Shaiful Alam Chowdhury

GreenOracle: estimating software energy consumption with energy measurement corpora

Software energy consumption is a relatively new concern for mobile application developers. Poor energy performance can harm adoption and sales of applications. Unfortunately for the developers, the measurement of software energy con-sumption is expensive in terms of hardware and difficult in terms of expertise. Many prior models of software energy consumption assume that developers can use hardware instrumentation and thus cannot evaluate software runningwithin emulators or virtual machines. Some prior modelsrequire actual energy measurements from the previous versions of applications in order to model the energy consumption of later versions of the same application.In this paper, we take a big-data approach to software energy consumption and present a model that can estimate software energy consumption mostly within 10% error (in joules) and does not require the developer to train on energy measurements of their own applications. This model leverages a big-data approach whereby a collection of prior applications’ energy measurements allows us to train, trans-mit, and apply the model to estimate any foreign application’s energy consumption for a test run. Our model is based on the dynamic traces of system calls and CPU utilization.

Client-Side Energy Efficiency of HTTP/2 for Web and Mobile App Developers

Recent technological advancements have enabled mobile devices to provide mobile users with substantial capability and accessibility. Energy is evidently one of the most critical resources for such devices, in spite of the substantial gain in popularity of mobile devices, such as smart phones, their utility is severely constrained by the bounded battery capacity. Mobile users are very interested in accessing the Internet although it is one of the most expensive operations in terms of energy and cost. HTTP/2 has been proposed and accepted as the new standard for supporting the World Wide Web. HTTP/2 is expected to offer better performance, such as reduced page load time. Consequently, from the mobile users point of view, the question arises:does HTTP/2 offer improved energy consumption performance achieving longer battery life?In this paper, we compare the energy consumption of HTTP/2 with its predecessor (i.e., HTTP/1.1) using a variety of realworld and synthetic test scenarios. We also investigate how Transport Layer Security (TLS) impacts the energy consumption of the mobile devices. Our study suggests that Round Trip Time (RTT) is one of the biggest factors in deciding how advantageous HTTP/2 is compared to HTTP/1.1. We conclude that for networks with higher RTTs, HTTP/2 has better energy consumption performance than HTTP/1.1.

Characterizing energy-aware software projects: are they different?

The improvement in battery technology for battery-driven devices is insignificant compared to their computing ability. In spite of the overwhelming advances in processing ability, adoption of sophisticated applications is hindered bythe fear of shorter battery life. This is one of the several reasons software developers are becoming conscious of writing energy efficient code.Research has been conducted to model software energyconsumption, to reduce energy drains, and to understanddevelopers expertise on energy efficiency. In this paper,however, we investigate the nature of energy-aware softwareprojects. We observed that projects concerned with energyissues are larger and more popular than the projects thatdo not address energy consumption. Energy related codechanges are larger than others (e.g., bug fixes). In addition,our initial results suggest that energy efficiency is mostlyaddressed on certain platforms and applications.

Is HTTP/2 More Energy Efficient Than HTTP/1.1 for Mobile Users?

Recent technological advancements have enabled mobile devices to provide mobile users with substantial capability and accessibility. Energy is evidently one of the most critical resources for such devices; in spite of the substantial gain in popularity of mobile devices, such as smartphones, their utility is severely constrained by battery life. Mobile users are very interested in accessing the Internet while it is one of the most expensive operations in terms of energy and cost. HTTP/2 has been proposed and accepted as the new standard for supporting the World Wide Web. HTTP/2 is expected to offer better performance, such as reduced page load time. Consequently, from the mobile users point of view, question arises: Does HTTP/2 offer improved energy consumption performance achieving longer battery life? In this paper, we compare the energy consumption of HTTP/2 with its predecessor (i.e., HTTP/1.1) using a variety of real world and synthetic test scenarios. We also investigate how Transport Layer Security (TLS) impacts the energy consumption of the mobile devices. Our study suggests that Round Trip Time (RTT) is one of the biggest factors in deciding how advantageous is HTTP/2 compared to HTTP/1.1. We conclude that for networks with higher RTTs, HTTP/2 has better energy consumption performance than HTTP/1.1.

Mining StackOverflow to filter out off-topic IRC discussion

Internet Relay Chat (IRC) is a commonly used tool by Open Source developers. Developers use IRC channels to discuss programming related problems, but much of the discussion is irrelevant and off-topic. Essentially if we treat IRC discussions like email messages, and apply spam filtering, we can try to filter out the spam (the off-topic discussions) from the ham (the programming discussions). Yet we need labelled data that unfortunately takes time to curate. To avoid costly cur ration in order to filter out off-topic discussions, we need positive and negative data-sources. On-line discussion forums, such as Stack Overflow, are very effective for solving programming problems. By engaging in open-data, Stack Overflow data becomes a powerful source of labelled text regarding programming. This work shows that we can train classifiers using Stack Overflow posts as positive examples of on-topic programming discussion. You Tube video comments, notorious for their lack of quality, serve as training set of off-topic discussion. By exploiting these datasets, accurate classifiers can be built, tested and evaluated that require very little effort for end-users to deploy and exploit.

