Data centers have major environmental impacts due to their energy consumption and the manufacturing of equipment. They emit greenhouse gases and consume energy and resources, such as rare earth and water. Efficient computing resource management is therefore a key challenge for Cloud service providers today as they need to meet a growing demand while limiting the oversizing of their infrastructures. Mechanisms derived from virtualization, such as Virtual Machines (VMs) consolidation, are used to optimize resource management and infrastructure sizing, but economic and technical constraints can hinder their adoption. They require prior infrastructure knowledge and usage study to evaluate their potential, involve complex placement algorithms, and are sometimes difficult to implement in hypervisors. In this paper, we propose ORCA (OuR Consolidation Algorithm), a complete consolidation methodology designed to facilitate the production implementation of such mechanisms. This methodology includes the study of VM usage, the use of prediction models, and a VM placement algorithm that takes advantage of resource oversubscription. The choice of relevant oversubscription ratios is also addressed, with a focus on memory overcommitment through the study of memory overcommitment mechanisms:ballooning, page sharing, and swapping. Results from a detailed simulation process and deployment on a production infrastructure are presented. The methodology is tested in simulation on two production infrastructure datasets, with power consumption reduction as high as 29.8% and without consolidation error. The production deployment using VMWare vSphere and considering fault tolerance requirements reduces the energy consumption by 6.12% without causing any performance degradation.

Computing providers offer flexible, scalable and heterogeneous resources. However, their usage often leads to energy waste due to bad user choices. During a job submission, users may choose a cluster more powerful than needed for workloads, or leave the choice to scheduler. In both cases, the lack of guidance can lead to unnecessary energy consumption. We study this problem by evaluating energy gains achieved by a recommendation system that assists users with more energy efficient cluster choices. We analyze historical workloads from a resource provider to extract consumption and performance patterns. Using these profiles, we recommend clusters that can run similar jobs more efficiently. To evaluate the accuracy of recommendations, we execute evaluation workloads. Workloads are first executed on a cluster chosen by job scheduler, then submitted to the system to obtain a recommended cluster, and finally executed on the recommended cluster and all other clusters of the computing provider. Results show that the clusters recommended by the system achieve an average relative position score of 0.97, where 0 corresponds to the worst observed cluster for a workload, and 1 to the best.

In an effort to raise awareness on the increasing carbon emissions of Cloud computing, the European Corporate Sustainability Reporting Directive effectively requires providers to supply their customers with an assessment of the carbon impact associated with their use. This represents a challenge for bare metal servers, where the deployment of dedicated power meters is often unfeasible at scale. To address this, we present PPEM- BM, a novel sensor-driven modeling approach to estimate the power consumption of bare metal servers using CPU temperature data acquired via IPMI. PPEM-BM enhances and generalizes the existing POWERHEAT method, which correlates CPU temperature with power. Our methodology involves training individual power models, performing cross-evaluation to determine their portability, and then using a Learning to Rank (LTR) model to select the most appropriate pre-trained model for a target server based on its hardware configuration and CPU temperature statistics. An experiment conducted on 1,076 production servers at OVHcloud shows that PPEM-BM demonstrates a significant improvement compared to models based solely on hardware profiles. The approach offers a practical, scalable, and cost-effective solution for hosting providers to monitor energy consumption without widespread sensor deployment.

Data centers are very energy-intensive facilities whose power provision is challenging and constrained by power bounds. In modern data centers, servers account for a significant portion of the total power consumption. In this context, the ability to limit the instant power consumption of an individual computing node is an important requirement. There are several energy and power capping techniques that can be used to limit compute node power consumption, such as Intel RAPL. Although it is nowadays mainly utilized for energy measurement, Intel RAPL (Running Average Power Limit) was originally designed for power limitation purposes. Some works use Intel RAPL for power limitation in a limited context without full knowledge of the inner workings of this technology and what is done behind the scenes to enforce the power constraint. Furthermore, Intel has not revealed any details about its internal implementation. It is unclear exactly how Intel RAPL technology operates and what effects it has on application performance and power consumption. In this work, we conduct a thorough analysis of Intel RAPL technology as a power capping leverage on a variety of heterogeneous nodes for a selection of CPU and memory intensive workloads. For this purpose, we first validate Intel RAPL power capping mechanism using a high-precision external power meter and investigate properties such as accuracy, power limit granularity, and settling time. Then, we attempt to determine which mechanisms are employed by RAPL to adjust power consumption.

