Minimizing Thermal Stress for Data Center Servers through Thermal-Aware Relocation

Chaudhry, Muhammad Tayyab; Ling, Teck Chaw; Hussain, Syed Asad; Manzoor, Atif

doi:10.1155/2014/684501

Cited by 12 publications

(10 citation statements)

References 21 publications

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“…5 There are many factors such as impaired cerebral autoregulation, disruption to the blood-brain barrier (BBB), oxidative stress, and direct inflammatory effects of free radicals and cytokines on glial cells, may contribute to the pathophysiology of sepsisinduced encephalopathy. 10 The mechanism by which these cytokines reach the brain to make damage is still uncertain. Cytokines cross BBB causing its damage and lead to increased permeability.…”

Section: Discussionmentioning

confidence: 99%

Efficacy of Cranial Doppler ultrasound in Diagnosis of Early-onset neonatal sepsis

Elheity

Abdelhakeem

Ahmed

et al. 2021

Al-Azhar International Medical Journal

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Background: Neonatal sepsis is considered a major neonatal issue and associated with a high rate of mortality in newborns. Because there is a lack of studies concerning early hemodynamic changes of cerebral circulation in neonatal sepsis, so our research aims to assess the cerebral blood flow (CBF) changes in newborns with early onset neonatal sepsis (EONS) and to clarify the role of cranial Doppler Ultrasound in EONS diagnosis. Aim of The Work:To evaluate cerebral hemodynamic changes using transcranial Doppler in newborns with EONS. Patient and Methods: This cross sectional study with 50 neonates involved in the study. The neonates were classified into 2 groups: Case Group (25 neonates) with a diagnosis of early-onset neonatal sepsis and control group (25 neonates) without clinical manifestations of sepsis. Examination of all neonates using Transcranial Doppler (TCD) was performed within seventy-two hours after birth. Measurement of cerebral blood flow velocity (CBFV) in two major cerebral vessels: The anterior cerebral artery (ACA) and middle cerebral artery (MCA) with documentation of doppler data: The peak systolic velocity (PSV), pulsatility index (PI), and resistive index (RI). Data were statistically analyzed followed by calculation of specificity, sensitivity, positive and negative predictive value at selected cut-off values of CBFV parameters. Results: Sepsis group showed statistically significant changes in doppler indices with increased PSV and deceased PI and RI in both arteries. Conclusion:The present study demonstrates changes in CBFV measured by transcranial Doppler in newborns with EONS which can be used as noninvasive bedside investigation.

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Section: Discussionmentioning

confidence: 99%

Efficacy of Cranial Doppler ultrasound in Diagnosis of Early-onset neonatal sepsis

Elheity

Abdelhakeem

Ahmed

et al. 2021

Al-Azhar International Medical Journal

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“…The proposed thermal model lacked of linkage between the power consumption, server utilization level and the resultant heat. A basic thermal-profiling was performed in [6] to analyze the data center servers for thermalstress with reference to inlet temperature change. The hotspots were predicted with the help of thermal-profiles of the servers.…”

Section: Related Workmentioning

confidence: 99%

“…Thermal-profiles are created by using customized scripts (i.e., thermal-benchmark) to manipulate multiple VM [6]. Thermal-benchmarks for data center servers are used to create thermal-profiles by manipulating the serverutilization as shown in Fig.…”

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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“…Heat recirculation is often linked to the increase in inlet temperature of the servers. The factors contributing to the increase in inlet temperature in addition to heat recirculation are the nonefficient placement of servers and server racks [Chaudhry et al 2014], air flow across servers [Ahuja et al 2013], thermal sensors' placement [Xiaodong et al 2013], or a fault in the cooling mechanism [Bo et al 2011;Junmei et al 2013]. The presence of heat-recirculation-based server grading may result in permanent underutilization or nonutilization of some servers.…”

Section: Heat Recirculationmentioning

confidence: 99%

“…The increased PUE and the monetary losses result in increased TCO over time. The outlet temperature of the server is also dependent on the inlet air temperature as per Equation (2) and as demonstrated by Rodero et al [2012] and Chaudhry et al [2014]. Thus, the hotspots can arise in a region of high inlet temperatures and a task scheduler needs to be thermal aware to lower the chances of hotspots.…”

Section: Thermal-aware Schedulingmentioning

confidence: 99%

Thermal-Aware Scheduling in Green Data Centers

et al. 2015

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Data centers can go green by saving electricity in two major areas: computing and cooling. Servers in data centers require a constant supply of cold air from on-site cooling mechanisms for reliability. An increased computational load makes servers dissipate more power as heat and eventually amplifies the cooling load. In thermal-aware scheduling, computations are scheduled with the objective of reducing the data-center-wide thermal gradient, hotspots, and cooling magnitude. Complemented by heat modeling and thermal-aware monitoring and profiling, this scheduling is energy efficient and economical. A survey is presented henceforth of thermal-ware scheduling and associated techniques for green data centers.

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Minimizing Thermal Stress for Data Center Servers through Thermal-Aware Relocation

Cited by 12 publications

References 21 publications

Efficacy of Cranial Doppler ultrasound in Diagnosis of Early-onset neonatal sepsis

Efficacy of Cranial Doppler ultrasound in Diagnosis of Early-onset neonatal sepsis

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

Thermal-Aware Scheduling in Green Data Centers

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