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Coordinating Cellular Background Transfers using LoadSense

Coordinating Cellular Background Transfers using LoadSense. Abhijnan Chakraborty , Vishnu Navda , Venkataa N. Padmanabhan , Ramachandran Ramjee Microsoft Research India . Presented by ZWZ. Outline. Introduction Motivation LoadSense The Peak-n-Sneak Protocol Evaluation Discussion.

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Coordinating Cellular Background Transfers using LoadSense

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  1. Coordinating Cellular Background Transfers using LoadSense AbhijnanChakraborty, Vishnu Navda, Venkataa N. Padmanabhan, RamachandranRamjee Microsoft Research India . Presented by ZWZ.

  2. Outline • Introduction • Motivation • LoadSense • The Peak-n-Sneak Protocol • Evaluation • Discussion

  3. Introduction • Background transfers. • Fluctuantthroughput. • Cellular Workload.

  4. Motivation • Is throughput stable? • 100G+ data download over 100+ hours. • Airtel: 3G and LTE in Bangalore, India • AT&T: LTE in Seattle, US • BSNL: 3G in Bangalore, India

  5. Motivation • What if we can predict the throughput? • Is signal quality (pilot power) a good indicator?

  6. LoadSense • The Metric: Power Radio • PR = PilotPower/TotalRawPower

  7. LoadSense • Both link quality and cellular load have an impact on the throughput. • Obtaining these information using a Specified tool from QualComm, QXDM, on a Windows Phone.

  8. LoadSense • Predicting throughput. • High (≥1.5Mbps) or low (<1.5Mbps). • Using SVM (RBF)

  9. Peek-n-Sneak Design • Peek: LoadSense. • Sneak: Collision resolution.

  10. Evaluation • Micro-benchmarks

  11. Evaluation • Macro-benchmarks

  12. Conclusion • Key point: throughput prediction with SVM. • Contributions： • LoadSense. • Peek-n-Sneak protocol, which saves energy consumption.

  13. Discussion • Other factors? • Pilot power & power ratio. Movement? • Possible directions. • Inference. • Correlation estimation/prediction. • Using SVM. (原因<=>结果) • app能耗分析，对一些用量(内存、radio等)做SVM，然后可以就可以通过这个来判定是否会引起battery drain. • Diagnosis. 同样的套路。 S A B C

  14. Thanks! Q & A

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