As the demand for AI-driven workloads increases, the energy consumption of Graphics Processing Units (GPUs) devices has come under intense scrutiny, particularly in hyperscale data centers where large numbers of accelerators are centralized and leased to diverse clients.

Ten years after the drafting of the Paris Agreement, the objective to reduce greenhouse gas emissions by half by 2030 seems difficult to reach. The impact of ICT technologies is growing year after year. Data centers have a large impact in this domain. As a lot of research is focused on improving energy efficiency of data centers, one could ask if this improvement would suffice to reduce greenhouse gas emissions. To study the impact of the energy efficiency on the long term, we propose to model a data center that has a limited power capacity and is regularly refreshed with new servers, more energy-efficient than the previous ones, to cope with its workload. Our results explore various growth rates for the load, and the energy efficiency and show that without reduction of usage of data center resources, the improvements in energy efficiency will not be not enough to reach the Paris Agreement’s objective.

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$\mathbf{1, 0 7 6}$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.

This paper presents a study of the current con-sumption of various Medium Access Control (MAC) states for Wi-Fi devices, focusing on the latest IEEE 802.11ax (Wi-Fi 6) and 802.11ac standards. Through detailed experimental measurements, we provide new, precise values for the current drawn in MAC states such as idle, transmission, reception, and sleep. We capture real-time current consumption in different scenarios with various traffic loads, offering an understanding of the power usage across different operational modes. The results reveal significant differences in power consumption patterns compared to earlier Wi-Fi standards (e.g., 802.11n), with notable improvements in energy efficiency in sleep and idle states, but increased current draw in transmission and reception states. These findings provide valuable insights into the energy behavior of recent Wi-Fi devices, offering a solid basis for the simulation of power management optimization strategies in next-generation wireless systems.

The deployment of applications closer to end-users through fog computing has shown promise in improving network communication times and reducing contention. However, the use of fog applications such as microservices necessitates intricate network interactions among heterogeneous devices. Consequently, understanding the impact of different application and infrastructure parameters on performance becomes crucial. Current literature either offers end-to-end models that lack granularity and validation or fine-grained models that only consider a portion of the infrastructure. Our research first compares experimentally the accuracy of the existing integrated frameworks. We then combine one of these tools with a collection of validated models to obtain comprehensive metrics regarding microservice applications operating in the fog. Through a use-case, we demonstrate the effectiveness of our approach in investigating fog environments, from examining application latencies to greenhouse gas emissions.

Integrating larger shares of renewables in data centers' electrical mix is mandatory to reduce their carbon footprint. However, as they are intermittent and fluctuating, renewable energies alone cannot provide a 24/7 supply and should be combined with a secondary source. Finding the optimal infrastructure configuration for both renewable production and financial costs remains difficult. In this paper, we examine three scenarios with on-site renewable energy sources combined respectively with the electrical grid, batteries alone and batteries with hydrogen storage systems. The objectives are first, to size optimally the electric infrastructure using combinations of standard microgrids approaches, secondly to quantify the level of grid utilization when data centers consume/ export electricity from/to the grid, to determine the level of effort required from the grid operator, and finally to analyze the cost of 100% autonomy provided by the battery-based configurations and to discuss their economical viability. Our results show that in the grid-dependent mode, 63.1% of the generated electricity has to be injected into the grid and retrieved later. In the autonomous configurations, the cheapest one including hydrogen storage leads to a unit cost significantly more expensive than the electricity supplied from a national power system in many countries.

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.

Edge Computing has emerged from the Cloud to tackle the increasingly stringent latency, reliability and scalability imperatives of modern applications, mainly in the Internet of Things arena. To this end, the data centers are pushed to the edge of the network to diversify and bring the services closer to the users. This spatial distribution offer a wide range of opportunities for allowing self-consumption from local renewable energy sources with regard to the local weather conditions. However, scheduling the users’ tasks so as to meet the service restrictions while consuming the most renewable energy and reducing the carbon footprint remains a challenge. In this paper, we design a nationwide Edge infrastructure, and study its behavior under three typical electrical configurations including solar power plant, batteries and the grid. Then, we study a set of techniques that collaboratively allocates resources on the edge data centers to harvest renewable energy and reduce the environmental impact. These strategies also includes energy efficiency optimization by means of reasonable quality of service degradation and consolidation techniques at each data center in order to reduce the need for brown energy. The simulation results show that combining these techniques allows to increase the self-consumption of the platform by 7.83% and to reduce the carbon footprint by 35.7% compared to the baseline algorithm. The optimizations also outperform classical energy-aware resource management algorithms from the literature. Yet, these techniques do not equally contribute to these performances, consolidation being the most efficient.

