Thermal-aware workload scheduling for energy efficient data centers

Vasić, Nedeljko; Scherer, Thomas; Schott, W.

doi:10.1145/1809049.1809076

Cited by 24 publications

(29 citation statements)

References 12 publications

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“…Our trace analysis reveals that the average peak duration is less than 2 hours long, implying that alternative power sources can supply necessary power during these periods [20]. Moreover, existing thermodynamic models like [38] can estimate how long the peak utilization can be accommodated without extra cooling, while keeping the temperature at desired levels.…”

Section: Deployment Discussionmentioning

confidence: 99%

Identifying and using energy-critical paths

Vasić

Bhurat

Novaković

et al. 2011

Proceedings of the Seventh COnference on Emerging Networking EXperiments and Technologies

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The power consumption of the Internet and datacenter networks is already significant, and threatens to shortly hit the power delivery limits while the hardware is trying to sustain ever-increasing traffic requirements. Existing energyreduction approaches in this domain advocate recomputing network configuration with each substantial change in demand. Unfortunately, computing the minimum network subset is computationally hard and does not scale. Thus, the network is forced to operate with diminished performance during the recomputation periods. In this paper, we propose REsPoNse, a framework which overcomes the optimalityscalability trade-off. The insight in REsPoNse is to identify a few energy-critical paths off-line, install them into network elements, and use a simple online element to redirect the traffic in a way that enables large parts of the network to enter a low-power state. We evaluate REsPoNse with real network data and demonstrate that it achieves the same energy savings as the existing approaches, with marginal impact on network scalability and application performance.

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Section: Deployment Discussionmentioning

confidence: 99%

Identifying and using energy-critical paths

Vasić

Bhurat

Novaković

et al. 2011

Proceedings of the Seventh COnference on Emerging Networking EXperiments and Technologies

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“…A joint optimization that balances the number of active hosts to trade off the power needed by the cooling subsystem and the idle power needed by the IT equipment can reduce the total power consumption of the data center [12]. As the cooling subsystem could require some time to adapt changing conditions, joint approaches to optimize the cooling subsystem and the IT equipment can configure the cooling subsystem over different operating points to deal with long-term fluctuations and use thermal-aware workload placement to deal with short-term ones [69].…”

Section: Energy Efficiency Of the Cooling And Power Supply Subsystemsmentioning

confidence: 99%

Toward sustainable data centers: a comprehensive energy management strategy

Guitart

2016

Computing

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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.

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“…There are various parameters (e.g., temperature and utilization) monitored for each server. This data is required by data center infrastructure management (DCIM) [1] tools and workload scheduling systems [2] to achieve energy efficient and proficient utilization on data center servers. Apart from the idle energy consumption, the total energy consumed and dissipated as heat by each server is increased for every utilization level increment and vice versa.…”

Section: Introductionmentioning

confidence: 99%

Thermal Prediction Models for Virtualized Data center Servers by using Thermal-profiles

Chaudhry

Chon

Ling

et al. 2016

MJCS

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The energy dissipated as heat for each utilization level of a data center server is empirically measured and stored as the thermal-profile. These thermal-profiles are used to predict the outlet temperatures of the related servers for current and future utilization. The predicted outlet temperature is an important parameter for energy efficient thermal-aware workload scheduling and workload migration in green data centers. This paper presents three models for outlet temperature prediction on virtualized data center servers based on thermal-profile. The best case scenario managed to predict the outlet temperature with a negligible error of 0.3 degree Celsius.Keywords: data center; servers; monitoring; virtualization; thermal-profile; thermal-prediction; INTRODUCTIONMonitoring systems in data centers look-over the environment and performance of tens and hundreds of servers periodically. There are various parameters (e.g., temperature and utilization) monitored for each server. This data is required by data center infrastructure management (DCIM) [1] tools and workload scheduling systems [2] to achieve energy efficient and proficient utilization on data center servers. Apart from the idle energy consumption, the total energy consumed and dissipated as heat by each server is increased for every utilization level increment and vice versa. The energy usage of a virtualized server involves the virtualized instances of operating systems called virtual machines (VMs). From now on, the word server is used interchangeably with virtualized server.As long as the hardware configuration of a server is not altered, each heterogeneous server will dissipate a different but certain amount of heat for each discrete utilization level. This symbolic heat is empirically measured and stored as a thermal-profile for each server. The thermal-profile is used to predict the outlet temperature of a server by a given value of CPU usage through thermal-prediction modeling. A thermal prediction model eliminates at least one of the parameters e.g., outlet temperature, from the data center monitoring system without the loss of performance and accuracy. Similarly, the number of thermal sensors used for outlet temperature monitoring is reduced. The ability of a monitoring system to generate accurate thermal predictions offline, makes the prediction to become an essential parameter for thermal-aware workload scheduling and thermal-aware workload migration for load balancing.Based upon thermal-profile, this paper presents multiple prediction models to predict outlet temperature of data center servers along with a matrix comparison among these models. The test results show that the outlet

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Thermal-aware workload scheduling for energy efficient data centers

Cited by 24 publications

References 12 publications

Identifying and using energy-critical paths

Identifying and using energy-critical paths

Toward sustainable data centers: a comprehensive energy management strategy

Thermal Prediction Models for Virtualized Data center Servers by using Thermal-profiles

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