Due to some overprovisioning policies and variable usage, data centers in production can face low average resource utilization. This can result in a waste of underused servers and energy. In this context, virtual machine (VM) consolidation combined with shutdown policies can be a pertinent approach for improving resource utilization and reducing energy consumption of the entire cloud infrastructure. However, VM consolidation requires expensive migration techniques, which can potentially affect performance. Consolidation of workload has been proposed and studied as a core capability since the invention of the Cloud. But after two decades of deployment of Cloud infrastructures, VM consolidation is still rarely used in production for small and large-scale environments. In this article, we explore and revisit the potential of savings that can be achieved through a versatile and efficient Virtual Machine consolidation in small and large-scale production infrastructures through usage analysis of two Cloud providers infrastructures. We show that potential benefits in terms of saved cloud resources and energy usage reduction can occur for systems in production.

Information and Communication Technologies (ICT) and Data Centres (DC) have nowadays considerable environmental impacts. The number of applications and services hosted in the cloud is significant, and the associated number of infrastructure is steadily increasing. Cloud Service Providers (CSP) need to respond to this growing demand and size their infrastructures accordingly but users misbehaviour and expectations in terms of service quality lead to oversized infrastructures. Infrastructures are sized to meet peak-demand, resulting in poor resource utilization and additional power consumption. But as their behaviour can increase DC energy consumption and environmental footprint, users can also help reducing them. In this paper, we study how users provided with a simple tool can reduce the power consumption of a virtualization cluster in a cloud company. Using a Virtual Machine (VM) shutdown policy, users can directly contribute to the mitigation of the power consumption of the infrastructure. Part of the paper is dedicated to profile the users and understand their behaviour when it comes to powering off their VMs. To further reduce the energy consumption of the cluster, we combine the VM shutdown policy with a simple consolidation heuristic. Simulations show a 23.95% power consumption reduction, with an additional 8.72% reduction thanks to the users. A production implementation was conducted and results in a 12.58% power consumption reduction over one week.

As digital services are increasingly being deployed and used in a variety of domains, the environmental impact of Information and Communication Technologies (ICTs) is a matter of concern. Artificial intelligence is driving some of this growth but its environmental cost remains scarcely studied. A recent trend in large-scale generative models such as ChatGPT has especially drawn attention since their training requires intensive use of a massive number of specialized computing resources. The inference of those models is made accessible on the web as services, and using them additionally mobilizes end-user terminals, networks, and data centers. Therefore, those services contribute to global warming, worsen metal scarcity, and increase energy consumption. This work proposes an LCA-based methodology for a multi-criteria evaluation of the environmental impact of generative AI services, considering embodied and usage costs of all the resources required for training models, inferring from them, and hosting them online. We illustrate our methodology with Stable Diffusion as a service, an open-source text-to-image generative deep-learning model accessible online. This use case is based on an experimental observation of Stable Diffusion training and inference energy consumption. Through a sensitivity analysis, various scenarios estimating the influence of usage intensity on the impact sources are explored.

Edge computing helps to release the tension at the center of IoT systems’ networks, thus reducing the latency, optimizing the bandwidth, and providing new privacy and security solutions, among others. Despite its benefits, edge computing faces unique challenges, including latency, security, and resource constraints. Among these challenges, energy consumption has emerged in the research community, and the global objective is to do "more with less". Researchers explore diverse strategies to enhance sustainability, from hardware optimizations to intelligent algorithms. The quest for energy efficiency and to reduce several impacts aligns with broader efforts to create an environmentally conscious technology landscape.In this paper, we present a task selection model for the edge. We focus on energy consumption and aim to maximize the value given by tasks, all the while minimizing the energy consumed. To do so, we develop a computer environment to simulate outdoor energy-harvesting edge devices, contribute to research reproducibility by recreating a photovoltaic energy harvesting prediction model, and train deep reinforcement learning models to select the best set of tasks at the edge. Our best deep reinforcement learning model, which uses Trust Region Policy Optimization, outperforms our best heuristic and is a robust task selector under varying external conditions.

EcoIndex has been proposed to evaluate the absolute environmental performance of a given URL using a score ranging from 0 to 100 (the higher, the better). In this article, we make a critical analysis of the initial approach and propose alternatives that no longer calculate a plain score but allow the query to be situated among other queries. The generalized critiques come with statistics and rely on extensive experiments (first contribution). Then, we move on to low-cost Machine Learning (ML) approaches (second contribution) and a transition before obtaining our final results (third contribution). Our research aims to extend the initial idea of analytical computation, i.e., a relation between three variables, in the direction of algorithmic ML computations. The fourth contribution corresponds to a discussion on our implementation, available on a GitHub repository. Along with the paper, we invite the reader to examine the question: What attributes make sense for our problem?, or equivalently, what is a relevant data policy for studying digital environmental impacts? Beyond computational questions, it is important for the scientific community to focus on this question in particular. We currently promote using well-established ML techniques because of their potential, which we discuss in the paper. However, we also question techniques for their frugality or otherwise. Our data science project is still at the data exploration stage. We also want to encourage synergy between technical expertise and business knowledge because this is fundamental for advancing the data project.