International audience

Several countries have deployed, or have started the deployment of a smart metering infrastructure in order to enable the Smart Grid. This infrastructure aims to provide new services to grid users and grid operators relying on several communication technologies. One of the goals of this infrastructure is to improve energy consumption, for instance by increasing the awareness of the users, or by enforcing energy management policies. Yet, this infrastructure also consumes energy. The objective of this work is to accurately characterize the energy consumption of each part of the smart metering infrastructure, at a nation-wide scale. We also explore several consumption scenarios highlighting the impact of legacy technologies on the energy consumption of the smart metering infrastructure.

Wi-Fi networks are extensively used to provide Internet access to end-users and to deploy applications at the edge. By playing a major role in modern networking, Wi-Fi networks are getting bigger and denser. However, studying their performance at large-scale and in a reproducible manner remains a challenging task. Current solutions include real experiments and simulations. While the size of experiments is limited by their financial cost and potential disturbance of commercial networks, the simulations also lack scalability due to their models' granularity and computational runtime. In this paper, we introduce a new Wi-Fi model for large-scale simulations. This model, based on flow-level simulation, requires fewer computations than state-of-the-art models to estimate bandwidth sharing over a wireless medium, leading to better scalability. Comparing our model to the already existing Wi-Fi implementation of ns-3, we show that our approach yields to close performance evaluations while improving the runtime of simulations by several orders of magnitude. Using this kind of model could allow researchers to obtain reproducible results for networks composed of thousands of nodes much faster than previously.

Abstract Vectorized instructions were introduced to improve the performance of applications. However, they come at the cost of an increase in the power consumption. As a consequence, processors are designed to limit their frequency when such instructions are used in order to respect the thermal design power limit. In this paper, we study and compare the impact of thermal design power and Simple Instruction Multiple Data (SIMD) instructions on performance, power and energy consumption of processors and memory. The study is performed on three different architectures providing different characteristics and four applications with different profiles (including one application with different phases, each phase having a different profile). The study shows that, because of processor frequency, performance and power consumption are strongly related to thermal design power. It also shows that AVX512 has unexpected behavior regarding processor power consumption, while DRAM power consumption is impacted by SIMD instructions because of the generated memory throughput. Finally, this paper tackles the impact of turboboost which shows equivalent to better performance for all the studied cases while not always decreasing energy consumption.

In order to improve locality aspects, new Cloud-related architectures such as Edge Computing have been proposed. Despite the growing popularity of these new architectures, their energy consumption has not been well investigated yet. To move forward on such a critical question, we first introduce a taxonomy of different Cloud-related architectures. From this taxonomy, we then present an energy model to evaluate their consumption. Unlike previous proposals, our model comprises the full energy consumption of the computing facilities, including cooling systems, and the energy consumption of network devices linking end users to Cloud resources. Finally, we instantiate our model on different Cloud-related architectures, ranging from fully centralized to completely distributed ones, and compare their energy consumption. The results show that a completely distributed architecture, because of not using intra-data center network and large-size cooling systems, consumes between 14 and 25 percent less energy than fully centralized and partly distributed architectures, respectively. To the best of our knowledge, our work is the first one to propose a model that enables researchers to analyze and compare energy consumption of different Cloud-related architectures.

Graph partitioning, a preliminary step of distributed graph processing, has been attracting increasing attention in the last decade. A high quality graph partitioning algorithm should facilitate graph processing by minimizing the communication overhead and maintaining the load balancing among distributed computing units. Offline partitioning algorithms usually require the knowledge of a complete graph, and therefore, are not adaptive to handle massive graph-structured data. On the contrary, streaming partitioning algorithms take edges or vertices as a stream and make partitioning decisions on the fly. However, the streaming manner faces dilemmas from time to time because of a lack of knowledge. Furthermore, an unmindful partitioning decision in such a dilemma could significantly decrease the partition quality. In this paper, we propose a novel window-based streaming graph partitioning algorithm (WSGP). WSGP leverages a greedy-based heuristic to perform edge partitioning. When facing a decision dilemma, WSGP utilizes a size-bounded window to buffer the edges. When the window is fully filled, an edge is poped and assigned to a partition. The assignment is decided by knowledge obtained from both the edges already settled and the ones still cached in the buffer window. Our experiments take into account various real-world benchmark graphs. The experimental results demonstrate that WSGP consistently has a smaller replication factor than the state-of-the-art algorithms by up to 23%, at a limited cost in terms of memory and comprehensive running time.

