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

云计算 计算机科学 CloudSim公司 服务器 分布式计算 能源消耗 虚拟机 数据中心 工作量 资源管理(计算) 资源配置 高效能源利用 计算机网络 操作系统 工程类 电气工程
作者
Sukhpal Singh Gill,Shreshth Tuli,Adel N. Toosi,Félix Cuadrado,Peter Garraghan,Rami Bahsoon,Hanan Lutfiyya,Rizos Sakellariou,Omer Rana,Schahram Dustdar,Rajkumar Buyya
出处
期刊:Journal of Systems and Software [Elsevier BV]
卷期号:166: 110596-110596 被引量:11
标识
DOI:10.1016/j.jss.2020.110596
摘要

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.

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