Although numerous hardware and software solutions exist for energy monitoring, they often require extensive setup, configuration, and technical expertise. This paper presents a preliminary study investigating the potential of widely available software traces to estimate software energy consumption. Experiments across synthetic and realistic workloads show that certain hardware metrics and structured software logs, such as SQL statements and Docker container logs, consistently correlate with energy usage, whereas unstructured logs, including general HTTP or Maven logs, exhibit less reliable correlations. These findings suggest that combining hardware metrics with structured software traces, while accounting for workload characteristics and application structure, offers a promising approach for estimating software energy consumption without dedicated energy monitoring tools.

With growing concerns about computing’s environmental impact, sustainable IT practices must engage all stakeholders—including end-users. This study investigates how user behavior influences software energy consumption through a mixed-methods approach: a campus survey (N=280) revealed that while 91.5% understand device energy use, only 55.3% recognize software’s role, and just 46.8% modify habits despite 79.3% acknowledging individual responsibility. Follow-up interviews highlighted demographic divides, with older (25-64) and advanced-degree users showing greater awareness. A week-long field experiment at an IT company demonstrates that real-time feedback—especially disaggregated data showing top energy-consuming apps—shifted perceptions, reducing hardware’s perceived influence by 29%. Though users adopted energy-saving actions (e.g., closing idle apps, switching to lightweight software), workplace priorities often limited sustained changes. Our findings prove user-centric interventions can raise awareness, but highlight the need for context-sensitive designs that balance energy efficiency with workflow demands. This work establishes actionable pathways to reduce ICT’s carbon footprint through behavior-aware software ecosystems.

GoF’s design patterns are popular structures designed for flexible and reusable object-oriented software. However, their energy impact is not well understood, with contradictory existing results. In this paper, we aim to answer the relation between design patterns and energy consumption through a multi-platform and multi-software stack empirical study, covering all design patterns, and multiple hardware, OS, languages and compilers. We found, with a high confidence level, that none of the GoF’s design patterns has a significant impact on energy consumption. Therefore, they can be considered energy-neutral, allowing eco-friendly developers to continue using them. We argue that software energy efficiency should rather be considered within the business logic embedded by a design pattern, and ultimately raised to a higher perspective of software development.

The rise of the energy impact of software systems requires the need to optimize and reduce their energy consumption. One area often neglected is the important role played by users to drive energy reductions. In this paper, we aim to reduce the energy impact of software by pushing end users to change their software usage behavior, through raising awareness and providing software green feedback. We present a comprehensive and detailed field study of the impact of green feedback on software usage by end users, and the efficiency of green feedback on software behavioral change, using a distributed architecture aimed at providing accurate green feedback in real time. We find that green feedback helps in raising awareness about software energy, and on the willingness of users to apply energy-efficient changes. However, we also find that users lack the knowledge and tools to properly adopt lasting and energy-effective behavioral changes.

Monitoring the power consumption of applications and source code is an important step in writing green software. In this paper, we propose PowerJoular and JoularJX, our software power monitoring tools. We aim to help software developers in understanding and analyzing the power consumption of their programs, and help system administrators and automated tools in monitoring the power consumption of large numbers of heterogeneous devices.

Nowadays, the use of IoT devices is essential in most sectors along with a rising concern regarding their energy consumption. Therefore, an accurate estimation of the energy consumption of such devices is required for energy-efficient improvements. This paper presents Energy Measurement Model (EMM), an energy estimation tool for Raspberry Pi 3 Model B+. It is a software-based model used to estimate the local energy consumption of a device by taking into consideration CPU utilization. The error rate of our model averages at 1.25%. Using this model, we study the energy impact of a set of algorithms, programming languages and compilers.

With the rising energy demand in ICT services and its associated environmental impact, the need for energy efficient Enterprise ICT solutions is growing. As data centres account for a large part of energy consumption in ICT, data centre operators strive to create opportunities to put more emphasis on reducing energy consumption. However, creating ICT Systems that are energy efficient by design remains a key challenge. In this paper, we identify and map contextual energy information about data centre operations in order to model their power related components. This contextual modelling is then mapped to deployment diagram where we introduce greenUML (gUML), an extension to UML diagrams to improve energy efficiency through energy analysis at design time. gUML will allow system architects to reason about the energy footprint of their applications at design time.

