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Erd OS Enabling opportunistic resources sharing in mobile Operating Systems

Erd OS Enabling opportunistic resources sharing in mobile Operating Systems. Narseo Vallina-Rodríguez Jon Crowcroft University of Cambridge. MUM 2010, Cyprus. WiFi. Bluetooth. CPU ( 1 GHz ). GSM/GPRS/3G. Storage (> 2 GB ). Camera. Accelerometer. GPS. Motivation. Motivation.

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Erd OS Enabling opportunistic resources sharing in mobile Operating Systems

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  1. ErdOSEnabling opportunistic resources sharing in mobile Operating Systems Narseo Vallina-Rodríguez Jon Crowcroft University of Cambridge MUM 2010, Cyprus

  2. WiFi Bluetooth CPU (1 GHz) GSM/GPRS/3G Storage (>2 GB) Camera Accelerometer GPS Motivation

  3. Motivation “Energy is still the main limitation in mobile systems”

  4. Motivation

  5. CPU GPS 3G Motivation

  6. Motivation

  7. Motivation Network Type Operator 1 Operator 2 Signal Strength

  8. Motivation Why not sharing mobile resources opportunistically with other users?

  9. ErdOS

  10. ErdOS Social energy-aware OS Access co-located resources opportunistically Customised proactive resources management Social connections provide access control

  11. Dataset Description • 18 Android OS users • 1-2 weeks • Resources Tracker “Exhausting battery statistics”. Mobiheld 2010

  12. Battery Statistics O.S. Info Current Voltage Remaining Capacity Temperature Charging Status CPU Process Memory Airplane Mode Telephony State Cellular Network Type Cellular Network State WiFi State Bluetooth State GPS State Traffic Time Location (Cell ID) Roaming Screen State Contextual Network & Telephony Dataset Description

  13. Usage Analysis Tools Principal Component Analisys (PCA): Transforms a number of possibly correlated variables into a smaller number of uncorrelated ones called Principal Components

  14. Principal Component Analysis

  15. Principal Component Analysis

  16. Context importance

  17. 50 Low Predictability 40 U4 U17 U12 U18 Std dev (%) U11 U15 30 U3 U8 U16 U13 U9 U5 U2 U1 20 U6 U7 U10 U14 10 High Predictability 40 60 80 100 20 Mean (%) Spatial context: Screen usage

  18. 50 Low Predictability 40 U8 U12 U10 Std dev (%) U6 U3 30 U11 U13 U2 U7 U9 20 U5 U1 10 U18 U4 High Predictability U15, 16 40 60 80 100 20 Mean (%) Spatial context: Cellular traffic U14

  19. Temporal context: Daily usage

  20. System Act Users’ Apps Users’ Actions Social Actions Remote Act. Resources Allocations:Activities Users’ Activities 2nd Level Activities

  21. Forecasting Resources Demands

  22. Forecasting Resources State

  23. Access Control • Social links facilitate access control and security • Unix-like permissions are made automatically based on users’ social networks • Proximity reduces privacy and security issues • OSNs can help to exchange public keys

  24. Architecture

  25. Related work • Resource allocation and energy-aware OS • ECOSystem. Zeng et al. ACM ASPLOS, 2002 • Quanto. Stoica et al. USENIX 2008 • CinderOS. Rumble et al. MOBIHELD 2009 • Mobile usage and energy demand • Falaki et al. ACM Mobisys 2010 • Oliver, ACM HotPlanet 2010 • Balasubramanian et al. ACM IMC 2010 • Rice et al. ACM PerCOM 2010

  26. Conclusions • Energy is a primary target for optimization in mobile handsets • Benefits in QoS and energy savings by accessing resources opportunistically • Social links can be used for access control policies • Applications and users’ behavior generate complex dynamics and interdependencies among resources • Energy allocation and resources control must be customized to each user and handset • Pro-active resources management aided by contextual information

  27. Future Work • Finishing implementation as an Android OS extension • Performance/Scalabilityevaluation • Demonstrate benefits of sharing different resources (Cellular Nets, GPS, CPU) • Resources Discovery Protocols • Research on lighter forecasting techniques • Cloud Computing? • Security evaluation • Incentive schemes?

  28. Questions? Thanks! Email: nv240@cam.ac.uk http://www.cl.cam.ac.uk/~nv240/erdos.html

  29. Usage Analysis - Tools Factor Analysis: Describes variability among observed variables in terms of fewer unobserved variables called factors

  30. Factor Analysis

  31. Previous energy-aware OS • ECOSystemGeneral Purpose, 2002 • QuantoSensors, 2008 • CinderMobile phones, 2009 • Main problems: • Sampling technique • Energy allocation based on battery capacity/discharging rate or offline measurements • Inaccurate indicator • Mobile resources demand require a totally different approach: • Context matters (i.e. Signal strength) • Proactive resources management

  32. Forecasting Downlink Traffic

  33. Temporal context. Periodicity

  34. Name Manager Username Physical Address (PhyAddr) Bluetooth MAC Address 802.11 MAC Address email(s) Social Networks (SocNets) Type Username Password Resources (Res) Type Name Availability Demand Contacts Username

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