Dynamic data center load response to variability in private and public electricity costs
Abstract
This paper assesses the potential cost-saving incentives for content distribution networks to shift traffic load among geographically distributed data centers in response to hourly variation in electricity prices. Such incentives are likely aligned with benefits to utilities and grid operators, which might take the form of peak-shaving or ancillary services. However, private cost savings are not strictly aligned with public benefits related to the avoidance of health and environmental damages from power plant emissions, so we compare private cost minimization with a strategy that minimizes these externalities. We find that feasible strategies exist to simultaneously realize public and private benefits and that load shifting can result in substantial cost savings and avoided damages in some circumstances. Concerns over increased latency and bandwidth costs can be mitigated with modifications to the model. However, the level of realized savings is dependent upon the specifics of a particular network operator and electricity rate schedule.
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Online algorithms for optimizing demand response can reduce overall power usage for computer servers by dynamically shifting the internet traffic load of data providers to underutilized servers, although most of this research has focused on minimizing costs rather than GHG emissions.
“Online algorithms for optimizing demand response can reduce overall power usage for computer servers by dynamically shifting the internet traffic load of data providers to underutilized servers, although most of this research, again, has focused on minimizing costs rather than GHG emissions [99,352].”
From Tackling climate change with machine learning (2023) · cites this paper as [352]
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Tackling climate change with machine learning
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- Authors
- Horner, N., Azevedo, I., Sicker, D., & Agarwal, Y.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm)
- Publisher
- IEEE
- Published
- 2016-11
- Pages
- 80-85
- OpenAlex
- W2562427903
- Cited by
- 7
- References
- 18
- Citations by year
- 2022: 2 · 2019: 1 · 2018: 2 · 2017: 2
- FWCI
- 2.35
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Smart Grid Communications (SmartGridComm) (publishedVersion)
- Funders
- National Science Foundation
- Carnegie Mellon University
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- electricity price variability, content delivery networks, peak shaving, ancillary services, externalities, power plant emissions, latency, bandwidth cost, cost minimization
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