Pricing data center demand response

Liu, Zhenhua; Liu, Iris; Low, Steven H.; Wierman, Adam

doi:10.1145/2591971.2592004

Cited by 99 publications

(26 citation statements)

References 42 publications

(57 reference statements)

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“…In practice, C i () is empirically measured by observing the system. According to principle of diminishing returns, as the unite amount of energy tenants save increase, the related cost will increase more and more fast [20], [21]. In reality, when tenants turn off the server, the assigning jobs to each working server increase.…”

Section: A Colocation Modelmentioning

confidence: 99%

Hierarchial Demand Response for Colocation Data Centers

Jin

Deng

2017

2017 IEEE International Conference on Smart Computing (SMARTCOMP)

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Abstract-Demand response is considered as a valuable functionality of the power grid and its potential impacts continue expanding with grid modernization. Colocation data centers (simply called colocation) are recognized as a notably promising resource for demand response due to their high power demand and remarkable potential in demand management. A major challenge of colocation demand response is the split incentive, that is, colocation operators desire demand response for financial compensation while tenants may not embrace demand response due to lack of incentives. To address this challenge, we study a hierarchical demand response scheme, where tenants within a colocation respond to the colocation operator while the operator interacts with the electric power company. We propose that twolevel marketing is suitable to this hierarchical scheme, and design a new market mechanism that results in a win-win situation for the operator and tenants. Specially, tenants who chooses to reduce power demand obtain financial rewards from the operator, while the operator receives financial compensation from the electric power company due to its tenants' demand reduction. An appealing feature of the mechanism is that it provably converges to a unique equilibrium solution. At the equilibrium, neither the operator or tenants can improve their individual economic performance by changing their own strategies. We evaluate the designed mechanism via detailed simulations and the results show the efficacy and validate the theoretical analysis for the mechanism.

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Section: A Colocation Modelmentioning

confidence: 99%

Hierarchial Demand Response for Colocation Data Centers

Jin

Deng

2017

2017 IEEE International Conference on Smart Computing (SMARTCOMP)

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“…The timeslot length is set to be 1 h. The simulation is carried out for a time scale of two days and the results are reported in the following section. The workload trace is taken from a commercial server farm owned by Facebook, obtained from [44] which have also been used earlier in [36]. The renewable energy trace and PUE are obtained from [44] and are also the ones used in [36].…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The workload trace is taken from a commercial server farm owned by Facebook, obtained from [44] which have also been used earlier in [36]. The renewable energy trace and PUE are obtained from [44] and are also the ones used in [36]. For the workload types, it is assumed that there are four different workload types.…”

Section: Performance Evaluationmentioning

confidence: 99%

Energy Aware Pricing in a Three-Tiered Cloud Service Market

2016

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Abstract:We consider a three-tiered cloud service market and propose an energy efficient pricing strategy in this market. Here, the end customers are served by the Software-as-a-Service (SaaS) providers, who implement customized services for their customers. To host these services, these SaaS providers, in turn, lease the infrastructure related resources from the Infrastructure-as-a-Service (IaaS) or Platform-as-a-Service (PaaS) providers. In this paper, we propose and evaluate a mechanism for pricing between SaaS providers and Iaas/PaaS providers and between SaaS providers and the end customers. The pricing scheme is designed in a way such that the integration of renewable energy is promoted, which is a very crucial aspect of energy efficiency. Thereafter, we propose a technique to strategically provide an improved Quality of Service (QoS) by deploying more resources than what is computed by the optimization procedure. This technique is based on the square root staffing law in queueing theory. We carry out numerical evaluations with real data traces on electricity price, renewable energy generation, workload, etc., in order to emulate the real dynamics of the cloud service market. We demonstrate that, under practical assumptions, the proposed technique can generate more profit for the service providers operating in the cloud service market.

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“…Flexibility of large industrial customers has already been extensively assessed and exploited by long standing programs (e.g., [6,7,8,9,10]). Since the inception of the smart grid, that industrial flexibility has increasingly been complemented by residential flexibility.…”

Section: Introductionmentioning

confidence: 99%

Modeling and analysis of residential flexibility: Timing of white good usage

et al. 2016

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Challenges that smart grids aim to address include the increasing fraction of supply by renewable energy sources, as well as plain rise of demand, e.g., by increased electrification of transportation. Part of the solution to these challenges lies in exploiting the opportunity to steer residential electricity consumption (e.g., for flattening the peak load or balancing the supply and demand in presence of the renewable energy production). To optimally exploit this opportunity, it is crucial to have insights on how flexible the residential demand is. Load flexibility is characterized by the amount of power, time of availability and duration of deferrable consumption. Residential flexibility however, is challenging to exploit due to the variation in types of customer loads and differences in appliance usage habits from one household to the other. Existing analyses of individual customer flexibility behavior in terms of timing are often based on inferences from surveys or customer load patterns (e.g., as observed through smart meter data): there is a high level of uncertainty about customer habits in offering the flexibility. Even though some of these studies rely on real world data, only few of them have quantitative data on actual flexible appliance usage, and none of them characterizes individual user behavior. In this paper, we address this gap and contribute with: (1) a new quantitative specification of flexibility, (2) two systematic methodologies for modeling individual customer behavior, (3) evaluation of the proposed models in terms of how accurately the data they generate corresponds with real world customer behavior, and (4) a basic analysis of factors influencing the flexibility behavior based on statistical tests. Experimental results for (2)-(4) are based on a unique dataset from a real-life field trial.

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Pricing data center demand response

Cited by 99 publications

References 42 publications

Hierarchial Demand Response for Colocation Data Centers

Hierarchial Demand Response for Colocation Data Centers

Energy Aware Pricing in a Three-Tiered Cloud Service Market

Modeling and analysis of residential flexibility: Timing of white good usage

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