Software energy consumption has emerged as a growing concern in recent years. Managing the energy consumed by a software is, however, a difficult challenge due to the large number of factors affecting it -- namely, features of the processor, memory, cache, and other hardware components, characteristics of the program and the workload running, OS routines, compiler optimisations, among others. In this paper we study the relevance of numerous architectural and program features (static and dynamic) to the energy consumed by software. The motivation behind the study is to gain an understanding of the features affecting software energy and to provide recommendations on features to optimise for energy efficiency.

Monitoring energy consumption of applications is crucial for energy optimisation and improvements in software systems. With the recent emphasis on energy efficiency, it is vital that software engineers have an understanding of the energy consumed by the code they write. In this paper, we present Jolinar, a tool that bridges the gap between energy measurements and accessibility to software engineers and even end-users. The tool builds on top of recent energy models to provide an accurate, light and easy-to-use interface for energy measurements. The target audience of Jolinar is both software engineers and non-technical end-users who want to monitor their applications' energy footprint. We show that end-users can use Jolinar's GUI to determine the energy consumed by the software they are using, and software engineers can use the tool to analyse energy consumption of systems to make energy-conscious decisions.

With the proliferation of Software systems and the rise of paradigms such the Internet of Things, Cyber-Physical Systems and Smart Cities to name a few, the energy consumed by software applications is emerging as a major concern. Hence, it has become vital that software engineers have a better understanding of the energy consumed by the code they write. At software level, work so far has focused on measuring the energy consumption at function and application level. In this paper, we propose a novel approach to measure energy consumption at a feature level, cross-cutting multiple functions, classes and systems. We argue the importance of such measurement and the new insight it provides to non-traditional stakeholders such as service providers. We then demonstrate, using an experiment, how the measurement can be done with a combination of tools, namely our program slicing tool (PORBS) and energy measurement tool (Jolinar).

Variability management is one of the main activities in the Software Product Line Engineering process. Common and varied features of related products are modelled along with the dependencies and relationships among them. With the increase in size and complexity of product lines and the more holistic systems approach to the design process, managing the ever-growing variability models has become a challenge. In this paper, we present MUSA, a tool for managing variability and features in large-scale models. MUSA adopts the Separation of Concerns design principle by providing multiple perspectives to the model, each conveying different set of information. The demonstration is conducted using a real-life model (comprising of 1000+ features) particularly showing the Structural View, which is displayed using a mind-mapping visualisation technique (hyperbolic trees), and the Dependency View, which is displayed graphically using logic gates.

Software design patterns are widely used in software engineering to enhance productivity and maintainability.However, recent empirical studies revealed the high energy overhead in these patterns. Our vision is to automatically detect and transform design patterns during compilation for better energy efficiency without impacting existing coding practices. In this paper, we propose compiler transformations for two design patterns, Observer and Decorator, and perform an initial evaluation of their energy efficiency.

SUMMARY Energy management solutions and approaches for computer systems are becoming broadly available as energy concerns are becoming mainstream. Many approaches have been proposed to manage the energy consumption of the hardware, operating system, or software layers. The widespread usage of ubiquitous devices and the high coverage of networks (Wi‐Fi and 3G) have led to a new generation of communicating and mobile devices that uses complex middleware platform functionalities. Therefore, energy management has emerged as a topic of research interest in the middleware layer, and solutions specific to this layer are proposed along the more traditional ones existing at the other levels. In this article, we report on a review of state‐of‐the‐art approaches for energy management middleware platforms. This article defines also an architectural taxonomy and compares existing approaches on the basis of this taxonomy. In particular, we review middleware platforms and detail a number of approaches where energy management is handled. Finally, we review application scenarios where the energy management concepts at the middleware layer are applied in intelligent environments. Copyright © 2012 John Wiley & Sons, Ltd.

GreenIT has emerged as a discipline concerned with the optimization of software solutions with regards to their energy consumption. In this domain, most of the state-of-the-art solutions concentrate on coarse-grained approaches to monitor the energy consumption of a device or a process. However, none of the existing solutions addresses in-process energy monitoring to provide in-depth analysis of a process energy consumption. In this paper, we therefore report on a fine-grained runtime energy monitoring framework we developed to help developers to diagnose energy hotspots with a better accuracy than the state-of-the-art.