Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

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

Gill, Sukhpal Singh, Tuli, Shreshth, Toosi, Adel Nadjaran, Cuadrado, Felix, Garraghan, Peter, Bahsoon, Rami, Lutfiyya, Hanan, Sakellariou, Rizos, Rana, Omer, Dustdar, Schahram and Buyya, Rajkumar 2020. ThermoSim: deep learning based framework for modeling and simulation of thermal-aware resource management for cloud computing environments. Journal of Systems and Software 166 , 110596. 10.1016/j.jss.2020.110596

Full text not available from this repository.

Abstract

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 behaviour of CDCs. There is a need for a thermal-aware framework to simulate and model the behaviour 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 modelling 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 analyse 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 modelling and simulating the thermal behaviour of a CDC and ThermoSim framework is better than Thas in terms of energy consumption, cost, time, memory usage and prediction accuracy.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Elsevier
ISSN: 0164-1212
Date of Acceptance: 6 April 2020
Last Modified: 11 May 2020 12:40
URI: http://orca.cf.ac.uk/id/eprint/131043

Actions (repository staff only)

Edit Item Edit Item