Precision Beekeeping systems collect data on colonies of bees using sensors to assist beekeepers and preserve bees. The collection, transfer, and processing of such data are energy-intensive processes. However, most Precision Beekeeping systems work under a limited energy budget.This article focuses on the placement of AI-based energy-aware Precision Beekeeping services at the edge and in the cloud. We deploy a set of smart beehives as edge devices in order to conduct experiments and collect real data. Then, we simulate large-scale systems with embedded services. Our results show that parameters like the number of smart beehives, the frequency of data collection, and different losses impact the placement of these services to enhance energy efficiency.

Internet of Things (IoT) applications often require the processing of data streams generated by devices dispersed over a large geographical area. Traditionally, these data streams are forwarded to a distant cloud for processing, thus resulting in high application end-to-end latency. Recent work explores the combination of resources located in clouds and at the edges of the Internet, called cloud-edge infrastructure, for deploying Data Stream Processing (DSP) applications. Most previous work, however, fails to scale to very large IoT settings. This paper introduces deployment strategies for the placement of Data Stream Processing (DSP) applications onto cloud-edge infrastructure. The strategies split an application graph into regions and consider regions with stringent time requirements for edge placement. The proposed Aggregate End-to-End Latency Strategy with Region Patterns and Latency Awareness (AELS+RP+LA) decreases the number of evaluated resources when computing an operator's placement by considering the communication overhead across computing resources. Simulation results show that, unlike the state-of-the-art, Aggregate End-to-End Latency Strategy with Region Patterns and Latency Awareness (AELS+RP+LA) scales to environments with more than 100k resources with negligible impact on the application end-to-end latency.

Data centers are very energy-intensive facilities that can generate various environmental impacts. Numerous energy, power, and environmental leverages exist and can help cloud providers and data center managers to reduce some of these impacts. But dealing with such heterogeneous leverages can be a challenging task that requires some support from a dedicated framework. This article presents a new approach for modeling, evaluating, and orchestrating a large set of technological and logistical leverages. Our framework is based on leverages modeling and Gantt chart leverages mapping. First experimental results based on selected scenarios show the pertinence of the proposed approach in terms of management facilities and potential impacts reduction.

The global energy demand for digital activities is constantly growing. Computing nodes and cloud services are at the heart of these activities. Understanding their energy consumption is an important step towards reducing it. On one hand, physical power meters are very accurate in measuring energy but they are expensive, difficult to deploy on a large scale, and are not able to provide measurements at the service level. On the other hand, power models and vendor-specific internal interfaces are already available or can be implemented on existing systems. Plenty of tools, called software-based power meters, have been developed around the concepts of power models and internal interfaces, in order to report the power consumption at levels ranging from the whole computing node to applications and services. However, we have found that it can be difficult to choose the right tool for a specific need. In this work, we qualitatively and experimentally compare several software-based power meters able to deal with CPU or GPU-based infrastructures. For this purpose, we evaluate them against high-precision physical power meters while executing various intensive workloads. We extend this empirical study to highlight the strengths and limitations of each software-based power meter.

This paper focuses on SDN-based approaches for deploying stream processing workloads on heterogeneous environments comprising wide-area networks, cloud and fog resources. Stream processing applications impose strict latency requirements to operate appropriately. Deploying workloads in the fog reduces unnecessary delays, but its computational resources may not handle all the tasks. On the other hand, offloading the tasks to the cloud is constrained by limited network resources and involves additional transmission delays that exceed latency thresholds. Adaptive workload deployment may solve these issues by ensuring that resource and latency requirements are satisfied for all the data streams processed by an application. This paper's main contribution consists of dynamic workload placement algorithms operating on stream processing requests with latency constraints. Provisioning of computing infrastructure exploits the interplay between fog and cloud under limited network capacity. The algorithms aim to maximize the ratio of successfully handled requests by effectively utilizing available resources while meeting application latency constraints. Experiments demonstrate that the goal can be achieved by detailed analysis of requests and ensuring balanced computing and network resources utilization. As a result, up to 30% improvement over the reference algorithms in success rate is observed.