Model Predictive Control of Data Centers in the Smart Grid Scenario

Parolini, Luca; Sinopoli, Bruno; Krogh, Bruce H.

doi:10.3182/20110828-6-it-1002.03237

Cited by 11 publications

(8 citation statements)

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“…in this direction can be found in [45], [47]. Data centers can play an important role in the smart grid because of their high power consumption density.…”

Section: Discussionmentioning

confidence: 99%

“…Tang et al [46] discuss a control algorithm that allocates the workload among servers so as to minimize their peak inlet temperatures. Parolini et al [47], [48] consider a data center as a node of the smartgrid, where time-varying and power-consumption-dependent electricity price information is used to manage data center operations.…”

Section: Data Center Level Controlmentioning

confidence: 99%

“…The model for the workload execution rates developed above is sufficient for our purposes, but we note that it can be extended to include different migration policies, workload classes, hardware requirements, and interactions among different types of workloads [47], [48], [56].…”

Section: A Computational Networkmentioning

confidence: 99%

“…In such a case, the goal of the control becomes the search of the best tradeoff between minimizing the cost of powering the data center and maximizing the profit induced by executing the workload. Initial results in this direction can be found in [47]. The thermal constraints on IT devices are formulated in terms of upper bounds on the input temperature of the thermal nodes…”

Section: B Thermal Networkmentioning

confidence: 99%

“…The coordinated control strategy is based on a discrete-time MPC approach and it manages the IT and the CT resources in a single optimization problem. Under mild assumptions, the coordinated controller is able to guarantee the enforcement of both the QoS and the thermal constraints [47]. The sets M, S, D, and T ref are defined as in the uncoordinated controller case.…”

Section: B Thermal Networkmentioning

confidence: 99%

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A Cyber–Physical Systems Approach to Data Center Modeling and Control for Energy Efficiency

et al. 2012

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Abstract-This paper presents data centers from a cyberphysical system (CPS) perspective. Current methods for controlling information technology (IT) and cooling technology (CT) in data centers are classified according to the degree to which they take into account both cyber and physical considerations. To evaluate the potential impact of coordinated CPS strategies at the data-center level, we introduce a control-oriented model that represents the data center as two coupled networks: a computational network representing the cyber dynamics and a thermal network representing the physical dynamics. These networks are coupled through the influence of the IT on both networks: servers affect both the quality of service (QoS) delivered by the computational network and the generation of heat in the thermal network. Using this model, three control strategies are evaluated with respect to their energy efficiency and computational performance: a baseline strategy that ignores CPS considerations, an uncoordinated strategy that manages the IT and CT independently, and a coordinated strategy that manages the IT and CT together to achieve optimal performance with respect to both QoS and energy efficiency. Simulation results show that the benefits to be realized from coordinating the control of IT and CT depend on the distribution and heterogeneity of the computational and cooling resources throughout the data center. A new cyber-physical index (CPI) is introduced as a measure of this combined distribution of cyber and physical effects in a given data center. We illustrate how the CPI indicates the potential impact of using coordinated CPS control strategies.

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“…in this direction can be found in [45], [47]. Data centers can play an important role in the smart grid because of their high power consumption density.…”

Section: Discussionmentioning

confidence: 99%

Section: Data Center Level Controlmentioning

confidence: 99%