Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Systems and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Systems and Software, 166, 2020 DOI: 10.1016/j.jss.2020.110596
Accepted author manuscript, 958 KB, PDF document
Available under license: CC BY-NC-ND
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - ThermoSim
T2 - Deep learning based framework for modeling and simulation of thermal-aware resource management for cloud computing environments
AU - Gill, S.S.
AU - Tuli, S.
AU - Toosi, A.N.
AU - Cuadrado, F.
AU - Garraghan, P.
AU - Bahsoon, R.
AU - Lutfiyya, H.
AU - Sakellariou, R.
AU - Rana, O.
AU - Dustdar, S.
AU - Buyya, R.
N1 - This is the author’s version of a work that was accepted for publication in Journal of Systems and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Systems and Software, 166, 2020 DOI: 10.1016/j.jss.2020.110596
PY - 2020/8/1
Y1 - 2020/8/1
N2 - 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-aware framework to simulate and model the behavior of nodes and measure the important performance parameters which can be affected by its temperature. In this paper, we propose a lightweight framework, ThermoSim, for modeling and simulation of thermal-aware resource management for cloud computing environments. This work presents a Recurrent Neural Network based deep learning temperature predictor for CDCs which is utilized by ThermoSim for lightweight resource management in constrained cloud environments. ThermoSim extends the CloudSim toolkit helping to analyze the performance of various key parameters such as energy consumption, service level agreement violation rate, number of virtual machine migrations and temperature during the management of cloud resources for execution of workloads. Further, different energy-aware and thermal-aware resource management techniques are tested using the proposed ThermoSim framework in order to validate it against the existing framework (Thas). The experimental results demonstrate the proposed framework is capable of modeling and simulating the thermal behavior of a CDC and ThermoSim framework is better than Thas in terms of energy consumption, cost, time, memory usage and prediction accuracy.
AB - 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-aware framework to simulate and model the behavior of nodes and measure the important performance parameters which can be affected by its temperature. In this paper, we propose a lightweight framework, ThermoSim, for modeling and simulation of thermal-aware resource management for cloud computing environments. This work presents a Recurrent Neural Network based deep learning temperature predictor for CDCs which is utilized by ThermoSim for lightweight resource management in constrained cloud environments. ThermoSim extends the CloudSim toolkit helping to analyze the performance of various key parameters such as energy consumption, service level agreement violation rate, number of virtual machine migrations and temperature during the management of cloud resources for execution of workloads. Further, different energy-aware and thermal-aware resource management techniques are tested using the proposed ThermoSim framework in order to validate it against the existing framework (Thas). The experimental results demonstrate the proposed framework is capable of modeling and simulating the thermal behavior of a CDC and ThermoSim framework is better than Thas in terms of energy consumption, cost, time, memory usage and prediction accuracy.
KW - Cloud computing
KW - Deep learning
KW - Energy
KW - Resource management
KW - Simulation
KW - Thermal-aware
KW - Energy utilization
KW - Environmental management
KW - Natural resources management
KW - Network security
KW - Power management
KW - Printing machinery
KW - Recurrent neural networks
KW - Resource allocation
KW - Virtual machine
KW - Cloud computing environments
KW - Current cloud computing
KW - Lightweight frameworks
KW - Modeling and simulating
KW - Performance parameters
KW - Resource management techniques
KW - Service Level Agreements
KW - Virtual machine migrations
KW - Green computing
U2 - 10.1016/j.jss.2020.110596
DO - 10.1016/j.jss.2020.110596
M3 - Journal article
VL - 166
JO - Journal of Systems and Software
JF - Journal of Systems and Software
SN - 0164-1212
M1 - 110596
ER -