Optimized Thermal-Aware Job Scheduling and Control of Data Centers

Damme, Tobias Van; Persis, Claudio De; Tesi, Pietro

doi:10.1016/j.ifacol.2017.08.1393

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…[ 48 , 51 , 55 ]. In [ 56 ], an optimization problem is proposed for thermal scheduling considering the optimal setpoints for the workload distribution and the temperature in the server room. Heat flow models are proposed for determining temperatures in case of a thermal aware workload scheduling policy, while a heat recirculation matrix is used to define the thermal influences between the servers [ 57 , 58 ].…”

Section: Related Workmentioning

confidence: 99%

“…In [ 12 ], a thermal aware consolidation mechanism is defined using a heat recirculation matrix and a set of bio-inspired algorithms that minimize overall DC energy consumption. Finally, in [ 56 ], the scheduling optimization problem is defined by considering the energy footprint reduction with thermal exchanges while incorporating both temperature and workload constraints.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers

Antal

Cristea²,

Pădurean

et al. 2021

Sensors

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Data centers consume lots of energy to execute their computational workload and generate heat that is mostly wasted. In this paper, we address this problem by considering heat reuse in the case of a distributed data center that features IT equipment (i.e., servers) installed in residential homes to be used as a primary source of heat. We propose a workload scheduling solution for distributed data centers based on a constraint satisfaction model to optimally allocate workload on servers to reach and maintain the desired home temperature setpoint by reusing residual heat. We have defined two models to correlate the heat demand with the amount of workload to be executed by the servers: a mathematical model derived from thermodynamic laws calibrated with monitored data and a machine learning model able to predict the amount of workload to be executed by a server to reach a desired ambient temperature setpoint. The proposed solution was validated using the monitored data of an operational distributed data center. The server heat and power demand mathematical model achieve a correlation accuracy of 11.98% while in the case of machine learning models, the best correlation accuracy of 4.74% is obtained for a Gradient Boosting Regressor algorithm. Also, our solution manages to distribute the workload so that the temperature setpoint is met in a reasonable time, while the server power demand is accurately following the heat demand.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers

Antal

Cristea²,

Pădurean

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Mhedheb and Streit [16] have considered thermal-aware VM management in Cloud datacenters to minimize energy using migration techniques. Van Damme et al [36] have characterized the optimal workload distribution in a datacenter using KKT conditions with a thermal constraint.…”

Section: Thermal-aware Scheduling Heuristicsmentioning

confidence: 99%

MILP formulations for spatio-temporal thermal-aware scheduling in Cloud and HPC datacenters

et al. 2019

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This paper focuses on scheduling problems related to the execution of computational jobs in datacenters with thermal constraints. Mixed integer linear programming (MILP) formulations are proposed that encompass both spatial and temporal aspects of the temperature evolution under a unified model. This model takes into account the dynamics of heat production and dissipation in order to schedule jobs at appropriate times on appropriate machines. The proposed MILP formulations are applicable to both high-performance computing (HPC) and Cloud settings, and can target several objectives including energy and makespan minimization, while incorporating the cooling costs and dynamic voltage and frequency scaling capabilities of servers. The applicability and usefulness of our formulations are demonstrated via several HPC and Cloud case-studies.

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Optimized Thermal-Aware Job Scheduling and Control of Data Centers

Damme

Persis

Tesi

2019

IEEE Trans. Contr. Syst. Technol.

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Abstract-Analyzing data centers with thermal-aware optimization techniques is a viable approach to reduce energy consumption of data centers. By taking into account thermal consequences of job placements among the servers of a data center, it is possible to reduce the amount of cooling necessary to keep the servers below a given safe temperature threshold. We set up an optimization problem to analyze and characterize the optimal setpoints for the workload distribution and the supply temperature of the cooling equipment. Furthermore under mild assumptions we design and analyze controllers that drive the data center to the optimal state without knowledge of the current total workload to be handled by the data center. The response of our controller is validated by simulations and convergence to the optimal setpoints is achieved under varying workload conditions.

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Optimized Thermal-Aware Job Scheduling and Control of Data Centers

Cited by 6 publications

References 18 publications

Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers

Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers

MILP formulations for spatio-temporal thermal-aware scheduling in Cloud and HPC datacenters

Optimized Thermal-Aware Job Scheduling and Control of Data Centers

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