Despite recent advances in improving the performance of high performance computing (HPC) and distributed systems, power dissipation and thermal cooling challenges persist, impacting their total cost of ownership. Making HPC systems more energy and thermal efficient will require understanding of individual power dissipation and temperature contributions of multiple hardware system components and their accompanying software. In this work, we present an experimental workflow for energy and temperature profiling on systems running parallel applications. It allows full and dynamic control over the execution of applications for the entire frequency range. Through its use, we show that the energy response to frequency scaling is highly dependent on the workload characteristics and it is convex in nature with an optimal frequency point. During the course of our experimentation, we encountered a non-intuitive finding, where we observed that the tested low-power processor is consuming more power on average than the standard processor.

Microservices transform monolithic applications into simple, scalable, and interacting services. It allows for faster development and fine-grained deployments. However, the cooperation of several services leads to intricate dependencies, hindering the detection of performance bottlenecks. Current microservice performance analysis methods require real deployments, a costly process both in time and resources, while performance prediction through simulation relies on models that are complex to develop and instantiate. In this paper, we propose a microservice performance analysis approach based on simulation. Our contribution first introduces a microservice performance model requiring few instantiation parameters. We then propose a methodology to automatically derive model instantiation values from a single execution trace. We evaluate this methodology on two benchmarks from the literature. Our approach accurately predicts the deployment performance of large-scale microservice applications in various configurations from a single execution trace. This provides valuable insights on the performance of an application prior to its deployment on real platform.

Smart grids require the large-scale deployment of communication means to interconnect the electrical devices and to autonomously pilot their management. Hence, interconnected tools from both the communication and the power system communities are required in order to adequately simulate the mutual dependencies between these two infrastructures. In this paper, we propose an open-source co-simulation framework for evaluating the mutual impacts between an electrical distribution network and its supervision communication network. A case study dealing with line congestion mitigation is presented to illustrate the versatility of our tool.

Building a sustainable Exascale machine is a very promising target in High Performance Computing (HPC). To tackle the energy consumption challenge while continuing to provide tremendous performance, the HPC community have rapidly adopted GPU-based systems. Today, GPUs have became the most prevailing components in the massively parallel HPC landscape thanks to their high computational power and energy efficiency. Modeling the energy consumption of applications running on GPUs has gained a lot of attention for the last years. Alas, the HPC community lacks simple yet accurate simulators to predict the energy consumption of general purpose GPU applications. In this work, we address the prediction of the energy consumption of CUDA kernels via simulation. We propose in this paper a simple and lightweight energy model that we implemented using the open-source framework SimGrid. Our proposed model is validated across a diverse set of CUDA kernels and on two different NVIDIA GPUs (Tesla M2075 and Kepler K20Xm). As our modeling approach is not based on performance counters or detailed-architecture parameters, we believe that our model can be easily approved by users who take care of the energy consumption of their GPGPU applications.

The Cloud already represents an important part of the global energy consumption, and this consumption keeps increasing. Many solutions have been investigated to increase its energy efficiency and to reduce its environmental impact. However, with the introduction of new requirements, notably in terms of latency, an architecture complementary to the Cloud is emerging: the Fog. The Fog computing paradigm represents a distributed architecture closer to the end-user. Its necessity and feasibility keep being demonstrated in recent works. However, its impact on energy consumption is often neglected and the integration of renewable energy has not been considered yet. The goal of this work is to exhibit an energy-efficient Fog architecture considering the integration of renewable energy. We explore three resource allocation algorithms and three consolidation policies. Our simulation results, based on real traces, show that the intrinsic low computing capability of the nodes in a Fog context makes it harder to exploit renewable energy. In addition, the share of the consumption from the communication network between the computing resources increases in this context, and the communication devices are even harder to power through renewable sources.

Information and Communication Technology takes a growing part in the worldwide energy consumption. One of the root causes of this increase lies in the multiplication of connected devices. Each object of the Internet-of-Things often does not consume much energy by itself. Yet, their number and the infrastructures they require to properly work have leverage. In this paper, we combine simulations and real measurements to study the energy impact of IoT devices. In particular, we analyze the energy consumption of Cloud and telecommunication infrastructures induced by the utilization of connected devices, and we propose an end-to-end energy consumption model for these devices.

