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SwitchR: Reducing System Power Consumption in a Multi-Client Multi-Radio Environment . Yuvraj Agarwal (University of California, San Diego) Trevor Pering, Roy Want (Intel Research), Rajesh Gupta (UC San Diego). Wearable and Mobile Devices: . Increasing Functionality

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Switchr reducing system power consumption in a multi client multi radio environment l.jpg

SwitchR: Reducing System Power Consumption in a Multi-Client Multi-Radio Environment

Yuvraj Agarwal (University of California, San Diego)Trevor Pering, Roy Want (Intel Research),Rajesh Gupta (UC San Diego)


Wearable and mobile devices l.jpg
Wearable and Mobile Devices:

  • Increasing Functionality

    • Faster processors, more memory

  • Applications are increasingly communication intensive

    • Streaming video, VoIP, Downloading files

  • Multiple wirelessradios often integrated on single device

    • (Bluetooth for PANs, WiFi for high-bandwidth data access)

  • Wearable/Mobile Computers  Power Consumption is very important!

    • Limited by battery lifetime

    • Communication over WiFi reduces battery lifetime even further….

      • In some cases up to 50% of total energy drain!


Reducing the energy for communication l.jpg
Reducing the energy for communication

  • Opportunity: Availability of multiple radio interfaces …

    • Can all be used for data transfer

    • Different characteristics : bandwidth, range, power consumption

  • Typically function as isolated systems,

    • Can we coordinate usage to provide a unified network connection ?

      • Seamlessly switch between radios

    • Primary Goal: Save energy

X

+


Radio characteristics l.jpg
Radio Characteristics

Higher throughput radios have a lower energy/bit value … have a higher idle power consumption

…and they have different rangecharacteristics


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Multi-Radio Switching

  • CoolSpots [Mobisys ‘06]:

    • Multi-Radio switching for a single-clientscenario

    • Specialized access point (Bluetooth + WiFi)

    • Switching decisions – Local to client

  • SwitchR:

    • Leverage existing WiFi APs : Incrementally deployable

    • Considers traffic imposed by other devices in a multi-clientscenario

    • Switching decision – global since it affect other clients

    • Evaluate energy savings on a distributed testbed

Problem Statement: Reduce energy consumption by choosing appropriate radio interface, while taking into consideration other clients.


Switchr architecture l.jpg
SwitchR Architecture

Infrastructure Network

BTG (Bluetooth Gateway)

Bluetooth Link

MD1

WiFi Link

Ethernet Link

Wi-Fi

Zone

MD2

Wi-Fi AP (WFAP)

MD3

MD = Mobile Devices

MD4

  • Switching Policy:

  • Hybrid Approach

  • Application requirements at nodes (local)

  • Channel quality and bandwidth (global)

  • Switching Mechanism:

  • Network Level Reconfigurations

  • ARPs and Routing updates


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Multi-Client Switching Policy

  • Hybrid approach to make switching decisions

    • Local knowledge (node level)

    • Global (channel utilization by other nodes)

  • Switching up (Bluetooth  WiFi)

    • ICMP response time and radio RSSI values

    • Capture application needs and channel characteristics

  • Switching-down (WiFi  Bluetooth)

    • Measure application bandwidth requirements

    • Periodically query BTG for residual capacity

    • Measure channel/link quality (local)


Evaluation testbed l.jpg
Evaluation: Testbed

BTG (Bluetooth Gateway)

Infrastructure Network

Bluetooth (Always Connected)

MD1

WiFi (Dynamically Switched)

Static Wired Connection

Wi-Fi

Zone

MD2

Wi-Fi AP

MD3

Mobile Device (MD)

MD4

Stargate2 node

  • Stargate2 research platform

    • WiFi + Bluetooth + Integrating power and data monitoring

  • Benchmark applications are striped across devices


Evaluation benchmarks l.jpg
Evaluation: Benchmarks

  • Baselines:

  • Idle: connected, but no data transfer

  • Transfer: bulk TCP data transfer

  • Streaming:

  • Media: 128k, 156k and g711 VoIP codec

  • Various QoS requirements

  • Web:

  • Combination of idle and data transfer

  • Idle: “think time”

  • Small transfer: basic web-pages

  • Bulk transfer: documents or media


Evaluation switching policies l.jpg
Evaluation: Switching Policies

  • Baselines policies

    • “Wifi-CAM” (Awake Mode)

    • “Wifi-PSM” (Power Save Mode)

  • Single-Client based “cap-dynamic” switching policy

  • SwitchR: “multi-client” switching policy

    • Combines both local (per client) and global knowledge


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Results: Baselines

Switching policies perform better that WiFi policies for “idle” benchmark, similar for “transfer”


Results l.jpg
Results:

multi-client policy saves up to 62% over single-client cap-dynamic policy

VoIP and streaming benchmarks benefit most since streams can use BT channel


Summary l.jpg
Summary

  • SwitchR: Multi-radio switching architecture

    • Incrementally deployable

    • Energy Savings (72% over WiFi-PSM)

    • Can increase battery lifetime substantially


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Thank You!

Website : http://mesl.ucsd.edu/yuvraj

Email : [email protected]


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Results: VoIP traffic

Although, bandwidth requirements less than bluetooth channel capacity

Web benchmark causes VoIP streams to switch to WiFi

multi-client policy saves upto 65% over cap-dynamic, allows VoIP streams to switch back


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