Using Model Predictive Control in Data Centers for Dynamic Server Provisioning

Fang, Qiu; Wang, Jun; Zhu, Han; Gong, Qi

doi:10.3182/20140824-6-za-1003.00482

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…Thermal aware task scheduling approaches to adjust CPU frequency based on Dynamic Voltage Frequency Scaling [ 61 ] are used to manage the energy demand and heat generation [ 62 , 63 , 64 ]. In [ 65 ], the authors build a steady and dynamical thermal interaction model of the DC. Based on these models, a task assignment and frequency optimization are performed in the first optimization stage, while the second stage uses a model predictive control (MPC) to represent the optimization problems that aim to minimize the cooling system power demand.…”

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

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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%

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

Antal

Cristea²,

Pădurean

et al. 2021

Sensors

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show abstract

“…Then again, MPC optimizes on the next sliding window, repeatedly. A linear constrained model predictive control approach is applied in [10] to a coordinated dynamic server provisioning and thermal dynamics for energy control of data centers. In [11], it is applied to critical time-sensitive cyber-physical systems, taking advantage of cloud and edge computing.…”

Section: Related Workmentioning

confidence: 99%

“…Content may change prior to final publication. (10) and under the assumption that the error e(k) can be measured at step k.…”

Section: B Three Control Strategiesmentioning

confidence: 99%

“…The problem with Reactive Control is that there is always a mismatch between the energy requested and the energy provided in the same period. In order to improve this, it is possible to take into account the accumulated error given by the amount of non-served requests in (10). Thus, an improved reactive energy provisioning is as follows:…”

Section: ) Reactive Feedback Controlmentioning

confidence: 99%

“…Then, to analyze QoS and energy waste for different dispatch periods T , simulations are performed using multi-sampling. A small period of 10 s is used to take into account the time-distribution of the arrival of demand in the balance equation (10) and (11). The provisioned energy u(kT ) is dispatched each period T = nT s , where n is an integer number of small periods periods.…”

Section: B Energy Error and Management Effortmentioning

confidence: 99%

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Proactive Data Center Management Using Predictive Approaches

et al. 2020

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Data Center (DC) management aims at promptly serving user requests while minimizing the energy consumed. This is achieved by turning off unnecessary servers to save energy and adapting the number of servers that are on to the time-varying and heterogeneous user requests. A great change in the number of servers on leads to a considerable management effort, also called control effort in the literature, which should be reduced as much as possible. Since feedback control can improve the performance of computing systems and networks, we propose to use it to achieve this dynamic capacity provisioning of the DC. In order to design this feedback control, first, we developed a dynamic model of the DC. The purpose of this paper is to design a feedback control strategy based on the DC model, able to optimize i) the Quality of Service, ii) the energy consumed and iii) the management effort. A simple Reactive open-loop Control which provides an amount of energy equal to the amount requested in the previous time interval is considered as a benchmark for comparison. Second, two feedback controls based on the balance equations of the DC are studied, namely i) Reactive Feedback Control providing an amount of energy equal to that provided by the reactive open-loop control but adding the accumulated demand that has not yet been served, and ii) Model Predictive Control optimizing a constrained cost that weights the management effort and the prediction error. Reactive Control, Reactive Feedback Control and Model Predictive Control are compared in terms of energy consumed, energy error and management effort. Quantitative results of the comparative performance evaluation are given, based on a data set collected from a real DC.

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