High-performance computing (HPC) aims at developing models and simulations for applications in numerous scientific fields. Yet, the energy consumption of these HPC facilities currently limits their size and performance, and consequently the size of the tackled problems. The complexity of the HPC software stacks and their various optimizations makes it difficult to finely understand the energy consumption of scientific applications. To highlight this difficulty on a concrete use-case, we perform an energy and power analysis of a software stack for the simulation of frequency-domain electromagnetic wave propagation. This solver stack combines a high order finite element discretization framework of the system of three-dimensional frequency -domain Maxwell equations with an algebraic hybrid iterative-direct sparse linear solver. This analysis is conducted on the KNL-based PRACE-PCP system. Our results illustrate the difficulty in predicting how to trade energy and runtime.

The growing appetite of new technologies, such as Internet-of-Things, for Cloud resources leads to an unprecedented energy consumption for these infrastructures. In order to make these energy-hungry distributed systems more sustainable, Cloud providers resort more and more to on-site renewable energy production facilities like photovoltaic panels. Yet, this intermittent and variable electricity production is often uncorrelated with the Cloud consumption induced by its workload. Geographical load balancing, virtual machine (VM) migration and consolidation can be used to exploit multiple Cloud data centers' locations and their associated photovoltaic panels for increasing their renewable energy consumption. However, these techniques cost energy and network bandwidth, and this limits their utilization. In this paper, we propose to rely on the flexibility brought by Smart Grids to exchange renewable energy between distributed sites and thus, to further increase the overall Cloud's self-consumption of the locally-produced renewable energy. Our solution is named SCORPIUS: Self-Consumption Optimization of Renewable energy Production In distribUted cloudS. It takes into account telecommunication network constraints and electrical grid requirements to optimize the Cloud's self-consumption by trading-off between VM migration and renewable energy exchange. Our simulation-based results show that SCORPIUS outperforms existing solutions on various workload traces of production Clouds in terms of both renewable self-consumption and overall energy consumption.

Reducing the energy consumed by datacenters becomes of major importance due to the current climate changes and the increasing success of cloud computing. Studies to optimize the energy consumption of cloud systems exist but do not take into account the end-user. The goal of this paper is to understand and quantify the link between the PaaS parameters a user can configure and the application energy consumption. In this work we summarize the parameters available at the PaaS layer and measure their influence on the energy consumed by RUBiS, a web application benchmark. Different database technologies and programming languages are compared as well as their software versions. Experimentation results show that by themselves the existing PaaS parameters can variate the application energy consumption. Although it shows that different programming languages do not consume the same, we discovered that a wider energy difference exists between database technologies which means higher energy savings is possible when the less consuming technology is used. Thus, users could optimize the energy impact of their applications by carefully configuring on-hand PaaS parameters.

The Functional Mock-up Interface (FMI) standard is becoming an essential solution for co-simulation. In this paper, we address a specific issue which arises in the context of Distributed Cyber-Physical System (DCPS) co-simulation where Functional Mock-up Units (FMU) need to interact with distributed application models. The core of the problem is that, in general, complex distributed application behaviors cannot be easily and accurately captured by a modeling formalism but are instead directly specified using a standard programming language. As a consequence, the model of a distributed application is often a concurrent program. The challenge is then to bridge the gap between this programmatic description and the equation-based framework of FMI in order to make FMUs interact with concurrent programs. In this article, we show how we use the unique model of execution of the SimGrid simulation platform to tackle this issue. The platform manages the co-evolution and the interaction between IT models and the different concurrent processes which compose a distributed application code. Thus, SimGrid offers a framework to mix models and concurrent programs. We show then how we specify an FMU as a SimGrid model to solve the DCPS co-simulation issues. Compared to other works of the literature, our solution is not limited to a specific use case and benefits from the versatility and scalability of SimGrid.

Distributed Clouds are nowadays an essential component for providing Internet services to always more numerous connected devices. This growth leads the energy consumption of these distributed infrastructures to be a worrying environmental and economic concern. In order to reduce energy costs and carbon footprint, Cloud providers could resort to producing onsite renewable energy, with solar panels for instance. In this paper, we propose NEMESIS: a Network-aware Energy-efficient Management framework for distributEd cloudS Infrastructures with on-Site photovoltaic production. NEMESIS optimizes VM placement and balances VM migration and green energy consumption in Cloud infrastructure embedding geographically distributed data centers with on-site photovoltaic power supply. We use the Simgrid simulation toolbox to evaluate the energy efficiency of NEMESIS against state-of-the-art approaches.

