Predicting the Energy-Consumption of MPI Applications at Scale Using Only a Single Node

Heinrich, Franz; Cornebize, Tom; Degomme, Augustin; Legrand, Arnaud; Carpen-Amarie, Alexandra; Hunold, Sascha; Orgerie, Anne‐Cécile; Quinson, Martin

doi:10.1109/cluster.2017.66

Cited by 36 publications

(26 citation statements)

References 26 publications

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“…This mapper function is instantiated with values taken from real energy measurements when fully loading the cores of a PM one by one. In [21], the authors show that this model is accurate to a few percent for the considered applications. So, for a PM x with k cores, average power consumption values are measured for 1, 2, and up to k fully loaded cores.…”

Section: Pms Dynamic Energy Consumptionmentioning

confidence: 92%

“…Yet, this simulator does not consider effect of packet length on energy consumption in its models. Finally, the SimGrid toolbox proposes accurate modeling of virtual machines [20], and energy consumption of servers [21]. But, the energy consumption of network components is not provided.…”

Section: B Energy Models and Simulation Toolsmentioning

confidence: 99%

“…Dynamic energy consumption of PMs depends mainly on CPU utilization [21], [32]. Based on previous work on multicore servers [21], [34], we use a piecewise linear function to express the dependence of PM's dynamic energy consumption to its CPU utilization. This mapper function is instantiated with values taken from real energy measurements when fully loading the cores of a PM one by one.…”

Section: Pms Dynamic Energy Consumptionmentioning

confidence: 99%

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Estimating Energy Consumption of Cloud, Fog, and Edge Computing Infrastructures

Ahvar

Orgerie

Lèbre

2022

IEEE Trans. Sustain. Comput.

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In order to improve locality aspects, new Cloudrelated 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% 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 Cloudrelated architectures.

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Section: Pms Dynamic Energy Consumptionmentioning

confidence: 92%

Section: B Energy Models and Simulation Toolsmentioning

confidence: 99%

Section: Pms Dynamic Energy Consumptionmentioning

confidence: 99%

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Estimating Energy Consumption of Cloud, Fog, and Edge Computing Infrastructures

Ahvar

Orgerie

Lèbre

2022

IEEE Trans. Sustain. Comput.

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“…one physical CPU core). The influence of the number of core on the server' energy consumption has been widely studied in the literature [26]. For a given application that scales smoothly when adding cores, the relation between number of cores and power consumption is linear.…”

Section: B Cloud Energy Consumptionmentioning

confidence: 99%

Estimating the End-to-End Energy Consumption of Low-Bandwidth IoT Applications for WiFi Devices

Guegan

Orgerie

2019

2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

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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.

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“…SCORPIUS has been implemented on the SimGrid simulation toolkit [7]. This simulator embeds accurate energy consumption models [8], reliable TCP/IP network models [9] and fine-grain VM and live-migration abstractions [10]. The simulation-based evaluation shows that SCORPIUS outperforms classical greedy and round-robin allocation methods on production workload traces that comprise heterogeneous VMs.…”

Section: Introductionmentioning

confidence: 99%

Self-Consumption Optimization of Renewable Energy Production in Distributed Clouds

Camus

Blavette

Dufossé

et al. 2018

2018 IEEE International Conference on Cluster Computing (CLUSTER)

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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.

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Predicting the Energy-Consumption of MPI Applications at Scale Using Only a Single Node

Cited by 36 publications

References 26 publications

Estimating Energy Consumption of Cloud, Fog, and Edge Computing Infrastructures

Estimating Energy Consumption of Cloud, Fog, and Edge Computing Infrastructures

Estimating the End-to-End Energy Consumption of Low-Bandwidth IoT Applications for WiFi Devices

Self-Consumption Optimization of Renewable Energy Production in Distributed Clouds

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