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: http://www.ideas4ios.com

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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:

http://aptito.com

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Still BIG Problem

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Figure Courtesy: http://cdn.cultofmac.com

Smartphone Dislikes

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0% 10% 20% 30% 40%

Call Quality

Contact List Import

Excessive Dropped Calls

3G Quality

Screen Size

4G Capability

Battery Life

Source: ChangeWave

Outline

Introduction

Background

Experimental Study

Pilot Solution

Evaluation

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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

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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

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Outline

Introduction

Background

Experimental Study

Pilot Solution

Evaluation

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I/O Path

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Red: Nexus One default static configurations

Outline

Introduction

Background

Experimental Study

Pilot Solution

Evaluation

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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

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Setup

Rooted smartphone Nexus One

8 benchmarks

Monsoon Power Monitor

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Power Consumption: Default Config.

(Queue Depth 128 / Write-back cache)

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Different algorithms - different power levels

No algorithm optimal for all benchmarks

Changing algorithms may save energy

Power Consumption: Queue Depth 4

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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

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Outline

Introduction

Background

Experimental Study

Pilot Solution

Evaluation

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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

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SmartStorage Architecture

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GUI

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I/O Pattern Matching

Compare phone’s I/O pattern with patterns from benchmark table

Matching feature: #𝑟𝑒𝑎𝑑𝑠 𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒𝑑 /𝑠𝑒𝑐𝑜𝑛𝑑

#𝑤𝑟𝑖𝑡𝑒𝑠 𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒𝑑 /𝑠𝑒𝑐𝑜𝑛𝑑

If phone’s rate of reads/writes per second close to a benchmark from table ◦ match is found

Else ◦ no match

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Outline

Introduction

Background

Experimental Study

Pilot Solution

Evaluation

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Energy Savings: Nexus One

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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

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THANK YOU!

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