A review on energy efficiency and demand response with focus on small and medium data centers

Vasques, Thiago Lara; Moura, Pedro; Almeida, Anı́bal T. de

doi:10.1007/s12053-018-9753-2

Cited by 68 publications

(33 citation statements)

References 118 publications

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“…In this paper, the blade type servers are considered among the different generalizations (e.g., blade, tower, and rack-able) since they contain similar basic hardware blocks, i.e, processors, memory, chipset, input/output (I/O) devices, storage, voltage regulators, and cooling systems (fans and heat sinks) [34], [35]. The power consumption of a server as a function of utilization is given in (2)…”

Section: B Power Consumption Models Of Load Sections 1) Server Power Consumptionmentioning

confidence: 99%

A Novel Reliability Index to Assess the Computational Resource Adequacy in Data Centers

2021

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The energy demand of data centers is increasing globally with the increasing demand for computational resources to ensure the quality of services. It is important to quantify the required resources to comply with the computational workloads at the rack-level. In this paper, a novel reliability index called loss of workload probability is presented to quantify the rack-level computational resource adequacy. The index defines the right-sizing of the rack-level computational resources that comply with the computational workloads, and the desired reliability level of the data center investor. The outage probability of the power supply units and the workload duration curve of servers are analyzed to define the loss of workload probability. The workload duration curve of the rack, hence, the power consumption of the servers is modeled as a function of server workloads. The server workloads are taken from a publicly available data set published by Google. The power consumption models of the major components of the internal power supply system are also presented which shows the power loss of the power distribution unit is the highest compared to the other components in the internal power supply system. The proposed reliability index and the power loss analysis could be used for rack-level computational resources expansion planning and ensures energy-efficient operation of the data center.INDEX TERMS Adequacy, data center, energy losses, internal power supply system, reliability.

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Section: B Power Consumption Models Of Load Sections 1) Server Power Consumptionmentioning

confidence: 99%

A Novel Reliability Index to Assess the Computational Resource Adequacy in Data Centers

2021

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“…An in depth field study of data center demand response by Lawrence Berkeley National Laboratories (LBNL) [20] concludes that postponing computational load is an important demand response strategy next to load migration, shutting down or idling IT equipment, adjusting cooling, and adjusting building properties like lighting. Several works have since investigated this flexibility [5,31,32,59,62] and have proposed solutions to exploit it [11-13, 16, 32, 64]. Existing literature also considers demand response in conjunction with local power generation [38] or focuses on directly forecasting energy flexibility [60].…”

Section: Demand Response In Data Centersmentioning

confidence: 99%

Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud

Wiesner,

Behnke,

Scheinert

et al. 2021

Preprint

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Depending on energy sources and demand, the carbon intensity of the public power grid fluctuates over time. Exploiting this variability is an important factor in reducing the emissions caused by data centers. However, regional differences in the availability of lowcarbon energy sources make it hard to provide general best practices for when to consume electricity. Moreover, existing research in this domain focuses mostly on carbon-aware workload migration across geo-distributed data centers, or addresses demand response purely from the perspective of power grid stability and costs.In this paper, we examine the potential impact of shifting computational workloads towards times where the energy supply is expected to be less carbon-intensive. To this end, we identify characteristics of delay-tolerant workloads and analyze the potential for temporal workload shifting in Germany, Great Britain, France, and California over the year 2020. Furthermore, we experimentally evaluate two workload shifting scenarios in a simulation to investigate the influence of time constraints, scheduling strategies, and the accuracy of carbon intensity forecasts. To accelerate research in the domain of carbon-aware computing and to support the evaluation of novel scheduling algorithms, our simulation framework and datasets are publicly available. CCS CONCEPTS• Social and professional topics → Sustainability; • Software and its engineering → Cloud computing.

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“…Temperature had a significant impact on the failure rates of the servers of data center [7]. According to the ASHRAE Technical Committee (TC) 9.9 research report, the servers of data center have different failure rates in different temperature ranges.…”

Section: Introductionmentioning

confidence: 99%

The impact of the operation mode of CRACs on the temperature distribution of hot and cold aisles for the data center room

et al. 2021

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Redundancy is generally considered in the computer room air conditioners (CRACs) of the data center room. The position of the CRAC in the on state and the operating air volume have a great influence on the temperature distribution. The temperature distribution of the data center room will affect the failure rate of the servers and the energy consumption of the air-conditioning system. Taking a typical data center room as an example, this paper uses numerical simulation to study the influence of the on/off state and air volume of CRACs on the temperature distribution of the hot and cold aisles and energy consumption. The calculation results show that we should preferably turn off the staggered CRACs on the opposite side and close to the middle position under the partial load. When the IT load is unevenly distributed, it is irrational to use the setting method of even distribution of the air volume of each CRAC. The air volume of each CRAC should be appropriately adjusted based on the distribution characteristic of IT load to reduce the energy consumption.

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A review on energy efficiency and demand response with focus on small and medium data centers

Cited by 68 publications

References 118 publications

A Novel Reliability Index to Assess the Computational Resource Adequacy in Data Centers

A Novel Reliability Index to Assess the Computational Resource Adequacy in Data Centers

Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud

The impact of the operation mode of CRACs on the temperature distribution of hot and cold aisles for the data center room

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