Reducing the massive amount of energy consumed by cloud datacenters becomes of major importance. In the usual approach where resources are consolidated into fewer servers in order to power down the others, it still remains periods of time when servers are not fully utilized. Consequently, it exists unused resources that are not exploited although they could be used to execute applications compatible with the variable availability of these resources. In this work, we propose a cloud system where the Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) layers interact to find execution trade-offs that exploit the unused resources at IaaS level. PaaS users are involved in the energy optimization by proposing to delay their executions and adapt resource sizes in order to fit with the available unused resources. Our evaluation by simulation is based on real data and expresses a realistic large scale cloud scenario. Results show that according to the proportion of energy-aware users, this system is able to reduce the amount of servers by using resources that would have been wasted otherwise. Therefore, our solution allows datacenters to consume less energy than with usual resource managers where all applications start their execution at submission time with their initial resource size.

Summary Current large‐scale systems, like datacenters and supercomputers, are facing an increasing electricity consumption. These infrastructures are often dimensioned according to the workload peak. However, as their consumption is not power‐proportional when the workload is low, the power consumption is still high. Shutdown techniques have been developed to adapt the number of switched‐on servers to the actual workload. However, datacenter operators are reluctant to adopt such approaches because of their potential impact on reactivity and hardware failures, and their energy gain is often largely misjudged. In this article, we evaluate the potential gain of shutdown techniques by taking into account shutdown and boot up costs in time and energy. This evaluation is made on recent server architectures and future energy‐aware architectures. Our simulations exploit real traces collected on production infrastructures under various machine configurations with several shutdown policies, with and without workload prediction. We study the impact of future's knowledge for saving energy with such policies. Finally, we examine the energy benefits brought by suspend‐to‐disk and suspend‐to‐RAM techniques, and we study the impact of shutdown techniques on the energy consumption of prospective hardware with heterogeneous processors (big‐medium‐little paradigm).

The emergence of Internet of Things (IoT) is participating to the increase of data-and energy-hungry applications. As connected devices do not yet offer enough capabilities for sustaining these applications, users perform computation offloading to the cloud. To avoid network bottlenecks and reduce the costs associated to data movement, edge cloud solutions have started being deployed, thus improving the Quality of Service. In this paper, we advocate for leveraging on-site renewable energy production in the different edge cloud nodes to green IoT systems while offering improved QoS compared to core cloud solutions. We propose an analytic model to decide whether to offload computation from the objects to the edge or to the core Cloud, depending on the renewable energy availability and the desired application QoS. This model is validated on our application use-case that deals with video stream analysis from vehicle cameras.

Monitoring and assessing the energy efficiency of supercomputers and data centers is crucial in order to limit and reduce their energy consumption. Applications from the domain of High Performance Computing (HPC), such as MPI applications, account for a significant fraction of the overall energy consumed by HPC centers. Simulation is a popular approach for studying the behavior of these applications in a variety of scenarios, and it is therefore advantageous to be able to study their energy consumption in a cost-efficient, controllable, and also reproducible simulation environment. Alas, simulators supporting HPC applications commonly lack the capability of predicting the energy consumption, particularly when target platforms consist of multi-core nodes. In this work, we aim to accurately predict the energy consumption of MPI applications via simulation. Firstly, we introduce the models required for meaningful simulations: The computation model, the communication model, and the energy model of the target platform. Secondly, we demonstrate that by carefully calibrating these models on a single node, the predicted energy consumption of HPC applications at a larger scale is very close (within a few percents) to real experiments. We further show how to integrate such models into the SimGrid simulation toolkit. In order to obtain good execution time predictions on multi-core architectures, we also establish that it is vital to correctly account for memory effects in simulation. The proposed simulator is validated through an extensive set of experiments with wellknown HPC benchmarks. Lastly, we show the simulator can be used to study applications at scale, which allows researchers to save both time and resources compared to real experiments.

