1 / 15

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

erika
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related