Investigating Decreasing Energy Usage in Mobile Apps via Indistinguishable Co...MobileSoft
"Investigating Decreasing Energy Usage in Mobile Apps via Indistinguishable Color Changes" by Tedis Agolli, Lori Pollock, James Clause
MobileSoft'17 Buenos Aires, Argentina, 2017.
Leafactor: Improving Energy Efficiency of Android Apps via Automatic RefactoringMobileSoft
"Leafactor: Improving Energy Efficiency of Android Apps via Automatic Refactoring"
by Luis Cruz, Rui Abreu and Jean-Noel Rouvignac
MobileSoft'17, Buenos Aires, Argentina, 2017
Using Automatic Refactoring to Improve Energy Efficiency of Android AppsLuis Cruz
The ever-growing popularity of mobile phones has brought additional challenges to the software development lifecycle. Mobile applications (apps, for short) ought to provide the same set of features as conventional software, with limited resources: such as, limited processing capabilities, storage, screen and, not less important, power source. Although energy efficiency is a valuable requirement, developers often lack knowledge of best practices. In this paper, we study whether or not automatic refactoring can aid developers ship energy efficient apps. We leverage a tool, Leafactor, with five energy code smells that tend to go unnoticed. We use Leafactor to analyze code smells in 140 free and open source apps. As a result, we detected and fixed code smells in 45 apps, from which 40% have successfully merged our changes into the official repository.
[February 2017 - Ph.D. Final Dissertation] Enabling Power-awareness For Multi...Matteo Ferroni
Power consumption has become a major concern for almost every digital system: from the smallest embedded devices to the biggest data centers, energy and power budgets are always constraining the performance of the system. Moreover, the actual power consumption of these systems is strongly affected by their current “working regime” (e.g., from idle to heavy-load conditions, with all the shades in between), which depends on the guest applications they host, as well as on the external interactions these are subject to. It is then difficult to make accurate predictions on the power consumed by the whole system over time, when it is subject to constantly changing operating conditions: a self-aware and goal-oriented approach to resource allocation may then improve the instantaneous performance of the system, but still the definition of energy saving policies remains not trivial as far as the system is not really able to learn from experience in real world scenarios.
In this context, this thesis proposes a holistic power modeling framework that a wide range of energy and power constrained systems can use to profile their energy and power consumption. Starting from the preliminary experience developed on power consumption models for mobile devices during my M.Sc. thesis, I designed a general methodology that can be tailored on the actual system's features, extracting a specific power model able to describe and predict the future behavior of the observed entity. This methodology is meant to be provided in an “as-a-service” fashion: at first, the target system is instrumented to collect power metrics and workload statistics in its real usage context; then, the collected measurements are sent to a remote server, where data is processed using well known techniques (e.g., Principal Components Analysis, Markov Decision Chains, ARX models, etc.); finally, an accurate power model is built as a function of the metrics monitored on the instrumented system. The generalized approach has been validated in the context of power consumption models for multi-tenant virtualized infrastructures, outperforming results from the state of the art. Finally, the experience developed on power consumption models for server infrastructures led me to the design of a power-aware and QoS-aware orchestrator for multi-tenant systems. On the one hand, I propose a performance-aware power capping orchestrator in a virtualized environment, that aims at maximizing performance under a power cap. On the other hand, I bring the same concepts into a different approach to multi-tenancy, i.e., containerization, thus moving the first steps towards power-awareness for Docker containers orchestration, laying the basis for further research work.
Full thesis: https://www.politesi.polimi.it/handle/10589/132112
LAS16-307: Benchmarking Schedutil in AndroidLinaro
LAS16-307: Benchmarking Schedutil in Android
Speakers: Steve Muckle
Date: September 28, 2016
★ Session Description ★
Being able to see the performance and power impacts of changes in a real world environment such as Android is a prerequisite to doing meaningful development on scheduler-guided frequency (or many other sensitive subsystems). The first half of this session will review setting up the tools to automate testing for performance and power in Android. The second half will cover the results of using these tests to compare the schedutil and interactive governors.
