This paper presents a detailed carbon-centered Life Cycle Analysis (LCA) of a video streaming device designed for long-term operation. We chose a development board with a screen to have full control on its operation, for which we have detailed information on its components allowing us to accurately model emissions from the production to the use of the device. Concerning its use, we perform a dedicated measurement series to determine the actual power consumption in different video playback scenarios and develop a linear power estimation model, which we use to evaluate different usage scenarios. The resulting LCA indicates that potential savings are highest when exploiting low-power sleep modes or switching off the device during its lifetime. When operating the device in a country with low carbon intensity, production and usage show similar emissions, while in countries with medium to high carbon emissions, the usage of the device causes significantly higher emissions.

With the escalating concern about global warming, the environmental impact of electronic devices must be scru-tinized. Life Cycle Assessments (LCA) reveal that Integrated Circuits (ICs) are the primary contributors to greenhouse gas emissions in these devices. However, performing an inventory to determine the ICs impact is a complex task due to missing data and the existing studies on ICs have been neglecting CMOS Image Sensors (CIS). Despite the surge in CIS usage, particularly in smartphones, there is a lack of comprehensive models to assess their en-vironmental impact. This paper proposes a multi-level set of models that leverage available information while considering the specificities of CIS. The most comprehensive model incorporates factors such as the total silicon area, geographical location (influencing the energy mix), and the technology node. To accommodate scenarios with incomplete data, subsequent models are designed to effectively utilize averaged parameters. The proposed models are applied to sensors manufactured by STMicroelectronics and Sony, and the results are compared with existing LCA results from Fairphone. Our approach provides a more comprehensive understanding of the environmental impact of CIS, contributing to the broader goal of reducing the carbon footprint of electronic devices. Our results suggest that the carbon impact of a Fairphone 4 image sensor is likely higher than previously estimated, with a significant gap between our findings and the expected value.

Serverless is a trending service model for cloud computing. It shifts a lot of the complexity from customers to service providers. However, current serverless platforms mostly consider the provider's infrastructure as homogeneous, as well as the users' requests. This limits possibilities for the provider to leverage heterogeneity in their infrastructure to improve function response time and reduce energy consumption. We propose a heterogeneity-aware serverless orchestrator for private clouds that consists of two components: the autoscaler allocates heterogeneous hardware resources (CPUs, GPUs, FPGAs) for function replicas, while the scheduler maps function executions to these replicas. Our objective is to guarantee function response time, while enabling the provider to reduce resource usage and energy consumption. This work considers a case study for a deepfake detection application relying on CNN inference. We devised a simulation environment that implements our model and a baseline Knative orchestrator, and evaluated both policies with regard to consolidation of tasks, energy consumption and SLA penalties. Experimental results show that our platform yields substantial gains for all those metrics, with an average of 35% less energy consumed for function executions while consolidating tasks on less than 40% of the infrastructure's nodes, and more than 60% less SLA violations.