Online algorithms for geographical load balancing

By Minghong Lin, Zhenhua Liu, Adam Wierman, L. Andrew. 2012 International Green Computing Conference (IGCC), 2012

315 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Information Systems

View Paper: https://www.semanticscholar.org/paper/95d2683da0094fb9cc83da054c06ca113a09128a

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Citations in the corpus

Thinking fast and slow: Optimization decomposition across timescales (2017, background): “In general, [25] proves that AFHC is 1 + O(1/w) competitive for SOCO problems with costs bounded below by a positive constant c0.”
A Survey on Mobile Edge Computing: The Communication Perspective (2017, methodology): “2) Geographical Load Balancing for MEC: GLB is another key technique for green DCNs [189], [190], which leverages the spatial diversities of the workload patterns, temperatures, and electricity prices, to make workload routing decision among different data centers.”
Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis (2019, background): “Though this prediction model may be too optimistic in the short term and over pessimistic in the long term, this model i) captures a commonly observed phenomenon in predictions that short-term predictions are usually much more accurate than the long-term predictions; ii) allows researchers to derive”
Data Center Energy Consumption Modeling: A Survey (2016, background): “Similar work to this research can be observed in [319][320].”
Renewable and cooling aware workload management for sustainable data centers (2012, background): “Categories and Subject Descriptors C.0 [Computer Systems Organization]: General Keywords sustainable data center, renewable energy, demand shaping, scheduling, cooling optimization 1.”