★ Resources ★
Etherpad: pad.linaro.org/p/las16-307
Presentations & Videos: http://connect.linaro.org/resource/las16/las16-307/
★ Event Details ★
Linaro Connect Las Vegas 2016 – #LAS16
September 26-30, 2016
http://www.linaro.org
http://connect.linaro.org
GREEN PAUWARE - For a power-thrifty mobile app marketplaceOlivier Le Goaër
Energy-intensive mobile applications are a burden for both end users and devel-opers, and ultimately are harmful to the planet. The objective of the GREEN PAUWARE project is to break these bad habits by creating an energy label for applications (ranging from A to G), as exists in other areas. Many authors agree on the necessity of an eco-score or ranking within the mobile apps market, but research is hampered by the complexity and lack of available tools.
This presentation proposes milestones to achieve this objective. In particular, it introduces an ecological bonus-malus system applied to Android development projects to try to give a score to applications. Then, it shows how to use a static code analysis tool like Android Lint to implement such a system. Finally, it features a distributed software architecture to collect scores and push labels towards end users so they can make informed decisions when installing applica-tions on their Android-powered devices.
Investigating Decreasing Energy Usage in Mobile Apps via Indistinguishable Co...MobileSoft
"Investigating Decreasing Energy Usage in Mobile Apps via Indistinguishable Color Changes" by Tedis Agolli, Lori Pollock, James Clause
MobileSoft'17 Buenos Aires, Argentina, 2017.
Leafactor: Improving Energy Efficiency of Android Apps via Automatic RefactoringMobileSoft
"Leafactor: Improving Energy Efficiency of Android Apps via Automatic Refactoring"
by Luis Cruz, Rui Abreu and Jean-Noel Rouvignac
MobileSoft'17, Buenos Aires, Argentina, 2017
Using Automatic Refactoring to Improve Energy Efficiency of Android AppsLuis Cruz
The ever-growing popularity of mobile phones has brought additional challenges to the software development lifecycle. Mobile applications (apps, for short) ought to provide the same set of features as conventional software, with limited resources: such as, limited processing capabilities, storage, screen and, not less important, power source. Although energy efficiency is a valuable requirement, developers often lack knowledge of best practices. In this paper, we study whether or not automatic refactoring can aid developers ship energy efficient apps. We leverage a tool, Leafactor, with five energy code smells that tend to go unnoticed. We use Leafactor to analyze code smells in 140 free and open source apps. As a result, we detected and fixed code smells in 45 apps, from which 40% have successfully merged our changes into the official repository.
[February 2017 - Ph.D. Final Dissertation] Enabling Power-awareness For Multi...Matteo Ferroni
Power consumption has become a major concern for almost every digital system: from the smallest embedded devices to the biggest data centers, energy and power budgets are always constraining the performance of the system. Moreover, the actual power consumption of these systems is strongly affected by their current “working regime” (e.g., from idle to heavy-load conditions, with all the shades in between), which depends on the guest applications they host, as well as on the external interactions these are subject to. It is then difficult to make accurate predictions on the power consumed by the whole system over time, when it is subject to constantly changing operating conditions: a self-aware and goal-oriented approach to resource allocation may then improve the instantaneous performance of the system, but still the definition of energy saving policies remains not trivial as far as the system is not really able to learn from experience in real world scenarios.
In this context, this thesis proposes a holistic power modeling framework that a wide range of energy and power constrained systems can use to profile their energy and power consumption. Starting from the preliminary experience developed on power consumption models for mobile devices during my M.Sc. thesis, I designed a general methodology that can be tailored on the actual system's features, extracting a specific power model able to describe and predict the future behavior of the observed entity. This methodology is meant to be provided in an “as-a-service” fashion: at first, the target system is instrumented to collect power metrics and workload statistics in its real usage context; then, the collected measurements are sent to a remote server, where data is processed using well known techniques (e.g., Principal Components Analysis, Markov Decision Chains, ARX models, etc.); finally, an accurate power model is built as a function of the metrics monitored on the instrumented system. The generalized approach has been validated in the context of power consumption models for multi-tenant virtualized infrastructures, outperforming results from the state of the art. Finally, the experience developed on power consumption models for server infrastructures led me to the design of a power-aware and QoS-aware orchestrator for multi-tenant systems. On the one hand, I propose a performance-aware power capping orchestrator in a virtualized environment, that aims at maximizing performance under a power cap. On the other hand, I bring the same concepts into a different approach to multi-tenancy, i.e., containerization, thus moving the first steps towards power-awareness for Docker containers orchestration, laying the basis for further research work.
