected] ; [email protected]

mailto:[email protected]

mailto:[email protected]

Backup Slides

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Evaluation:PictorFramework

-1.00%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

STK 0AD RE D2 IM ITP AVG

< 6%

2.73%

• Overhead• TheFPSreductionis2.73%onaverage,and5.5%atmaximum.• Pictorframeworkhaslowoverhead.

Fig3.3 FPS reduction with Pictor framework.

FPS

Redu

ctio

n

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• HardwareEfficiency:• OneGPUcanserve2to3gameswhilemaintaining25+FPS.

ResourceEfficiency

• PowerEfficiency:• Powerconsumptionpergamealsodecreasesignificantly.

Fig3.5 Average server and client FPS when running 1-4 instances of same game on one server.

Fig3.6 Per-instance power usage when running 1-4 instances of same game on one server.

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Performance:CPUCycleBreakdown• Running1– 4instancesofsamegame,figureshows,• IM has problems on instruction fetching, comparing to other applications, since IM has

largest portion on front end.• Serious contention is observed on back end, especially for RE.

Fig3.4 CPU cycle breakdown into back end, front end, bad speculation, and retiring.

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Performance:RTTBreakdown• Round-TripTime(RTT)Breakdown• SendFrametimeandSendInputtimearethetimespentoninputsendingandframetransmittingonthenetwork.• Whenrunning1– 4instancesofsamegame,ServertimedominatestheRTT,indicatingcloudsystem/hardwaredesigniscrucialtocloud3Dperformance.

Fig3.7 Break RTT into 3 parts: server time, send frame time, and send input time.

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Performance:ServerTimeBreakdown• ServerTimeBreakdown

• Whenrunning1– 4instancesofsamegame,applicationtimeismosttimeconsuming.

Fig3.8 Server time breakdown when running 1-4 instances of same game on one server.

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Performance:AppTimeBreakdown

Pictor Framework is effective for identifying system bottleneck

• ApplicationTimeBreakdown• Whenrunning1– 4instancesofsamegame,frame copy needs to be optimized.

Fig3.9 Application time breakdown when running 1-4 instances of same game on one server.

Caching

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Observation1: Time measurement of readPixel() returns different results, when we use CPU clock and GPU clock. The difference is about 6~9ms. • XGetWindowAttributes() is called in readPixel() and it is extremely slow and

consumes 6~9ms.• XGetWindowAttributes() only returns window size.

Key idea: only call XGetWindowAttributes() once and save the result for future use.

Insight: Caching makes Pixel Data Copy Stage shorter.

1 2 3 4 6 8 9N N N N N-1 N-1 N-1 N-1

N5

7

Render1 2 3 4 6 8 9N N N N N-1 N-1 N-1 N-1

N5

7

RenderBenefit

Input Processing Frame Copy and Sending

Parallelization

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VIDEOMemDRAM

PBO

FB

CPUCPUCPUCPU

CPUCPUCPUGPU

Frame

Observation2:Pixel copy has two sequential steps: FrameBuffer->PixelBuffer->DRAM.

DRAM

PBO2

FB

CPUCPUCPUCPU

CPUCPUCPUGPU

PBO1

Key idea: parallel the sequential steps.

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EffectivenessofTwoOptimizations• Optimizations improve server FPS by 57.7% on average and 115.23% at

maximum.• The client FPS was improved by 7.4% on average.• The RTT was reduced by 8.5% on average.

Fig3.10 Improved FPS/RTT with our optimizations.

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DockerOverhead

• Motivation: Cloud 3D applications are becoming a major type of workload for cloud. • Main reason: no standard benchmarking infrastructures for this new area,

including benchmark suite and performance evaluation tools.

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• Challenges: 3D Benchmarking Framework need to 1. generate human-like input to interact with random objects in 3D

applications. 2. track input processing and accurately measure performance of each

component in cloud 3D system.3. be general and extendable, which is required for the fast-evolution 3D

applications.• Goal: An effective benchmarking framework for cloud 3D that can

address challenges above.

Motivation&Goal

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Evaluation:BenchmarkDiversity

The benchmark suite covers a variety of 3D applications with diversebehaviors and resource utilizations


à GoodDiversity.

Cloud3DSystemPipeline(1)

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Render and send Frame N

1 2 3 4 6 8 9N N N N

5 7

Input Processing Render Frame Sending

2D/3D Libs


Client Proxy

Server Proxy


Internet

Server Client𝑰𝒎𝒂𝒈𝒆𝐼𝑛𝑝𝑢𝑡


𝐶𝑚𝑑𝑠

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4

5

6

7 8 9

N N N N N

Frame CopyCPU StageGPU Stage

1 2 3 4 6 8 9N+1 N+1 N+1 N+1

5 7N+1 N+1 N+1 N+1 N+1

Hook 1 Hook 2 Hook 3 Hook 4 Hook 5 Hook 6 Hook 7 Hook 8 Hook 9 Hook 10

Render and send Frame N+1

ID RTT

ID VALID T_hook1 T_hook2 T_hook3 T_hook4 T_hook5 T_hook6 T_hook7 T_hook8 T_hook9 T_hook10

FPS

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Render Frame N

1 2 3 4 6 8 9Send Frame N-1N N N N N-1 N-1 N-1 N-1

N5

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Input Processing

Render

Render Frame N+2

1 2 3 4 6 8 9Send Frame N+1N+2 N+2 N+2 N+2 N+1 N+1 N+1 N+1

N+25

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Render Frame N+11 2 3 4 6 8 9

Send Frame N N+1 N+1 N+1 N+1 N N N NN+15

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Render

Render

Cloud3DSystemPipeline(2)

Frame SendingFrame CopyGPU Stage

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ExperimentSetup• SystemSoftwarePlatform• TurboVNC andVirtualGL 2.6

• Cloud3DBenchmarkSuite• Four3Dapplications• TwoVRapplications• Allapplicationsbelongtodifferentgamegenres.

• Hardware• Server:Inteli7-7820x,16GB• NVIDIAGTX1080TiGPU,11GB• Client:Inteli5-7400,8GB


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