Networking infrastructures are considered to consume as much energy as terminal end-user equipment or datacenters. While energy consumption of wireless networks is a matter of concern since their beginning, it is not the case for wired networks as they do not rely on batteries, but on plugged equipment. Yet, facing growing consumption, energy-efficient techniques start to be implemented in wired networks. However, measuring the end-to-end energy consumption of wired networking infrastructures remains a real challenge for network operators and scientists. This article presents the ECOFEN (Energy Consumption mOdel For End-to-end Networks) framework which allows to support precise simulation of energy consumption of large-scale complex wired networks. The experimental validation shows that Ecofen provides accurate energy consumption values.

With the generalization of cloud infrastructures usage, energy consumption has become a major issue. Scheduling heuristics have been proposed to optimize the resource usage of data center so as to take down the energy consumption. This paper tackles the problem with a different approach by taking into consideration the availability of renewable energy. First we formalize the green energy aware scheduling problem (GEASP) and propose a global model based on constraint programming as well as a search heuristic to solve it efficiently. The proposed model integrates the various aspects inherent to the dynamic planning in a data center: heterogeneous physical machines, various application types (i.e., active or online applications and batch applications), actions and energetic costs of turning ON/OFF physical machines, interrupting/resuming batch applications, CPU and RAM resource consumption, tasks migration, migration costs, and integration of green energy availability. The model can therefore reduce both the costs related to energy consumption and the carbon footprint of a data center. We evaluate the model against the state-of-the-art framework PIKA on real-world workload and solar power traces.

Cloud computing has become an attractive and easy-to-use solution for users who want to externalize the run of their applications. However, data centers hosting cloud systems consume enormous amounts of energy. Reducing this consumption becomes an urgent challenge with the rapid growth of cloud utilization. In this paper, we explore a way for energy-aware HPC cloud users to reduce their footprint on cloud infrastructures by reducing the size of the virtual resources they are asking for. We study the influence of green users on the system energy consumption and compare it with the consumption of more aggressive users in terms of resource utilization. We found that larger resources are more energy demanding even if they are faster in executing the applications. But, reducing too much the resources' size is also not beneficial for the energy consumption. A tradeoff lies in between these two options.

The fast growth of cloud computing considerably increases the energy consumption of cloud infrastructures, especially, data centers. To reduce brown energy consumption and carbon footprint, renewable energy such as solar/wind energy is considered recently to supply new green data centers. As renewable energy is intermittent and fluctuates from time to time, this paper considers two fundamental approaches for improving the usage of renewable energy in a small/medium-sized data center. One approach is based on opportunistic scheduling: more jobs are performed when renewable energy is available. The other approach relies on Energy Storage Devices (ESDs), which store renewable energy surplus at first and then, provide energy to the data center when renewable energy becomes unavailable. In this paper, we explore these two means to maximize the utilization of on-site renewable energy for small data centers. By using real-world job workload and solar energy traces, our experimental results show the energy consumption with varying battery size and solar panel dimensions for opportunistic scheduling or ESD-only solution. The results also demonstrate that opportunistic scheduling can reduce the demand for ESD capacity. Finally, we find an intermediate solution mixing both approaches in order to achieve a balance in all aspects, implying minimizing the renewable energy losses. It also saves brown energy consumption by up to 33% compared to ESD-only solution.

Cloud computing allows for elasticity as users can dynamically benefit from new virtual resources when their workload increases. Such a feature requires highly reactive resource provisioning mechanisms. In this paper, we propose two new workload prediction models, based on constraint programming and neural networks, that can be used for dynamic resource provisioning in Cloud environments. We also present two workload trace generators that can help to extend an experimental dataset in order to test more widely resource optimization heuristics. Our models are validated using real traces from a small Cloud provider. Both approaches are shown to be complimentary as neural networks give better prediction results, while constraint programming is more suitable for trace generation.

The costs of current data centers are mostly driven by their energy consumption (specifically by the air conditioning, computing and networking infrastructure). Yet, current pricing models are usually static and rarely consider the facilities' energy consumption per user. The challenge is to provide a fair and predictable model to attribute the overall energy costs per virtual machine (VM). Current pay-as-you-go models of Cloud providers allow users to easily know how much their computing will cost. However, this model is not fully transparent as to where the costs come from (e.g., energy). In this paper we introduce EPAVE, a model for Energy-Proportional Accounting in VM-based Environments. EPAVE allows transparent, reproducible and predictive cost calculation for users and for Cloud providers. We show these characteristics of EPAVE by a number of use cases in heterogeneous data centers and discuss the applicability of EPAVE.

