Enabling Desktop-Class Performance for Mobile Applications with Pervasive and Flexible Mobile-Cloud Computing

Architectures and Systems for Mobile-Cloud Computing: A Workload-Driven PerspectivePrashant NairAdviser: Moin QureshiECEGeorgia TechXin ZhangAdviser: Mayur NaikCSGeorgia TechS2014-66133/26/2014 Silicon Valley

~30 secondsBriefly introduce ourselves1MotivationMobile devices have become the primary computing deviceyearsperformance

3/26/2014 Silicon Valley2G3G4G2~1minMobile devices are becoming the primary computing devicesThe performance of mobile devices have improved dramatically in the past years but still theres large gap between server performance and mobile performance.It is unlikely that the performance of mobile devices will catch up servers because of stringent limits on the size, weight, and energy constraints.The speed and coverage of mobile networks have improved rapidly in past years.One way to bridge the gap between peoples increasing demands on mobile applications and limited power of mobile devices is to offload computation from mobile devices to the cloud.2Hello Siri, What is Mobile Cloud Computing?

Call John.

Call JohnDialing 123-456-7890

33/26/2014 Silicon Valley~1minIn fact, some mobile apps are already offloading computation to the cloud. On example is siri.Describe the workflow of siri.3Mobile-Cloud: Enabling New Applications

Idea: Offload Computation to Cloud

43/26/2014 Silicon Valley~1minMobile cloud computing will bring richer and more powerful apps.Mobile devices have rich sensors. (phone, tablet and even wearable devices)Using these sensors we can implement applications like augmented reality.However mobile devices do not have the power to run them(show videos)Mobile-cloud computing is the rescue(show another pair of videos)4Key Challenges01010101010101111101011100011. Interleaved I/O and computation

2. Network latency01010101011010100101110000010110101011013. Diverse and dynamic execution environment

53/26/2014 Silicon Valley~1min30sIntroduce the nave offloading scheme (Siri)It does not work in practice because:1. Interleaved I/O and computation(talk about the progress bar example)2. Network latency3. Diverse and dynamic environment5Our Solution: Flexible Offloading SchemesBi-directional offloading110100010001011100001010101010101

0101063/26/2014 Silicon ValleyChallenge 1: Interleaved I/O and computation~45sIllustrate bi-directional offloading.The catch is to allow interrupting the cloud executing to access I/O on mobile.6Our Solution: Lower Bandwidth via PersistencePersistent cloud01010101010101110101010001010101000011100010101

Challenge 2: Network latency73/26/2014 Silicon Valley~45sIllustrate persistent cloud. The catch is to utilize locality and partition the program. As a result, part of the program data lives on the cloud, part lives on the mobile.

Change 7Our Solution: Analytical Models for Tradeoffs

Software featuresNetwork featuresHardware featuresAnalytical modelRuntime decision!Challenge 3: Diverse and dynamic execution environment83/26/2014 Silicon Valley~1minBuilding analytical model:Trace workload from mobile apps, and extract software featuresNetwork featuresHardware features8Offloading System

Analytical modelHow to offloadOffloading schemesWhat to offload

93/26/2014 Silicon Valley~50sRound up.The offloading schemes tell our system how to offload a certain part of computation. Analytical model tells which part to offload at runtime.As a result, our system will be able to handle a large range of mobile apps, diverse hardware devices and different network nections9RoadmapMobile workload tracing Trace mobile workloads of top 150 Google Play apps

Workload characterization Identify features common and unique to mobile workloads

Analytical models of performance and energy usageProduce tolerable error bounds compared to hardware measurement

Mobile-cloud computing systemShow speedup and energy savings(Infrastructure implemented)(~3 months)(~3 months)(~6 months)103/26/2014 Silicon Valley~1min10

Result of Tracing Chess For 10 Rounds

UIAI

24 threads3 million function calls17 million memory reads13 million memory writes113/26/2014 Silicon Valley

~1minShow the statistics.(Mention that therere many threads, but many dont live long.) Explain thread dependency graph (read-write dependency).Explain the computation I/O interleaving bar graph(well, actually it is offloadable vs. unoffloadable in fact)Say this app has a relative simple algorithm in AI. Mobile cloud computing can enable more sophisticated algorithm.11Summary Mobile devices have become the new PC

Big performance gap between mobile and desktop

Our Proposal:

Enable desktop-class performance for mobile apps by:

Offloading computation to the cloud Using robust analytical modeling

Enable new applications and usage models

123/26/2014 Silicon Valley

~1minTalk about motivation againWhat we try to doBroader impact on programming and hardware, give vision

12