A cyber-physical systems approach to energy management in data centers

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

doi:10.1145/1795194.1795218

Cited by 69 publications

(43 citation statements)

References 21 publications

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“…We further study thermal-aware server and workload consolidation solutions to optimize the data centers' total energy consumption and ensure avoiding cooling computing power tradeoff. The existing thermal-aware scheduling algorithms for Internet data centers are heuristic in the sense that they are either based on simulation studies or do not provide guarantee on their optimality and avoiding cooling-computing power tradeoff [44,94,104].…”

Section: Application Of the Solutionsmentioning

confidence: 99%

“…Sharma et al, introduced thermal load balancing and showed that dynamic thermal management based upon asymmetric workload placement can promote uniform temperature distribution and reduce cooling energy [104]. Parolini et al, provided analytical formulation to manage the workload distribution among servers which relies on the expected value of the traffic over time [94]. Thermal-aware server provisioning for IDCs is also studied by Faraz and Vijaykumar.…”

Section: Thermal-aware Schedulingmentioning

confidence: 99%

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Sustainable Cloud Computing

Domdouzis

2015

Green Information Technology

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Energy consumption of the data centers worldwide is rapidly growing fueled by everincreasing demand for Cloud computing applications ranging from social networking to e-commerce. Understandably, ensuring energy-efficiency and sustainability of Cloud data centers without compromising performance is important for both economic and environmental reasons. In order to achieve these objectives, this dissertation develops a cyber-physical multi-tier server and workload management architecture which operates at the local and the global (geo-distributed) data center level.This dissertation devises optimization frameworks for each tier to optimize energy consumption, energy cost and carbon footprint of the data centers. The proposed solutions are aware of various energy management tradeoffs that manifest due to the cyber-physical interactions in data centers, while providing provable guarantee on the solutions' computation efficiency and energy/cost efficiency. The local data center level energy management takes into account the impact of server consolidation on the cooling energy, avoids cooling-computing power tradeoff, and optimizes the total energy (computing and cooling energy) considering the data centers' technology trends (servers' power proportionality and the cooling power efficiency). The global data center level cost management explores the diversity of the data centers to minimize the utility cost while satisfying the carbon cap requirement of the Cloud and while dealing with the adversity of the prediction error on the data center parameters. Finally, the synergy of the local and the global data center energy and cost optimization is shown to help towards achieving carbon neutrality (net-zero) in a cost efficient manner.

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Section: Application Of the Solutionsmentioning

confidence: 99%

Section: Thermal-aware Schedulingmentioning

confidence: 99%

Sustainable Cloud Computing

Domdouzis

2015

Green Information Technology

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“…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%

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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“…From an implementation viewpoint, the solution operates either locally (chip, server, etc.) [1], [14], [21] or globally (cluster, data center) [17], [10], [18], [19], [7], [16].…”

Section: Prior Workmentioning

confidence: 99%

Dynamic power allocation in server farms: A Real Time Optimization approach

Aghajani

Parolini

Sinopoli

2010

49th IEEE Conference on Decision and Control (CDC)

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Abstract-This paper concerns the power minimization problem in server farms. The power minimization problem over dynamic power allocation schemes is formally defined and formulated as an optimization problem. It is shown that finding the optimal solution for this optimization problem is not feasible. Inspired by control theory, a well-established method to optimize a cost function over the constraints imposed by the evolution of a dynamical system, called Real-Time Optimization (RTO), is invoked to find a sub-optimal solution for the power minimization problem. The obtained algorithm is simulated and compared with the state-of-the-art optimal static power allocation solution. A considerable improvement in energy consumption is attained for the same quality of service (QoS) level, when dynamic power allocation is used.

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A cyber-physical systems approach to energy management in data centers

Cited by 69 publications

References 21 publications

Sustainable Cloud Computing

Sustainable Cloud Computing

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

Dynamic power allocation in server farms: A Real Time Optimization approach

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