Secure neural network inference (SNNI) enables the use of deep neural networks in scenarios involving multiple stakeholders, protecting the confidentiality of client data and of the neural network’s parameters. The cryptographic techniques used introduce high computational overhead, leading to significant energy consumption. Reducing the energy consumption of SNNI is thus an important objective. A prerequisite for energy optimization is the ability to profile the energy consumption of SNNI. However, this is challenging due to the complexity of the cryptographic techniques, neural networks, and technical setup involved. This paper is the first to propose an energy profiling approach for SNNI. Our approach measures the energy consumption for securely processing individual layers of the neural network, thus providing fine-grained insights into the energy profile of SNNI. We evaluate our approach using the ResNet50 neural network and the Cheetah SNNI framework. Our results show that we can reliably measure the energy consumption of individual layers. By introducing short periods of inactivity between layers to disentangle them, we achieve high correlation between execution time and energy consumption, suggesting that, under appropriate conditions, execution time may be used as a proxy for energy consumption. Our approach and insights can foster the design of more energy-efficient SNNI protocols.

Microservice-based systems are usually developed according to agile practices like DevOps, which enables rapid and frequent releases to promptly react and adapt to changes. Monitoring is a key enabler for these systems, as they allow to continuously get feedback from the field and support timely and tailored decisions for a quality-driven evolution. In the realm of monitoring tools available for microservices in the DevOps-driven development practice, each with different features, assumptions, and performance, selecting a suitable tool is an as much difficult as impactful task. This article presents the results of a systematic study of the grey literature we performed to identify, classify and analyze the available monitoring tools for DevOps and microservices. We selected and examined a list of 71 monitoring tools, drawing a map of their characteristics, limitations, assumptions, and open challenges, meant to be useful to both researchers and practitioners working in this area. Results are publicly available and replicable. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Context. WebAssembly was created as an alternative to JavaScript for developing heavy loading web applications. Since JavaScript is known to have long execution times. A lot of research is already performed to compare the run-time performance of WebAssembly against that of JavaScript. However, little research is available that compares the energy consumption of WebAssembly versus JavaScript. Goal. With this study we aim to identify the correlation between the energy consumption and the use of WebAssembly versus JavaScript. This will aid developers in deciding which method matches the needs of their project best in terms of energy efficiency. Method. The subjects of the experiment are WebAssembly and JavaScript. During the experiment two research questions are defined. For the first research question the programming language is the independent variable. For the second research question the web browser is the independent variable. For both research questions is the energy consumption of the Android device in Joules the dependent variable. Results. We can confirm that the energy consumption of WebAssembly is less than that of JavaScript. The browser also plays a role since the energy consumption of Firefox is significantly smaller than that of Chrome for both WebAssembly and JavaScript. Conclusions. This study provides evidence that using WebAssembly for the development of web applications can reduce the energy consumption and thus improve the battery life of a user’s Android device. Developers can use this information when choosing a programming language to develop a web application. Moreover, using Firefox over Chrome does also reduce the energy consumption of web applications developed both with WebAssembly and JavaScript.

Context. Due to the growing popularity of smartphones, mobile web browsing is more popular than ever with users desiring fast loading web apps and low energy usage. A technique that might improve the run-time performance and reduce the energy consumption of this action is the Critical CSS technique.

Context. Developers have access to tools like Google Lighthouse to assess the performance of web apps and to guide the adoption of development best practices. However, when it comes to energy consumption of mobile web apps, these tools seem to be lacking. Goal. This study investigates on the correlation between the performance scores produced by Lighthouse and the energy consumption of mobile web apps.