Full thesis: https://www.politesi.polimi.it/handle/10589/132112
LAS16-307: Benchmarking Schedutil in AndroidLinaro
LAS16-307: Benchmarking Schedutil in Android
Speakers: Steve Muckle
Date: September 28, 2016
★ Session Description ★
Being able to see the performance and power impacts of changes in a real world environment such as Android is a prerequisite to doing meaningful development on scheduler-guided frequency (or many other sensitive subsystems). The first half of this session will review setting up the tools to automate testing for performance and power in Android. The second half will cover the results of using these tests to compare the schedutil and interactive governors.
★ Resources ★
Etherpad: pad.linaro.org/p/las16-307
Presentations & Videos: http://connect.linaro.org/resource/las16/las16-307/
★ Event Details ★
Linaro Connect Las Vegas 2016 – #LAS16
September 26-30, 2016
http://www.linaro.org
http://connect.linaro.org
GREEN PAUWARE - For a power-thrifty mobile app marketplaceOlivier Le Goaër
Energy-intensive mobile applications are a burden for both end users and devel-opers, and ultimately are harmful to the planet. The objective of the GREEN PAUWARE project is to break these bad habits by creating an energy label for applications (ranging from A to G), as exists in other areas. Many authors agree on the necessity of an eco-score or ranking within the mobile apps market, but research is hampered by the complexity and lack of available tools.
This presentation proposes milestones to achieve this objective. In particular, it introduces an ecological bonus-malus system applied to Android development projects to try to give a score to applications. Then, it shows how to use a static code analysis tool like Android Lint to implement such a system. Finally, it features a distributed software architecture to collect scores and push labels towards end users so they can make informed decisions when installing applica-tions on their Android-powered devices.
Altitude SF 2017: Granular, Precached, & Under BudgetFastly
New technologies like Service Workers and H/2 are making it possible to finally load code into our applications proportionate to what’s in view. These approaches require smarter frameworks and better tools, but enable us to once again write (roughly) what we send to users. Alex discusses the challenges and benefits of adopting these emerging approaches to app construction and delivery.
The road to "green code" is paved with good intentions, but few solutions are actionable to date. This talk gives the keys to understanding sustainable software development (mobile in particular) and shows how code quality control with SonarQube™ can help meet the challenge of global limits.
Samsung Developer's Conference - Maximize App Performance while Minimizing Ba...rickschwar
Trepn Profiler was recently showcased at the Samsung Developer's Conference in a session titled:
Maximize App Performance while Minimizing Battery Drain
You can view the full video of this event here: https://www.youtube.com/watch?v=SR_1WGD88Pw
Here is the outline of the entire session:
· 0:00:00 – Agenda – Rick Schwartz
· 0:01:32 – The challenge – Mobile trends
· 0:04:50 – Does your app consume too much power?
· 0:10:48 – Software power measurement Best Practices
· 0:14:17 – Demo: Using Trepn to profile your mobile processor
· 0:27:06 – Per-rail power measurements
· 0:30:48 – Demo: Power profiling in Eclipse
· 0:50:10 – How to efficiently use your cellular radio
· 0:54:50 – Trepn Profiler Deep dive – Eugene Kolinko
· 0:55:24 – How to insert markers in your code to identify power spikes
· 1:00:44 – How to: perform automated testing with Trepn Profiler
· 1:14:34 – Common causes of excessive power consumption – Rick Schwartz
· 1:21:44 – Recap of power saving tips
· 1:24:47 – Qualcomm Snapdragon Performance Visualizer overview – Kevin Sapp
· 1:29:59 – What can Snapdragon Performance Visualizer do?
