Energy Model for Low-Power Cluster

Flórez, Edson; Barrios, Carlos; Emeras, Joseph; Pecero, Johnatan E.

doi:10.1109/ccgrid.2017.22

Cited by 2 publications

(4 citation statements)

References 16 publications

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“…Florez, Pecero, Emeras, and Barrios, 2017, [14] • The authors used an ARM-built cluster, called a millicluster, designed to provide high energy output at low power. A model was developed for estimating energy consumption founded on experimental findings, derived from measurements carried out during a benchmarking process representative of a real-life workload.…”

Section: Reference Objectivesmentioning

confidence: 99%

“…In general, there are ways to boost energy efficiency at the level of HPC site infrastructures; these include [12]: (i) Reduction of electrical losses in wires during transformation; (ii) advanced cooling technologies; and (3) waste heat reuse. Reference [14], proposed that energy consumption could be estimated using an empirical energy model, calculating each processor's energy consumption at periodic intervals. To predict any application of the broad range of energy-consuming HPC applications, you can build models using the decision trees method; it automatically picks the best suitable model for the running workload.…”

Section: Hpc and Energy Efficiencymentioning

confidence: 99%

“…Data collection, preprocessing, division of dataset, forecasting model selection; were methodologies deployed to obtained the models. Reference [14], envisaged that future research directions are; the adaptation of the current energy model to an algorithm for job planning, maximizing energy usage, analyzing several performance metrics concurrently, and comparing energy efficiency with other HPC energy management technology systems in ARM-based mili-cluster job schedules. References [14,18], indicated that the energy and power management tools available could be classified into two domains: tracking and managing.…”

Section: Hpc and Energy Efficiencymentioning

confidence: 99%

“…Currently, energy efficiency (EE) is a critical problem for HPC sustainable development and has been addressed from different perspectives such as; network, input, output, and resource organization. The industry has concentrated on built-in and low-power computing infrastructures using reduced instruction set computing (RISC) processors for an increase in EE [14,15]. HPC centers use a massive volume of power to operate powerful computers and infrastructures required to cool [16].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing

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Power-consuming entities such as high performance computing (HPC) sites and large data centers are growing with the advance in information technology. In business, HPC is used to enhance the product delivery time, reduce the production cost, and decrease the time it takes to develop a new product. Today’s high level of computing power from supercomputers comes at the expense of consuming large amounts of electric power. It is necessary to consider reducing the energy required by the computing systems and the resources needed to operate these computing systems to minimize the energy utilized by HPC entities. The database could improve system energy efficiency by sampling all the components’ power consumption at regular intervals and the information contained in a database. The information stored in the database will serve as input data for energy-efficiency optimization. More so, device workload information and different usage metrics are stored in the database. There has been strong momentum in the area of artificial intelligence (AI) as a tool for optimizing and processing automation by leveraging on already existing information. This paper discusses ideas for improving energy efficiency for HPC using AI.

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Section: Reference Objectivesmentioning

confidence: 99%

Section: Hpc and Energy Efficiencymentioning

confidence: 99%

Section: Hpc and Energy Efficiencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Overview of Digital Transformation and Environmental Sustainability: Threats, Opportunities, and Solutions

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2024

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Digital transformation, powered by technologies like AI, IoT, and big data, is reshaping industries and societies at an unprecedented pace. While these innovations promise smarter energy management, precision agriculture, and efficient resource utilization, they also introduce serious environmental challenges. This paper examines the dual impact of digital technologies, highlighting key threats such as rising energy consumption, growing e-waste, and the increased extraction of raw materials. By synthesizing the existing literature, this study highlights mitigation strategies that include adopting energy-efficient practices, integrating renewable energy, and implementing circular economy principles. It emphasizes the need for a balanced approach—making the most of technological advances while protecting the environment. By identifying gaps in the current research, this paper also suggests future areas to explore to ensure that digital progress does not come at the expense of our planet. This review advocates for an integrated strategy to achieve sustainable digital growth aligned with global climate goals.

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Energy Model for Low-Power Cluster

Cited by 2 publications

References 16 publications

Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing

Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing

An Overview of Digital Transformation and Environmental Sustainability: Threats, Opportunities, and Solutions

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