Scheduling Flexible Demand in Cloud Computing Spot Markets

Keller, Robert M.; Häfner, Lukas; Sachs, Thomas; Fridgen, Gilbert

doi:10.1007/s12599-019-00592-5

Cited by 16 publications

(11 citation statements)

References 62 publications

(87 reference statements)

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“…DSF can be principally differentiated between temporally or spatially utilization of flexibility Fridgen et al (2017). This differentiation is also possible for the flexibility of DCs (Keller et al, 2019). Temporal flexibility might significantly contribute to the stability of the power grid of the future (Schott et al, 2018).…”

Section: Summary and Policy Implicationsmentioning

confidence: 99%

Compensating balancing demand by spatial load migration – The case of geographically distributed data centers

et al. 2019

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The increasing share of renewables confronts existing power grids with a massive challenge, stemming from additional volatility to power grids introduced by renewable energy sources. This increases the demand for balancing mechanisms, which provide balancing power to ensure that power supply always meets with demand. However, the ability to provide cost-efficient and eco-friendly balancing power can vary significantly between locations. Fridgen et al. (2017) introduce an approach based on geographically distributed data centers, aiming at the spatial migration of balancing power demand between distant locations. Although their approach enables the migration of balancing demand to costefficient and/or eco-friendly balancing mechanisms, it will come up against limits if deployed on a global scale. In this paper, we extend Fridgen et al. (2017)'s approach by developing a model based on geographically distributed data centers, which not only enables the migration of balancing demand but also compensates for this migration when it is contradictory between different balancing power markets without burdening conventional balancing mechanisms. Using a simulation based on real-world data, we demonstrate the possibility to exploit the potential of compensation balancing demand offered by spatial load migration resulting in economic gains that will incentivize data center operators to apply our model.

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Section: Summary and Policy Implicationsmentioning

confidence: 99%

Compensating balancing demand by spatial load migration – The case of geographically distributed data centers

et al. 2019

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“…We are the first to introduce the approach of integrating RES-plants and DCs to increase the economic viability of RES-plants, and our evaluation illustrates that this approach is applicable in practice. Additionally, we contribute to IS research on energy flexibility, see e.g., Kahlen et al (2014), Fridgen et al (2016), and Keller et al (2020). Moreover and following Watson et al (2010), our IES provides the opportunity to use flow networks more efficiently as it is able to shift energy demand and, in this way, to supply local flexibility.…”

Section: Supplementary Informationmentioning

confidence: 86%

“…Such ways include, e.g., technical aspects of DCs redundancy architecture and workload prediction, approaches to use heat generated by the DC, or the participation of DCs in DRprograms (Kliazovich et al 2013;Klingert 2018;Shi et al 2016;Shuja et al 2012). The ability to take part in DRprograms is, of course, dependent on the level of flexibility the DC is able to provide with respect to Service Level Agreements (SLAs) deadlines (Keller et al 2020). Vieira et al (2015) distinguish between three levels of flexibility handling DC services requests: (i) ''fixed-time requests '' (no flexibility), (ii) ''floating-time requests '' (partly temporally flexible, e.g., interruptible), and (iii) ''variable-time requests'' (temporally flexible).…”

Section: Related Literature and Backgroundmentioning

confidence: 99%

“…Moreover, we note that SLAs may prevent the operator from idling the DC because of direct costs (due to SLA breaches) or indirect costs (due to lower customer satisfaction as a result of downtimes). However, regarding our evaluation, we consider the two use-cases to execute''variable-time requests'' that may be shifted temporally in line with Keller et al (2020) and Vieira et al (2015). In the following, we give some more details on the two exemplary use-cases.…”

Section: Machine Learning and Cryptocurrency Miningmentioning

confidence: 99%

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Not All Doom and Gloom: How Energy-Intensive and Temporally Flexible Data Center Applications May Actually Promote Renewable Energy Sources

et al. 2021

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To achieve a sustainable energy system, a further increase in electricity generation from renewable energy sources (RES) is imperative. However, the development and implementation of RES entail various challenges, e.g., dealing with grid stability issues due to RES’ intermittency. Correspondingly, increasingly volatile and even negative electricity prices question the economic viability of RES-plants. To address these challenges, this paper analyzes how the integration of an RES-plant and a computationally intensive, energy-consuming data center (DC) can promote investments in RES-plants. An optimization model is developed that calculates the net present value (NPV) of an integrated energy system (IES) comprising an RES-plant and a DC, where the DC may directly consume electricity from the RES-plant. To gain applicable knowledge, this paper evaluates the developed model by means of two use-cases with real-world data, namely AWS computing instances for training Machine Learning algorithms and Bitcoin mining as relevant DC applications. The results illustrate that for both cases the NPV of the IES compared to a stand-alone RES-plant increases, which may lead to a promotion of RES-plants. The evaluation also finds that the IES may be able to provide significant energy flexibility that can be used to stabilize the electricity grid. Finally, the IES may also help to reduce the carbon-footprint of new energy-intensive DC applications by directly consuming electricity from RES-plants.

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“…As a third possibility users could make a request for platform resources on the spot market (SM) [32,33], where machine capacity currently idle is made available by the provider to potential customers. The spot market is characterised by the spot price, determined by the platform considering supply and demand of service [34].…”

Section: Introductionmentioning

confidence: 99%

Pricing cloud IaaS computing services

Dimitri

2020

J Cloud Comp

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The economics of cloud computing has recently attracted increasing attention. In particular, a topic which is still under debate is how prices charged to customers for cloud resources are formed, since alternative pricing rules could be considered. Based on three pricing schemes inspired by those used by Amazon EC2, the main global cloud service provider, in the paper we address two main issues. First we present a methodology for the relevant parameters of the pricing rules to be determined in an optimal way, that is to maximise the provider's revenue. Moreover, we discuss reasons for co-existence of three pricing rules, rather than fewer, to access the cloud. Our findings suggest that this may be due to a larger coverage of the potential demand, since customers applying for cloud services vary in their willingness to pay for the job, the time length of the service, the computational power requested etc. Furthermore, the pricing rule in the so-called, spot market, can provide the platform with useful information on the customers willingness to pay for cloud services. This is because in the spot market users offer a price for service, but pay less than that if their request is satisfied.

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Scheduling Flexible Demand in Cloud Computing Spot Markets

Cited by 16 publications

References 62 publications

Compensating balancing demand by spatial load migration – The case of geographically distributed data centers

Compensating balancing demand by spatial load migration – The case of geographically distributed data centers

Not All Doom and Gloom: How Energy-Intensive and Temporally Flexible Data Center Applications May Actually Promote Renewable Energy Sources

Pricing cloud IaaS computing services

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