An Economic Energy Approach For Queries On Data Centers – João Saraiva, Miguel Guimarães, Orlando Belo

Energy consumption is an issue that involves all of us, both as individuals and as members of a society, and covers all our areas of activity. It is something so broad that its impact has important reflections on our social, cultural and financial structures. The domain of software, and in particular database systems, is not an exception. Although it seems to be a little bit strange to study the energy consumption of just one query, when we consider the execution of a a few thousand queries per second, quickly we see the importance of the querying consumption in the monthly account of any company that has a conventional data center.

To demonstrate the energy consumption of queries in data centers, we idealized a small dashboard for monitoring and analyzing the sales of a company, and implemented all the queries needed for populating it and ensuring its operation. The queries were organized into two groups, oriented especially to two distinct database management systems: one relational (MySQL) and one non relational (Neo4J).

The goal is to evaluate the energy consumption of different types of queries, and at the same time compare it in terms of relational and non-relational database approaches. This paper relates the process we implemented to set up the energy consumption application scenario, measure the energy consumption of each query, and present our first preliminary results.

Leafactor: Improving Energy Efficiency of Android Apps via Automatic Refactoring – Luis Cru, Rui Abreu and Jean-Nöel Rouvignac

Leafactor is a tool to automatically improve the energy consumption of Android apps. It does so by refactoring the source code to follow a set of patterns known to be energy efficient. The toolset was validated using 222 refactorings in 140 open-source apps. Changes were submitted to the original apps by creating pull requests to the official projects.

Performance-based Guidelines for Energy Efficient Mobile Applications – Luís Cruz and Rui Abreu

Mobile and wearable devices are nowadays the de facto personal computers, while desktop computers are becoming less popular. Therefore, it is important for companies to deliver efficient mobile applications. As an example, Google has published a set of best practices to optimize the performance of Android applications. However, these guidelines fall short to address energy consumption. As mobile software applications operate in resource-constrained environments, guidelines to build energy efficient applications are of utmost importance. In this paper, we studied whether or not eight best performance-based practices have an impact on the energy consumed by Android applications. In an experimental study with six popular mobile applications, we observed that the battery of the mobile device can last up to approximately an extra hour if the applications are developed with energy-aware practices. This work paves the way for a set of guidelines for energy-aware automatic refactoring techniques.

Invited paper for the Poster Track at ICSE 2017.

Helping Programmers Improve the Energy Efficiency of Source Code – Rui Pereira, Tiago Carção, Marco Couto, Jácome Cunha, João Paulo Fernandes, João Saraiva

This paper briefly proposes a technique to detect energy inefficient fragments in the source code of a software system. Test cases are executed to obtain energy consumption measurements, and a statistical method, based on spectrum-based fault localization, is introduced to relate energy consumption to the system’s source code. The result of our technique is an energy ranking of source code fragments pointing developers to possible energy leaks in their code.

The Influence of the Java Collection Framework on Overall Energy Consumption – Rui Pereira, Marco Couto, Jácome Cunha, João Paulo Fernandes and João Saraiva

This paper presents a detailed study of the energy consumption of the different Java Collection Framework (JFC) implementations. For each method of an implementation in this framework, we present its energy consumption when handling different amounts of data. Knowing the greenest methods for each implementation, we present an energy optimization approach for Java programs: based on calls to JFC methods in the source code of a program, we select the greenest implementation. Finally, we present preliminary results of optimizing a set of Java programs where we obtained 6.2% energy savings.

In 5th International Workshop on Green and Sustainable Software (GREENS) [PDF] [Bib]

Haskell in Green Land: Analyzing the Energy Behavior of a Purely Functional Language – Luís Gabriel Lima, Gilberto Melfe, Paulo Lieuthier, Francisco Soares-Neto, Fernando Castor and João Paulo Fernandes

Recent work has studied the effect that factors such as code obfuscation, refactorings and data types have on energy efficiency. In this paper, we attempt to shed light on the energy behavior of programs written in a lazy purely functional language, Haskell. We have conducted two empirical studies to analyze the energy efficiency of Haskell programs from two different perspectives: strictness and concurrency. Our experimental space exploration comprises more than 2000 configurations and 20000 executions.

We found out that small changes can make a big difference in terms of energy consumption. For example, in one of our benchmarks, under a specific configuration, choosing one data sharing primitive (MVar) over another (TMVar) can yield 60% energy savings. In another benchmark, the latter primitive can yield up to 30% energy savings over the former. Thus, tools that support developers in quickly refactoring a program to switch between different primitives can be of great help if energy is a concern. In addition, the relationship between energy consumption and performance is not always clear. In sequential benchmarks, high performance is an accurate proxy for low energy consumption. However, for one of our concurrent benchmarks, the variants with the best performance also exhibited the worst energy consumption. To support developers in better understanding this complex relationship, we have extended two existing performance analysis tools to also collect and present data about energy consumption.

In IEEE International Conference on Software Analysis, Evolution, and Reengineering [PDF] [Bib]