· 1:35:25 – Live View demo
· 1:40:29 – Adding Custom Data to Snapdragon Performance Visualizer
· 1:47:42 – Statistical profiling with Snapdragon Performance Visualizer (OProfile)
· 1:50:44 – Tracing with Snapdragon Performance Visualizer
· 1:52:11 – Profile View demo
· 2:03:40 – Qualcomm Embedded Power Monitor demo
· 2:12:30 – Graphics and gaming overview – Manish Sirdeshmukh
· 2:27:08 – OpenGL ES optimizations - Dave Astle
· 2:33:53 – Adreno software tool and SDK overview – Manish Sirdeshmukh
· 2:38:04 – Adreno profile demo - Dave Astle
How to Lower Android Power Consumption Without Affecting Performancerickschwar
Most mobile apps waste power because they do not manage the processor, cellular radio and Wi-Fi network properly. Excess power consumption can lead to bad reviews and poor ratings. This session will teach you how to determine whether your app consumes too much power. You'll also learn how to resolve the most common power-related problems.
Make sure to watch the video that goes along with these slides. You can view it here: https://www.parleys.com/tutorial/how-lower-power-consumption-your-app-without-affecting-performance
Topics Discussed
• Why mobile power consumption has increased so much
• What are the top 5 power-related problems?
• How to determine how much power your app consumes when it’s idle and active
• How do you know if an app consumes too much power?
• How to quickly test an app’s performance in 25 key areas
• How to pinpoint the causes of power spikes in your code
• Why it's so important to manage the cellular radio effectively
• How to power and performance profile your app without leaving your IDE
• Using software to determine whether you are managing the cellular radio properly
• Best Practices for connectivity, performance and power measurement
• How much power can you save when your port code to run on a DSP?
• How to get early access to development smartphone and tablets with next generation mobile processors up to 6 months before they appear in commercial devices
• How to see where the power is going by measuring individual rails including CPU, GPU, display, memory, Wi-Fi, sensors and more
• An introduction to automated power testing and more.
About the Author
Rick is a senior product manager at Qualcomm. His team creates next-gen smartphones and tablets that are made available to software developers. He also manages Qualcomm’s power and performance profiling software.
#DV15 #BatteryOptimization #Android #perfmatters @mostlytech1
A Framework for Regression Testing of Outdoor Mobile ApplicationsMobileSoft
"A Framework for Regression Testing of Outdoor Mobile Applications"
by Carlo Bernashina Roman Fedorov Darian Frajberg Piero Fraternali.
MobileSoft'17, Buenos Aires, Argentina, 2017.
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Most mobile apps waste power because they do not manage the processor, cellular radio and Wi-Fi network properly. Excess power consumption can lead to bad reviews and poor ratings. This session will teach you how to determine whether your app consumes too much power. You'll also learn how to resolve the most common power-related problems.
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Topics Discussed
• Why mobile power consumption has increased so much
• What are the top 5 power-related problems?
• How to determine how much power your app consumes when it’s idle and active
• How do you know if an app consumes too much power?
• How to quickly test an app’s performance in 25 key areas
• How to pinpoint the causes of power spikes in your code
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• How to power and performance profile your app without leaving your IDE
• Using software to determine whether you are managing the cellular radio properly
• Best Practices for connectivity, performance and power measurement
• How much power can you save when your port code to run on a DSP?
• How to get early access to development smartphone and tablets with next generation mobile processors up to 6 months before they appear in commercial devices
• How to see where the power is going by measuring individual rails including CPU, GPU, display, memory, Wi-Fi, sensors and more
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Web: http://mobilesoftconf.org/2017/
FB: https://www.facebook.com/groups/MobilesoftConference/
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Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
2. Motivation
• Mobile and wearable devices
are very popular nowadays
• Users expect all-day battery
life on their devices
• Impact of energy
improvements in mobile
applications is hard to
measure and time consuming
2
4. Research Questions
RQ1: Can programming practices be blindly applied
in order to improve energy efficiency in an
Android application?
