Journal article

ThermoSim: Deep learning based framework for modeling and simulation of thermal-aware resource management for cloud computing environments

SS Gill, S Tuli, AN Toosi, F Cuadrado, P Garraghan, R Bahsoon, H Lutfiyya, R Sakellariou, O Rana, S Dustdar, R Buyya

Journal of Systems and Software | Elsevier | Published : 2020


Current cloud computing frameworks host millions of physical servers that utilize cloud computing resources in the form of different virtual machines. Cloud Data Center (CDC) infrastructures require significant amounts of energy to deliver large scale computational services. Moreover, computing nodes generate large volumes of heat, requiring cooling units in turn to eliminate the effect of this heat. Thus, overall energy consumption of the CDC increases tremendously for servers as well as for cooling units. However, current workload allocation policies do not take into account effect on temperature and it is challenging to simulate the thermal behavior of CDCs. There is a need for a thermal-..

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Funding Acknowledgements

We would like to thank Joseph Richardson (Lancaster University, UK), Shashikant Ilager (University of Melbourne, Australia), Shikhar Tuli (IIT Delhi, India) and Dominic Lindsay (Lancaster University, UK) for their feedback to improve the quality of the paper. We would like to thank the editors, area editor and anonymous reviewers for their valuable comments and suggestions to help and improve our research paper. An initial investigation on this work was carried out at Melbourne CLOUDS Lab, which was supported by Melbourne-Chindia Cloud Computing (MC3) Research Network.