The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Online algorithms for geographical load balancing
Open accessAbstract
It has recently been proposed that Internet energy costs, both monetary and environmental, can be reduced by exploiting temporal variations and shifting processing to data centers located in regions where energy currently has low cost. Lightly loaded data centers can then turn off surplus servers. This paper studies online algorithms for determining the number of servers to leave on in each data center, and then uses these algorithms to study the environmental potential of geographical load balancing (GLB). A commonly suggested algorithm for this setting is “receding horizon control” (RHC), which computes the provisioning for the current time by optimizing over a window of predicted future loads. We show that RHC performs well in a homogeneous setting, in which all servers can serve all jobs equally well; however, we also prove that differences in propagation delays, servers, and electricity prices can cause RHC perform badly, So, we introduce variants of RHC that are guaranteed to perform as well in the face of such heterogeneity. These algorithms are then used to study the feasibility of powering a continent-wide set of data centers mostly by renewable sources, and to understand what portfolio of renewable energy is most effective.
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Cites
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Power provisioning for a warehouse-sized computer
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Cutting the electric bill for internet-scale systems
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Power and performance management of virtualized computing environments via lookahead control
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Energy-aware server provisioning and load dispatching for connection-intensive internet services
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Greening geographical load balancing
Liu, Z., Lin, M., Wierman, A., Low, S. H., & Andrew, L. L. H. (2011) · Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems · doi:10.1145/1993744.1993767 · Cited by 467
Self-citationDynamic Right-Sizing for Power-Proportional Data Centers
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Self-citation
Cited by
A Survey on Mobile Edge Computing: The Communication Perspective
Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2745201 · Cited by 5,607
Data Center Energy Consumption Modeling: A Survey
Dayarathna, M., Wen, Y., & Fan, R. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2481183 · Cited by 1,165
(2018) · doi:10.1109/tcc.2016.2535201 · Cited by 39
Energy Efficient Indivisible Workload Distribution in Geographically Distributed Data Centers
Khalil, M. I. K., Ahmad, I., & Almazroi, A. A. (2019) · IEEE Access · doi:10.1109/access.2019.2924085 · Cited by 26
Distributed Online Learning of Fog Computing Under Nonuniform Device Cardinality
Ren, C., Lyu, X., Ni, W., Tian, H., & Liu, R. P. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2867481 · Cited by 22
Metadata
- Authors
- Lin, M., Liu, Z., Wierman, A., & Andrew, L. L. H.
- Venue
- 2012 International Green Computing Conference (IGCC)
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 1-10
- OpenAlex
- W2020196954
- Cited by
- 290
- References
- 26
- FWCI
- 24.00
- Type
- conference paper
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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