Cutting Down Electricity Cost in Internet Data Centers by Using Energy Storage

Guo, Yuanxiong; Ding, Zongrui; Fang, Yuguang; Wu, Dapeng

doi:10.1109/glocom.2011.6134209

Cited by 24 publications

(4 citation statements)

References 14 publications

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“…The GLB has been the subject of many studies in cloud data centers [16,17,[28][29][30][31]46]. Most research has been on determining the best approach to load balancing regardless of time, which has led to formulating an optimization problem that can be handled using various optimization methods.…”

Section: Glb and Geo-distributed Data Centersmentioning

confidence: 99%

Renewable-Aware Geographical Load Balancing Using Option Pricing for Energy Cost Minimization in Data Centers

et al. 2022

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It is becoming increasingly difficult to properly control the power consumption of widely dispersed data centers. Energy consumption is high because of the need to run these data centers (DCs) that handle incoming user requests. The rising cost of electricity at the data center is a contemporary problem for cloud service providers (CSPs). Recent studies show that geo-distributed data centers may share the load and save money using variable power prices and pricing derivatives in the wholesale electricity market. In this study, we evaluate the problem of reducing energy expenditures in geographically dispersed data centers while accounting for variable system dynamics, power price fluctuations, and renewable energy sources. We present a renewable energy-based load balancing employing an option pricing (RLB-Option) online algorithm based on a greedy approach for interactive task allocation to reduce energy costs. The basic idea of RLB-Option is to process incoming user requests using available renewable energy sources. In contrast, in the case of unprocessed user requests, the workload will be processed using brown energy or call option contract at each timeslot. We formulate the energy cost minimization in geo-distributed DCs as an optimization problem considering geographical load balancing, renewable energy, and an option pricing contract from the derivative market while satisfying the set of constraints. We prove that the RLB-Option can reduce the energy cost of the DCs close to that of the optimal offline algorithm with future information. Compared to standard workload allocation methods, RLB-Option shows considerable cost savings in experimental evaluations based on real-world data.

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Section: Glb and Geo-distributed Data Centersmentioning

confidence: 99%

Renewable-Aware Geographical Load Balancing Using Option Pricing for Energy Cost Minimization in Data Centers

et al. 2022

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“…Several studies explored the opportunity of energy cost-saving by routing jobs when/where the electricity prices were lower [16,17]. Some prior studies assumed that the electricity price variations and/or job arrivals follow certain stationary (although possibly unknown) distributions [18][19][20]. Rao et al [21] tackled the problem taking into account the spatial and time diversity in dynamic electricity markets.…”

Section: Related Workmentioning

confidence: 99%

Reducing the Operational Cost of Cloud Data Centers through Renewable Energy

Laganá¹,

Mastroianni²,

Meo

et al. 2018

Algorithms

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The success of cloud computing services has led to big computing infrastructures that are complex to manage and very costly to operate. In particular, power supply dominates the operational costs of big infrastructures, and several solutions have to be put in place to alleviate these operational costs and make the whole infrastructure more sustainable. In this paper, we investigate the case of a complex infrastructure composed of data centers (DCs) located in different geographical areas in which renewable energy generators are installed, co-located with the data centers, to reduce the amount of energy that must be purchased by the power grid. Since renewable energy generators are intermittent, the load management strategies of the infrastructure have to be adapted to the intermittent nature of the sources. In particular, we consider EcoMultiCloud , a load management strategy already proposed in the literature for multi-objective load management strategies, and we adapt it to the presence of renewable energy sources. Hence, cost reduction is achieved in the load allocation process, when virtual machines (VMs) are assigned to a data center of the considered infrastructure, by considering both energy cost variations and the presence of renewable energy production. Performance is analyzed for a specific infrastructure composed of four data centers. Results show that, despite being intermittent and highly variable, renewable energy can be effectively exploited in geographical data centers when a smart load allocation strategy is implemented. In addition, the results confirm that EcoMultiCloud is very flexible and is suited to the considered scenario.

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“…This is the sum of amortized capital and operating costs over a time horizon [17]. Queuing theory-based Lyapunov optimization techniques are also used to find near-optimal solution to minimize monthly electricity bills [1,18]. These optimization analyses typically focus on lead-acid battery storage, although lithium-ion batteries are also considered by some researchers.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of demand response in a datacenter with lithium-ion battery storage

Mamun

Narayanan

Wang

et al. 2016

Journal of Energy Storage

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This article optimizes lithium-ion battery management in a datacenter to: (i) maximize the dollar savings attainable through peak shaving, while (ii) minimizing battery degradation. To the best of the authors' knowledge, such multi-objective optimal datacenter battery management remains relatively unexplored. We solve this optimization problem using a second-order model of battery charge dynamics, coupled with a physics-based model of battery aging via solid electrolyte interphase (SEI) growth. Our optimization study focuses on a classical feedforward-feedback energy management policy, where feedforward control is used for peak shaving, and feedback is used for tracking a desired battery state of charge (SOC). Three feedforward-feedback architectures are examined: a proportional (P) control architecture, a proportionalintegral (PI) architecture, and a PI architecture with a deadband in its feedforward path. We optimize these architectures' parameters using differential evolution, for real datacenter power demand histories. Our results show a significant Pareto tradeoff between dollar savings and battery longevity for all architectures. The introduction of a deadband furnishes a more attractive Pareto front by allowing the feedforward controller to focus on shaving larger peaks. Moreover, the use of integral control improves the robustness of the feedback policy to demand uncertainties and battery pack sizing.

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Cutting Down Electricity Cost in Internet Data Centers by Using Energy Storage

Cited by 24 publications

References 14 publications

Renewable-Aware Geographical Load Balancing Using Option Pricing for Energy Cost Minimization in Data Centers

Renewable-Aware Geographical Load Balancing Using Option Pricing for Energy Cost Minimization in Data Centers

Reducing the Operational Cost of Cloud Data Centers through Renewable Energy

Multi-objective optimization of demand response in a datacenter with lithium-ion battery storage

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