RQ2: Do best practices for performance improvement
also improve energy efficiency?
RQ3: Do these best practices actually have an impact
on real mature Android applications?
4
5. Research Questions
RQ1: Can programming practices be blindly applied
in order to improve energy efficiency in an
Android application?
RQ2: Do best practices for performance improvement
also improve energy efficiency?
RQ3: Do these best practices actually have an impact
on real mature Android applications?
5
6. Research Questions
RQ1: Can programming practices be blindly applied
in order to improve energy efficiency in an
Android application?
RQ2: Do best practices for performance improvement
also improve energy efficiency?
RQ3: Do these best practices actually have an impact
on real mature Android applications?
6
7. Methodology
A. Android application selection
B. Static analysis and refactoring
C. Generation of automatic UI tests
D. Energy measurement tools setup
E. Experiments execution
F. Data analysis
7
8. A. Android application
selection
• 6 open-source Apps available at Google Play and/or F-Droid
8
Writeily ProLoop - Habit Tracker Talalarmo
GnuCash Simple Gallery Acrylic Paint
9. B. Static analysis and
refactoring
• Android Lint to detect code smells
• 8 performance-based code smells were studied
• Each code smell was fixed and a new version of
the app was generatedTABLE II: Anti-patterns found in open source applications.
Anti-Pattern Loop - Habit Tracker Writeily Pro Talalarmo GnuCash Acrylic Paint Simple Gallery
DrawAllocation - - • - • -
WakeLock - - • - - -
Recycle - - - • - -
ObsoleteLayoutParam - - - • - -
ViewHolder - • - - - -
Overdraw • • - - • •
UnusedResources • • - - - -
UselessParent - • - - - -
9
10. C. Generation of automatic
UI tests
• Scripts to mimic user interaction
• Manually created using Android View Client
• Allows replication of experiments
10
12. D. Energy measurement
tools setup
• Power is an
instantaneous
measurement (watts)
• Energy is a
measurement of
power over a period
of time (joules)
13. E. Experiments execution
• For every fixed code
smell the experiment
was equally executed
30 times for statistical
validation
Controller
Computer
ODROID
Uninstall App &
remove user data
Install APK
Upload Energy
Logger script
Start Energy Logger
Return Energy
Logger PID
UI interaction
Open App
Return Energy Logs
Open App
Stop Energy Logger
(PID)
ADB through USB
13
14. F. Data analysis
• Power readings were down-sampled to 1 second
• Energy consumptions that differ 2 standard
deviations from the mean were eliminated
14
15. Significance Tests
15
Welch’s t-test results
UnusedResource,
and UselessParent
did not provide
significant results.
Fig. 9: Energy consumption for Simple Gallery.
TABLE IV: Significance Welch’s t-test results
Application Pattern Test p-value
Loop - Habit Tracker
Overdraw -0.56 .5784
UnusedResources -0.83 .4121
All -0.08 .9362
Writeily Pro
Overdraw -0.10 .9180
UnusedResources -0.03 .9790
ViewHolder 3.02 .0038
UselessParent 0.20 .8434
All 2.93 .0049
Talalarmo
DrawAllocation 4.18 .0001
WakeLock 4.43 < .0001
All 2.16 .0353
GnuCash
ObsoleteLayoutParam 2.57 .0127
Recycle 2.55 .0140
All 2.47 .0164
AcrylicPaint
DrawAllocation 0.64 .5221
Overdraw 45.88 < .0001
All -5.84 < .0001
Simple Gallery Overdraw -4.04 .0010
DrawAllocation also
Although it occurred in
Talalarmo, we observed
tion was also tested wi
statistically significant im
picker redraw routine. U
debug view updates, we
a single time when a ne
fix in the overall execu
been the reason for not
consumption.
