Evaluating Impact of Storage on Smartphone Energy Efficiency David T. Nguyen

LIFE IN MOBILE ERA.. 1,038,000,000 SMARTPHONE USERS WORLDWIDE [IBTIMES] 27% INCREASED # SMARTPHONES SOLD ANNUALLY [IDC] Figure Courtesy: David T. Nguyen2

SMARTPHONE APPS DO EVERYTHING! 850,000 APPS IN APPLE STORE 05/13 [APPLE] 800,000 APPS IN GOOGLE PLAY 05/13 [CANALYS] 145,000 APPS IN WINDOWS STORE 05/13 [CANALYS] 120,000 APPS IN BLACKBERRY WORLD 05/13 [CANALYS] Figure Courtesy: David T. Nguyen3

Still BIG Problem David T. Nguyen4 Figure Courtesy:

Smartphone Dislikes David T. Nguyen5 Source: ChangeWave

Outline  Introduction  Background  Experimental Study  Pilot Solution  Evaluation David T. Nguyen6

Introduction Researching energy consumption essential What has been done ◦ Performance bottleneck in storage [Kim et al., FAST ‘12] ◦ No direct study of storage – energy consumption correlation David T. Nguyen7

Introduction Thesis Statement ◦ Investigate impact of storage on smartphone energy efficiency ◦ Explain root reasons of such impact ◦ Develop storage-aware energy saving solutions Expected Contributions ◦ Better understanding of storage subsystem and its impact on energy efficiency ◦ Storage-aware energy saving solutions David T. Nguyen8

Outline Introduction  Background  Experimental Study  Pilot Solution  Evaluation David T. Nguyen9

I/O Path David T. Nguyen10 Red: Nexus One default static configurations

Outline Introduction Background  Experimental Study  Pilot Solution  Evaluation David T. Nguyen11

Approach Investigate impact of different storage configurations on power levels 1.Run series of benchmarks under default configurations 2.Repeat benchmarks under different configurations 3.Compare energy consumptions David T. Nguyen12

Setup Rooted smartphone Nexus One 8 benchmarks Monsoon Power Monitor David T. Nguyen13

Power Consumption: Default Config. (Queue Depth 128 / Write-back cache) David T. Nguyen14 Different algorithms - different power levels No algorithm optimal for all benchmarks Changing algorithms may save energy

Power Consumption: Queue Depth 4 David T. Nguyen15 Shorter queue depth saves energy in most cases Not storage intensive benchmarks consume more power due to overhead of smaller queue

Optimal Configurations Run benchmarks with all combinations of scheduling algorithms and queue depths Record in benchmark table David T. Nguyen16

Outline Introduction Background Experimental Study  Pilot Solution  Evaluation David T. Nguyen17

Big Idea Track phone’s run-time I/O pattern Match phone’s pattern with pattern from benchmark table Dynamically configure parameters with optimal savings David T. Nguyen18

SmartStorage Architecture David T. Nguyen19

GUI David T. Nguyen20

I/O Pattern Matching David T. Nguyen21

Outline Introduction Background Experimental Study Pilot Solution  Evaluation David T. Nguyen22

Energy Savings: Nexus One David T. Nguyen23

Remaining Steps Energy savings with different caching policies / file systems / queue depths Matching using machine learning Adaptive I/O pattern recalculation Root reasons of energy savings David T. Nguyen24

THANK YOU! David T. Nguyen25