GreenMiner: a hardware based mining software repositories software energy consumption framework

Hindle, Abram; Wilson, Alex; Rasmussen, Kent; Barlow, E. Jed; Campbell, Joshua Charles; Romansky, Stephen

doi:10.1145/2597073.2597097

Cited by 127 publications

(103 citation statements)

References 21 publications

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“…If so how should one normalize it [3], [6]? Zhang [3] poses a method of normalizing across platform with linear scaling -this technique is used by the Green-Miner [6] to normalize measurements from different smart-phones under test. Our models should address the hardware dependent performance versus the hardware invariant performance.…”

Section: A Ranking Applications By Energy Efficiencymentioning

confidence: 99%

Green software engineering: the curse of methodology

Hindle

2015

Preprint

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Computer Science often seems distant from its natural science cousins, especially software engineering which feels closer to sociology and psychology than to physics. Physical measurements are often rare in software engineering, except in a few niches. One such important niche is that of software energy consumption, green mining, green IT, and sustainable computing, which all fall under the umbrella of green software engineering.With the physical measurement of energy consumption comes all of the limitations of measurement and experimentation that exist in the natural sciences and engineering.Issues abound, from attribution of energy use, isolation of components, to replicable experiments. These get further complicated by cloud computing whereby systems are virtualized and attribution of resource usage is a serious issue. Thus in this work we discuss the current state of software energy consumption, and where will it go. Abstract-Computer Science often seems distant from its natural science cousins, especially software engineering which feels closer to sociology and psychology than to physics. Physical measurements are often rare in software engineering, except in a few niches. One such important niche is that of software energy consumption, green mining, green IT, and sustainable computing, which all fall under the umbrella of green software engineering.With the physical measurement of energy consumption comes all of the limitations of measurement and experimentation that exist in the natural sciences and engineering. Issues abound, from attribution of energy use, isolation of components, to replicable experiments. These get further complicated by cloud computing whereby systems are virtualized and attribution of resource usage is a serious issue.Thus in this work we discuss the current state of software energy consumption, and where will it go.

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Section: A Ranking Applications By Energy Efficiencymentioning

confidence: 99%

Green software engineering: the curse of methodology

Hindle

2015

Preprint

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“…The earliest work on energy consumption focused on modeling, monitoring, and improving energy consumption at the hardware level (e.g., [2], a detailed survey of these works is presented in Hindle [9], [10]). Later on, focus shifted to techniques to model, monitor, and improve energy consumption at the level of the operating system and applications.…”

Section: Related Workmentioning

confidence: 99%

On the impact of sampling frequency on software energy measurements

Saborido

Arnaoudova

Beltrame

et al. 2015

Preprint

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Abstract-Energy consumption is a major concern when developing and evolving mobile applications. The user wishes to access fast and powerful mobile applications, which is usually in contrast to optimized battery life and heat generation. The software engineering community have acknowledged the relevance of the problem and researchers are investigating ways to reduce energy consumption, for example by examining which library, device configuration, and applications parameters should be used to promote long battery life. We conjecture that these studies are at the border between hardware and software and we must be careful on how the energy consumption is measured and how the energy consumption is attributed to methods and libraries.To the best of our knowledge, no previous work investigates how much energy and power consumption is due to high frequency events missed when sampling at low frequencies such as 10 kHz and verified the error at the precision of method level. Low frequency sampling is a rough approximation that hinders the understanding of fine grain details: the real picture of energy consumption as well as the root causes are missed. This has profound implications on the choice of methods to evolve or components to replace. In this paper, we propose an approach for accurate measurements of the energy consumption of mobile applications. We apply the proposed approach to assess the energy consumption of 21 mobile, closed source, applications and four open source Android applications.We show that by sampling at 10 kHz one may expect a median error of 8%, however, such error may be as high as 50% for short fast executing methods. Finally, we revisit a previous approach that estimates the energy consumption of methods based on execution time and found that it can miss as much as 84% of the energy, with a median of 30%.

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“…In order to run and capture the energy consumption profiles of implementations of these protocols, the Green Miner test bed [32] was used. Green Miner-a continuous testing framework similar to a continuous integration framework but with a focus on energy consumption testing-consists of five major components: YiHua YH-305D power supply (phone power supply), Raspberry Pi model B computer (test monitoring), Arduino Uno (energy measurement), Adafruit INA219 breakout board (energy measurement), and four Galaxy Nexus phones (systems under test).…”

Section: Green Minermentioning

confidence: 99%

“…The phones were connected to a WPA secured wireless N network located in the same room, and thus ensuring very low variability of Internet access in order to have reliable measurements for our test scripts. Interested readers are encouraged to study review relevant Green Miner literature [32,48].…”

Section: Green Minermentioning

confidence: 99%

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Is HTTP/2 more energy efficient than HTTP/1.1 for mobile users?

Chowdhury

Sapra

Hindle

2015

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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.

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GreenMiner: a hardware based mining software repositories software energy consumption framework

Cited by 127 publications

References 21 publications

Green software engineering: the curse of methodology

Green software engineering: the curse of methodology

On the impact of sampling frequency on software energy measurements

Is HTTP/2 more energy efficient than HTTP/1.1 for mobile users?

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