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

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

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  1. 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)

  2. 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!

  3. 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 +

  4. Radio Characteristics Higher throughput radios have a lower energy/bit value … have a higher idle power consumption …and they have different rangecharacteristics

  5. 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.

  6. 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

  7. 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)

  8. 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

  9. 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

  10. 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

  11. Results: Baselines Switching policies perform better that WiFi policies for “idle” benchmark, similar for “transfer”

  12. 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

  13. Summary • SwitchR: Multi-radio switching architecture • Incrementally deployable • Energy Savings (72% over WiFi-PSM) • Can increase battery lifetime substantially

  14. Thank You! Website : http://mesl.ucsd.edu/yuvraj Email : yuvraj@cs.ucsd.edu

  15. 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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