Snooze is an open-source scalable, autonomic, and energy-efficient virtual machine (VM) management framework for private clouds. It allows users to build compute infrastructures from virtualized resources. Particularly, once installed and configured, it allows its users to submit and control the life-cycle of a large number of VMs. For scalability, the system relies on a self-organizing hierarchical architecture. Moreover, it implements self-healing mechanisms in case of failure to enable high availability. It also performs energy-efficient distributed VM management through consolidation and power management techniques. This poster focuses on the experimental validation of two main properties of Snooze: scalability and fault-tolerance.

Virtualized data centers where several virtual machines (VMs) are hosted per server are becoming more popular due to Cloud Computing. As a consequence of energy efficiency concerns, the exact combination of VMs running on a specific server will most likely change over time. We present experimental results how to use the energy/power consumption logs of a power monitored server as a side-channel that allows us to recognize the exact combination of VMs it currently hosts to a high degree. For classification, we use a maximum log-likelihood approach, which works well for comparably small training and test set sizes. We also show to which degree a specific VM can be recognized, regardless of other VMs currently running on the same server, and show false negative/positive rates. To cross-validate our results, we have used a Kolmogorov-Smirnov test, resulting in comparable quality of recognition within shorter time. In order to clarify whether our approach is generalizable and yields reproducible results, we have set up a second experimental infrastructure in Lyon, using a different hardware platform and power measurement device. We have obtained similar results and have experimented with different CPU frequency scaling governors, yielding comparable quality of recognition. As a result, energy consumption data of servers must be protected carefully, as it is potentially valuable information for an attacker trying to track down a VM to mount further attack steps.

Reducing the energy consumption of wired networks has become a key concern for manufacturers of network equipments and network providers. In this paper, we propose an energy-efficient data transfer framework that uses advance bandwidth provisioning and on/off algorithms to put unused nodes of wired networks into sleep mode. This framework is termed as High-level Energy-awaRe Model for bandwidth reservation in End-to-end networkS (HERMES). We explore centralized, decentralized and clustered approaches for managing network resources and bandwidth reservations in this framework. By applying these approaches to HERMES, we evaluate via simulation their efficiency in terms of performance and energy consumption.

This paper presents HERMES: an energy-efficient data transfer framework for data-center, grid and cloud networks. An architecture and simulations are provided to show that this framework could save more than two-thirds of the energy currently consumed by these networks.

Wired networks are increasing in size and their power consumption is becoming a matter of concern. Evaluating the end-to-end electrical cost of new network architectures and protocols is difficult due to the lack of monitored realistic infrastructures. We propose an End-to-End energy Cost mOdel and simulator For Evaluating power consumption in large-scale Networks (ECOFEN) whose user's entries are the network topology and traffic. Based on configurable measurement of different network components (routers, switches, NICs, etc.), it provides the power consumption of the overall network including the end-hosts as well as the power consumption of each equipment over time.

The question of energy savings has been a matter of concern since a long time in the mobile distributed systems and battery-constrained systems. However, for large-scale non-mobile distributed systems, which nowadays reach impressive sizes, the energy dimension (electrical consumption) just starts to be taken into account. In this paper, we present the GREEN-NET1framework which is based on 3 main components: an ON/OFF model based on an Energy Aware Resource Infrastructure (EARI), an adapted Resource Management System (OAR) for energy efficiency and a trust delegation component to assume network presence of sleeping nodes.

While an extensive set of research project deals with the saving power problem of electronic devices powered by electric battery, few have interest in large scale distributed systems permanently plugged in the wall socket. However, a rapid study shows that each computer, member of a distributed system platform, consume a substantial quantity of power especially when those resources are idle. Today, given the number of processing resources involved in large scale computing infrastructure, we are convinced that we can save a lot of electric power by applying what we called green policies. Those policies, introduced in this article, propose to alternatively switch On and Off computer nodes in a clever way.

The question of energy savings has been a matter of concern since a long time in the mobile distributed systems and battery-constrained systems. However, for large-scale non-mobile distributed systems, which nowadays reach impressive sizes, the energy dimension (electrical consumption) just starts to be taken into account. In this paper, we analyze the usage of an experimental grid over a one-year period. Based on this analysis, we propose a resource reservation infrastructure which takes into account the energy issue. We validate our infrastructure on the large scale experimental Grid5000 platform and present the obtained gains in terms of energy.