An exploratory study on assessing the energy impact of logging on Android applications

Execution logs are debug statements that developers insert into their code. Execution logs are used widely to monitor and diagnose the health of software applications. However, logging comes with costs, as it uses computing resources and can have an impact on an application’s performance. Compared with desktop applications, one additional critical computing resource for mobile applications is battery power. Mobile application developers want to deploy energy efficient applications to end users while still maintaining the ability to monitor. Unfortunately, there is no previous work that study the energy impact of logging within mobile applications. This exploratory study investigates the energy cost of logging in Android applications using GreenMiner, an automated energy test-bed for mobile applications. Around 1000 versions from 24 Android applications (e.g., Calculator, FeedEx, Firefox, and VLC) were tested with logging enabled and disabled. To further investigate the energy impacting factors for logging, controlled experiments on a synthetic application were performed. Each test was conducted multiple times to ensure rigorous measurement. Our study found that although there is little to no energy impact when logging is enabled for most versions of the studied applications, about 79% (19/24) of the studied applications have at least one version that exhibit medium to large effect sizes in energy consumption when enabling and disabling logging. To further assess the energy impact of logging, we have conducted a controlled experiment with a synthetic application. We found that the rate of logging and the number of disk flushes are significant factors of energy consumption attributable to logging. Finally, we have examined the relation between the generated OS level execution logs and mobile energy consumption. In addition to the common cross-application log events relevant to garbage collection and graphics systems, some mobile applications also have workload-specific log events that are highly correlated with energy consumption. The regression models built with common log events show mixed performance. Mobile application developers do not need to worry about conservative logging (e.g., logs generated at rates of ≤ 1 message per second), as they are not likely to impact energy consumption. Logging has a negligible effect on energy consumption for most of the mobile applications tested. Although logs have been used effectively to diagnose and debug functional problems, it is still an open problem on how to leverage software instrumentation to debug energy problems.

Deep Green: Modelling Time-Series of Software Energy Consumption

Inefficient mobile software kills battery life. Yet, developers lack the tools necessary to detect and solve energy bugs in software. In addition, developers are usually tasked with the creation of software features and triaging existing bugs. This means that most developers do not have the time or resources to research, build, or employ energy debugging tools.We present a new method for predicting software energy consumption to help debug software energy issues. Our approach enables developers to align traces of software behavior with traces of software energy consumption. This allows developers to match run-time energy hot spots to the corresponding execution. We accomplish this by applying recent neural network models to predict time series of energy consumption given a softwares behavior. We compare our time series models to prior state-of-the-art models that only predict total software energy consumption. We found that machine learning based time series based models, and LSTM based time series based models, can often be more accurate at predicting instantaneous power use and total energy consumption.

GreenScaler: training software energy models with automatic test generation

Software energy consumption is a performance related non-functional requirement that complicates building software on mobile devices today. Energy hogging applications (apps) are a liability to both the end-user and software developer. Measuring software energy consumption is non-trivial, requiring both equipment and expertise, yet researchers have found that software energy consumption can be modelled. Prior works have hinted that with more energy measurement data we can make more accurate energy models. This data, however, was expensive to extract because it required energy measurement of running test cases (rare) or time consuming manually written tests. In this paper, we show that automatic random test generation with resource-utilization heuristics can be used successfully to build accurate software energy consumption models. Code coverage, although well-known as a heuristic for generating and selecting tests in traditional software testing, performs poorly at selecting energy hungry tests. We propose an accurate software energy model, GreenScaler, that is built on random tests with CPU-utilization as the test selection heuristic. GreenScaler not only accurately estimates energy consumption for randomly generated tests, but also for meaningful developer written tests. Also, the produced models are very accurate in detecting energy regressions between versions of the same app. This is directly helpful for the app developers who want to know if a change in the source code, for example, is harmful for the total energy consumption. We also show that developers can use GreenScaler to select the most energy efficient API when multiple APIs are available for solving the same problem. Researchers can also use our test generation methodology to further study how to build more accurate software energy models.

Did I make a mistake? Finding the impact of code change on energy regression.

Software energy consumption is a performance related non-functional requirement that complicates building software on mobile devices today. Energy hogging applications are a liability to both the end-user and software developer. Measuring software energy consumption is non-trivial, requiring both equipment and expertise, yet many researchers have found that software energy consumption can be modelled. Prior works have hinted that with more energy measurement data one can make more accurate energy models but this data was expensive to extract because it required energy measurement of running test cases (rare) or time consuming manually written tests. We address these concerns by automatically generating test cases to drive applications undergoing energy measurement. Automatic test generation allows a model to be continuously improved in a model building process whereby applications are extracted, tests are generated, energy is measured and combined with instrumentation to train a grander big-data model of software energy consumption. This continuous process has allowed the authors to generate and extract measurements from hundreds of applications in order to build accurate energy models capable of predicting the energy consumption of applications without end-user energy measurement. We clearly show that models built from more applications reduce energy modelling error.