300 research outputs found
Matching Renewable Energy Supply and Demand in Green Datacenters
In this paper, we propose GreenSlot, a scheduler for parallel batch jobs in a datacenter powered by a photovoltaic solar array and the electrical grid (as a backup). GreenSlot predicts the amount of solar energy that will be available in the near future, and schedules the workload to maximize the green energy consumption while meeting the jobs’ deadlines. If grid energy must be used to avoid deadline violations, the scheduler selects times when it is cheap. Our results for both scientific computing workloads and data processing workloads demonstrate that GreenSlot can increase solar energy consumption by up to 117% and decrease energy cost by up to 39%, compared to conventional schedulers. Based on these positive results, we conclude that green datacenters and green-energy-aware scheduling can have a significant role in building a more sustainable IT ecosystem
Matching Renewable Energy Supply and Demand in Green Datacenters
In this paper, we propose GreenSlot, a scheduler for parallel batch jobs in a datacenter powered by a photovoltaic solar array and the electrical grid (as a backup). GreenSlot predicts the amount of solar energy that will be available in the near future, and schedules the workload to maximize the green energy consumption while meeting the jobs’ deadlines. If grid energy must be used to avoid deadline violations, the scheduler selects times when it is cheap. Our results for both scientific computing workloads and data processing workloads demonstrate that GreenSlot can increase solar energy consumption by up to 117% and decrease energy cost by up to 39%, compared to conventional schedulers. Based on these positive results, we conclude that green datacenters and green-energy-aware scheduling can have a significant role in building a more sustainable IT ecosystem
Toward sustainable data centers: a comprehensive energy management strategy
Data centers are major contributors to the emission of carbon dioxide to the atmosphere, and this contribution is expected to increase in the following years. This has encouraged the development of techniques to reduce the energy consumption and the environmental footprint of data centers. Whereas some of these techniques have succeeded to reduce the energy consumption of the hardware equipment of data centers (including IT, cooling, and power supply systems), we claim that sustainable data centers will be only possible if the problem is faced by means of a holistic approach that includes not only the aforementioned techniques but also intelligent and unifying solutions that enable a synergistic and energy-aware management of data centers.
In this paper, we propose a comprehensive strategy to reduce the carbon footprint of data centers that uses the energy as a driver of their management procedures. In addition, we present a holistic management architecture for sustainable data centers that implements the aforementioned strategy, and we propose design guidelines to accomplish each step of the proposed strategy, referring to related achievements and enumerating the main challenges that must be still solved.Peer ReviewedPostprint (author's final draft
Climate policy costs of spatially unbalanced growth in electricity demand: the case of datacentres. ESRI Working Paper No. 657 March 2020
We investigate the power system implications of the anticipated expansion in electricity
demand by datacentres. We perform a joint optimisation of Generation and Transmission Expansion
Planning considering uncertainty in future datacentre growth under various climate policies.
Datacentre expansion imposes significant extra costs on the power system, even under the cheapest
policy option. A renewable energy target is more costly than a technology-neutral carbon reduction
policy, and the divergence in costs increases non-linearly in electricity demand. Moreover, a carbon
reduction policy is more robust to uncertainties in projected demand than a renewable policy. High
renewable targets crowd out other low-carbon options such as Carbon Capture and Sequestration.
The results suggest that energy policy should be reviewed to focus on technology-neutral carbon
reduction policies
Untangling Carbon-free Energy Attribution and Carbon Intensity Estimation for Carbon-aware Computing
Many organizations, including governments, utilities, and businesses, have
set ambitious targets to reduce carbon emissions as a part of their
sustainability goals. To achieve these targets, these organizations
increasingly use power purchase agreements (PPAs) to obtain renewable energy
credits, which they use to offset their ``brown'' energy consumption. However,
the details of these PPAs are often private and not shared with important
stakeholders, such as grid operators and carbon information services, who
monitor and report the grid's carbon emissions. This often results in incorrect
carbon accounting where the same renewable energy production could be factored
into grid carbon emission reports and also separately claimed by organizations
that own PPAs. Such ``double counting'' of renewable energy production could
lead to organizations with PPAs to understate their carbon emissions and
overstate their progress towards their sustainability goals. Further, we show
that commonly-used carbon reduction measures, such as load shifting, can have
the opposite effect of increasing emissions if such measures were to use
inaccurate carbon intensity signals. For instance, users may increase energy
consumption because the grid's carbon intensity appears low even though carbon
intensity may actually be high when renewable energy attributed to PPAs are
excluded. Unfortunately, there is currently no consensus on how to accurately
compute the grid's carbon intensity by properly accounting for PPAs. The goal
of our work is to shed quantitative light on the renewable energy attribution
problem and evaluate its impact of inaccurate accounting on carbon-aware
systems
Strategies for improving the sustainability of data centers via energy mix, energy conservation, and circular energy
Information and communication technologies (ICT) are increasingly permeating our daily life and we ever more commit our data to the cloud. Events like the COVID-19 pandemic put an exceptional burden upon ICT. This involves increasing implementation and use of data centers, which increased energy use and environmental impact. The scope of this work is to summarize the present situation on data centers as to environmental impact and opportunities for improvement. First, we introduce the topic, presenting estimated energy use and emissions. Then, we review proposed strategies for energy efficiency and conservation in data centers. Energy uses pertain to power distribution, ICT, and non-ICT equipment (e.g., cooling). Existing and prospected strategies and initiatives in these sectors are identified. Among key elements are innovative cooling techniques, natural resources, automation, low-power electronics, and equipment with extended thermal limits. Research perspectives are identified and estimates of improvement opportunities are mentioned. Finally, we present an overview on existing metrics, regulatory framework, and bodies concerned
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