Stochastic Programming for Cost Optimization in Geographically Distributed Internet Data Centers

Peng Wang; Yujie Cao; Zhaohao Ding; Honghai Tang; Xuanyuan Wang; Ming Cheng

doi:10.17775/CSEEJPES.2020.02930

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Stochastic Programming for Cost Optimization in Geographically Distributed Internet Data Centers

Regular Papers | 更新时间：2026-10-15

- Stochastic Programming for Cost Optimization in Geographically Distributed Internet Data Centers
- Stochastic Programming for Cost Optimization in Geographically Distributed Internet Data Centers
- 中国电机工程学会电力与能源系统学报(英文) 2022年8卷第4期页码：1215-1232
- 作者机构：
  
  1. Electrical and Electronic Engineering Department, North China Electric Power University,Beijing,China
  2. Beijing Electric Power Trading Center,Beijing,China
  3. Jibei Electric Power Trading Center,Beijing,China
  4. Alibaba Infrastructure Service,Hangzhou,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2020.02930
  中图分类号：
- 纸质出版：2022-07-01
- 稿件说明：
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Peng Wang, Yujie Cao, Zhaohao Ding, 等. Stochastic Programming for Cost Optimization in Geographically Distributed Internet Data Centers[J]. 中国电机工程学会电力与能源系统学报(英文), 2022,8(4):1215-1232.

Peng Wang, Yujie Cao, Zhaohao Ding, et al. Stochastic Programming for Cost Optimization in Geographically Distributed Internet Data Centers[J]. CSEE Journal of Power and Energy Systems, 2022, 8(4): 1215-1232.
Peng Wang, Yujie Cao, Zhaohao Ding, 等. Stochastic Programming for Cost Optimization in Geographically Distributed Internet Data Centers[J]. 中国电机工程学会电力与能源系统学报(英文), 2022,8(4):1215-1232. DOI： 10.17775/CSEEJPES.2020.02930.

Peng Wang, Yujie Cao, Zhaohao Ding, et al. Stochastic Programming for Cost Optimization in Geographically Distributed Internet Data Centers[J]. CSEE Journal of Power and Energy Systems, 2022, 8(4): 1215-1232. DOI： 10.17775/CSEEJPES.2020.02930.

摘要

Abstract

The development of cloud computing has accelerated the worldwide growth of internet data centers (IDCs). While a large portion of the energy consumption generated by intense computation introduces greater operation expenditures to the IDC enterprises. To manage the overall costs and utilize resources to their fullest extent

this paper introduces the concept of spatio-temporal workload allocation among the geographically distributed IDCs within a cloud

with the guarantee of the workload completion time and the consideration of computing service delay penalties by introducing the cost of inconvenience. Apart from the effort of the workload migration

the spatio-temporal variance of the renewable energies in the data center microgrids (DMGs) is fully considered in this paper. What's more

as the power consumed by the IDCs are primarily converted into heat

the waste heat recovery process is embedded in each IDC to demonstrate the effectiveness of the repurposed heat

which can be used by the residential heating demand in the thermal system

for total cost reduction and energy usage efficiency in the whole operating system. Applying real-life data traces of the electricity price

renewable energies and heating demand

these extensive evaluations demonstrate that both spatial and temporal complementary attempts on the supply side and demand side

along with power and thermal complementary efforts

can significantly reduce the overall cost for the IDC enterprise.

关键词

Keywords

references

2. P. Delforge. ( 2015, Feb. 06 ). NRDC: Americas data centers consuming and wasting growing amounts of energy [Online] . Available: https://www.nrdc.org/resources/americas-data-centers-consuming-and-wasting-growing-amounts-energy

3. Y. Sverdlik. ( 2018, Mar. 03 ). Data Center Knowledge. Hyperscale data center spend was up 20 percent in 2017, analysts say [Online ] . Available: https://www.datacenterknowledge.com/cloud/hyper-scale-data-center-spend-was-20-percent-2017-analysts-say.

4. California Energy Commission. California Solar Energy Statistics and Data , [Online ] . Available: https://ww2.energy.ca.gov/almanac/renew-ables_data/solar/index_cms.php.

5. J. S. Hill. ( 2017, Feb. 14 ). Clean Technica: New North American wind power record set in central United States [Online ] . Available: https://cleantechnica.com/2017/02/14/new-north-american-wind-power-record-set-central-united-states/.

22. M. Monroe. ( 2016, Mar. 10 ). Data Center Knowledge: How to reuse waste heat from data centers intelligently [Online ] . Available: https://www.datacenterknowledge.com/archives/2016/05/10/how-to-reuse-waste-heat-from-data-centers-intelligently/.

23. R. Miller. ( 2008, Apr. 03 ). Data Center Knowledge: Data center used to heat swimming pool [Online ] . Available: https://www.datacenterknow-ledge.com/archives/2008/04/02/data-center-used-to-heat-swimming-pool

24. DatacenterDynamics. ( 2016, Jan. 15 ). Data Centre Dynamics: Yandex data center heats Finnish city [Online ] . Available: https://www.datacent-erdyn amics.com/news/yandex-data-center-heats-finnish-city/.

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