Fixing incorrect Wake
ment of 1%. In the ori
lock was not being pro
to energy drain in part
application is no longer
was not properly releas
power state. This woul
given the nature of ou
effectively tested. Thus,
in a real case scenario.
ObsoleteLayoutParam
16. Effect SizeTABLE V: Effect size of significant patterns
Application Pattern MD Cohen’s d IMP (%) Savings (min)
Writeily Pro
ViewHolder # -5.39 -0.78 4.50 65
All # -5.42 -0.76 4.53 65
Talalarmo
DrawAllocation # -0.86 -1.11 1.47 21
WakeLock # -0.85 -1.17 1.46 21
All # -0.48 -0.57 0.82 12
GnuCash
ObsoleteLayoutParam # -1.41 -0.67 0.72 10
Recycle # -1.28 -0.66 0.65 9
All # -1.53 -0.64 0.78 11
Acrylic Paint
Overdraw " 1.42 1.64 -2.26 -33
All " 1.37 1.51 -2.18 -31
Simple Gallery Overdraw " 3.08 1.04 -2.11 -30
WakeLock usage also provided an improve-
original version of Talalarmo, the wake
properly released which could have lead
itself several times. In this case, having an
important, and fixing Overdraw is expected
ing results. On the other hand, if a view is be16
View Holder has the biggest impact 😀 while
Overdraw increased energy consumption 😕
17. Research Questions
RQ1: Can programming practices be blindly applied in order to improve
energy efficiency in an Android application?
Yes, apps had energy efficiency improved without changing the
feature set and without requiring previous knowledge of the app.
RQ2: Do best practices for performance improvement also improve energy
efficiency?
Not necessarily. While five optimizations improved energy
efficiency, two did not affect, and one had a negative impact.
RQ3: Do these best practices actually have an impact on real mature Android
applications?
Yes, three out of six real apps improved energy efficiency.
17
18. Conclusions & Future Work
• Anti-patterns ViewHolder, DrawAllocation,
WakeLock, ObsoleteLayoutParam, and Recycle have
to be considered when developing energy-efficient
apps
• Extend the study to other optimizations
• Automatic refactoring (Autorefactor, FB pfff, Walkmod,
Kadabra?)
• Label mobile applications with respect to energy
efficiency
18
19. Hypothesis
Performance based optimizations
can be used to ship energy efficient
Android applications.
3
Effect SizeTABLE V: Effect size of significant patterns
Application Pattern MD Cohen’s d IMP (%) Savings (min)
Writeily Pro
ViewHolder # -5.39 -0.78 4.50 65
All # -5.42 -0.76 4.53 65
Talalarmo
DrawAllocation # -0.86 -1.11 1.47 21
WakeLock # -0.85 -1.17 1.46 21
All # -0.48 -0.57 0.82 12
GnuCash
ObsoleteLayoutParam # -1.41 -0.67 0.72 10
Recycle # -1.28 -0.66 0.65 9
All # -1.53 -0.64 0.78 11
Acrylic Paint
Overdraw " 1.42 1.64 -2.26 -33
All " 1.37 1.51 -2.18 -31
Simple Gallery Overdraw " 3.08 1.04 -2.11 -30
WakeLock usage also provided an improve-
he original version of Talalarmo, the wake
g properly released which could have lead
itself several times. In this case, having an efficient redraw is
important, and fixing Overdraw is expected to create interest-
ing results. On the other hand, if a view is being created several16
View Holder has the biggest impact ! while
Overdraw increased energy consumption "
Research Questions
RQ1: Can programming practices be blindly applied in order to improve
energy efficiency in an Android application?
Yes, apps had energy efficiency improved without changing the
feature set and without requiring previous knowledge of the app.
RQ2: Do best practices for performance improvement also improve energy
efficiency?
Not necessarily. While five optimizations improved energy
efficiency, two did not affect, and one had a negative impact.
RQ3: Do these best practices actually have an impact on real mature Android
applications?
Yes, three out of six real apps improved energy efficiency.
17
19