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ThermoSim: Deep Learning based Framework for Modeling and Simulation of Thermal-aware Resource Management for Cloud Computing Environments
Journal of Systems and Software ( IF 3.5 ) Pub Date : 2020-08-01 , DOI: 10.1016/j.jss.2020.110596
Sukhpal Singh Gill , Shreshth Tuli , Adel Nadjaran Toosi , Felix Cuadrado , Peter Garraghan , Rami Bahsoon , Hanan Lutfiyya , Rizos Sakellariou , Omer Rana , Schahram Dustdar , Rajkumar Buyya

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.

中文翻译:

ThermoSim:基于深度学习的云计算环境热感知资源管理建模和仿真框架

当前的云计算框架承载着数以百万计的物理服务器,它们以不同的虚拟机的形式利用云计算资源。云数据中心 (CDC) 基础设施需要大量能源来提供大规模计算服务。此外,计算节点会产生大量热量,进而需要冷却装置来消除这种热量的影响。因此,CDC 的整体能源消耗对于服务器和冷却装置来说都大幅增加。然而,当前的工作负载分配策略没有考虑对温度的影响,模拟 CDC 的热行为具有挑战性。需要一个热感知框架来模拟和建模节点的行为,并测量可能受其温度影响的重要性能参数。在本文中,我们提出了一个轻量级框架 ThermoSim,用于对云计算环境的热感知资源管理进行建模和仿真。这项工作提出了一种基于循环神经网络的 CDC 深度学习温度预测器,ThermoSim 将其用于受限云环境中的轻量级资源管理。ThermoSim 扩展了 CloudSim 工具包,有助于分析各种关键参数的性能,例如能源消耗、服务水平协议违规率、虚拟机迁移次数和在管理云资源以执行工作负载期间的温度。此外,使用提议的 ThermoSim 框架测试了不同的能量感知和热感知资源管理技术,以便根据现有框架 (Thas) 对其进行验证。
更新日期:2020-08-01
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