Over the past few years, energy provisioning in server farms and data-centres has become an active area of research. As such, many models have been proposed where an individual server has setup times and can switch between two different energy states (on and off). To make such models tractable, assumptions are usually made on the type of policies the system can implement. However, it is often not known if such assumptions allow for the model to capture the optimal policy, or if such a model will be strictly suboptimal. In this work we model such systems using Markov Decision Processes (MDPs) and derive several structural properties which (partially) describe the optimal policy. These properties reduce the set of feasible policies significantly, allowing one to describe the optimal policy by a set of thresholds which have considerable structure. In addition to the analysis, we discuss the current literature in the context of our results.
%0 Conference Paper
%1 7277432
%A Maccio, V.J.
%A Down, D.G.
%B Teletraffic Congress (ITC 27), 2015 27th International
%D 2015
%K Cost_function Energy_consumption Energy_states MDP Markov_decision_process Markov_processes Servers Switches Turning computer_centres data-centre energy-aware_queueing_system itc itc27 optimal_control queueing_theory server_farm telecommunication_control telecommunication_power_management
%P 98-106
%R 10.1109/ITC.2015.19
%T On Optimal Control for Energy-Aware Queueing Systems
%U https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc27/7277432.pdf?inline=true
%X Over the past few years, energy provisioning in server farms and data-centres has become an active area of research. As such, many models have been proposed where an individual server has setup times and can switch between two different energy states (on and off). To make such models tractable, assumptions are usually made on the type of policies the system can implement. However, it is often not known if such assumptions allow for the model to capture the optimal policy, or if such a model will be strictly suboptimal. In this work we model such systems using Markov Decision Processes (MDPs) and derive several structural properties which (partially) describe the optimal policy. These properties reduce the set of feasible policies significantly, allowing one to describe the optimal policy by a set of thresholds which have considerable structure. In addition to the analysis, we discuss the current literature in the context of our results.
@inproceedings{7277432,
abstract = {Over the past few years, energy provisioning in server farms and data-centres has become an active area of research. As such, many models have been proposed where an individual server has setup times and can switch between two different energy states (on and off). To make such models tractable, assumptions are usually made on the type of policies the system can implement. However, it is often not known if such assumptions allow for the model to capture the optimal policy, or if such a model will be strictly suboptimal. In this work we model such systems using Markov Decision Processes (MDPs) and derive several structural properties which (partially) describe the optimal policy. These properties reduce the set of feasible policies significantly, allowing one to describe the optimal policy by a set of thresholds which have considerable structure. In addition to the analysis, we discuss the current literature in the context of our results.},
added-at = {2016-07-11T18:20:14.000+0200},
author = {Maccio, V.J. and Down, D.G.},
biburl = {https://www.bibsonomy.org/bibtex/24984daa995ed7790aa04d95d42dcc035/itc},
booktitle = {Teletraffic Congress (ITC 27), 2015 27th International},
doi = {10.1109/ITC.2015.19},
interhash = {6f8cfa375e95200c7018a8c037d3d297},
intrahash = {4984daa995ed7790aa04d95d42dcc035},
keywords = {Cost_function Energy_consumption Energy_states MDP Markov_decision_process Markov_processes Servers Switches Turning computer_centres data-centre energy-aware_queueing_system itc itc27 optimal_control queueing_theory server_farm telecommunication_control telecommunication_power_management},
month = {Sept},
pages = {98-106},
timestamp = {2020-04-30T18:18:14.000+0200},
title = {On Optimal Control for Energy-Aware Queueing Systems},
url = {https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc27/7277432.pdf?inline=true},
year = 2015
}