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Computation Offloading

Computation Offloading. 11/13. Proposal - Offloading. Identify essential information for making offloading decisions Essential information retrieval What to identify How to identify Implement/Improve framework How to build our prototype The influences of property change

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Computation Offloading

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  1. Computation Offloading 11/13

  2. Proposal - Offloading • Identify essential information for making offloading decisions • Essential information retrieval • What to identify • How to identify • Implement/Improve framework • How to build our prototype • The influences of property change • Computation-intensive => I/O-intensive • Decision policy • Client OS/Server side/Hybrid offloading decision making. • Smart offloading policies. • (Client OS rescheduling) • Resource integration • Virtual storage • Support file-related computation-intensive applications. • Ex: Face matching

  3. Essential Information Retrieval • What to identify? • According to most related works, they care about: • Power consumption • Performance • Execution time • Response time

  4. Related Works • Cyber foraging • a pervasive computing technique where resource poor, mobile devices offload some of their heavy work to stronger surrogate machines in the vicinity. • Framework for Power Aware Remote Processing, ‘01 • Puppeteer, ‘01 • Spectra, ‘02 • Chroma, ‘03 • Goyal & Carter's system, ‘04 • Slingshot, ‘05 • Scavenger, ‘09

  5. Related Works(Cont.) • Scavenger • Offloading Python applications • Remote data store • Client-side scheduling • Considers surrogate strength and utilization, input size,network media(speed), and data locality. • Can add output size and task complexity into consideration. • Local and global profiling. • Application developer needs to annotate the offloading function.

  6. Related Works(Cont.) • CloneCloud • Uses a combination of static analysis and dynamic profiling to optimally and automatically partition an application. • Optimization objective is to choose a set of partitions that minimize sum of computation cost and migration cost. • Use a standard integer linear programming solver to solve the optimization problem.

  7. Related Works(Cont.) • Kumar’s work takes into account: • The speed of both smart phone and remote cloud resource. • The number of bytes that need to be transferred. • The network bandwidth. • The energy consumption of the smart phone in idle, computing, and communicating state.

  8. Related Works(Cont.) • MAUI • Offloading .NET applications(C#) • Developer annotates “remoteable” methods. • Profiling • Device: energy consumption. • Program: state transfer requirements, runtime duration, # of CPU cycles. • Network: Round-trip time, bandwidth, pack loss. • Also formulate into a 0-1 ILP problem. • Solver is on the server instead of device.

  9. Summary • Most of the existing works focused more on power than performance. • The most important element is the network speed, then the computation ability of server. • I think we can start by monitoring following metrics: • (device)CPU load changing • Network speed • 3G/Wifi

  10. Applicable • Image processing • object recognition, OCR, face detection, barcode analysis • Audio processing • speech recognition • Text processing • machine translation • Artificialintelligence for games • Chess • 3D rendering • 3D home interior design • Security • taint analysis and virus scans

  11. My Consideration • The cyber foraging technique starts from 2001. • Why it does not become popular during the rise of smart phone?

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