利用深度q学习最小化服务器能耗的人工智能方法

A. Kaulage, Shraddha Shaha, Tanaya Naik, Khushi Nikumbh, Vedant Jagtap
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摘要

本文的重点是最小化数据中心服务器的能耗。服务器的能源消耗可能受到许多因素的影响,例如连接设备的数量、正在处理的工作负载和组件的能源效率。由于多种原因,高能耗可能会导致严重问题,因为它可能影响服务器的可靠性,因为能耗产生的高温可能导致硬件故障和其他技术问题。因此,降低服务器能耗对于提高数据中心运营的成本效益、可持续性、可扩展性和可靠性非常重要。一种被称为深度Q-Learning (DQL)的强化学习可以用来解决服务器能耗问题。DQL背后的基本思想是训练一个人工代理,比如一个神经网络,来实时地做出关于能源消耗的决定。智能体通过在设定中频繁执行动作来训练,并根据特定动作消耗的能量获得奖励。随着时间的推移,代理了解哪些操作最有可能节省能源,然后可以部署它来对服务器中的能源消耗做出实时决策。实验结果表明,使用深度Q-Learning (DQL)可以平均节省66%的服务器能耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI Approach for Minimizing The Energy Consumption of Servers Using Deep-Q-Learning
This paper focuses on minimizing energy consumption by servers in data centers. Server’s energy consumption can be impacted by numerous factors, such as the number of connected devices, the workload being processed, and the energy efficiency of the components.High energy consumption can be serious because of several reasons as it can impact the reliability of servers because high temperatures generated by energy consumption can lead to hardware failure and other technical issues. Therefore, reducing energy consumption in servers is important for improving the cost-effectiveness, sustainability, scalability, and reliability of data center operations. A type of reinforcement learning called Deep Q-Learning (DQL) can be used to address issues with server energy consumption. The basic idea behind DQL is to train an artificial agent, such as a neural network, to make decisions about energy consumption in real time. The agent is trained by frequently performing actions in a setting and earning rewards depending on the amount of energy consumed by specific actions. Over time, the agent learns which actions are most likely to lead to energy savings, and it can then be deployed to make real-time decisions about energy consumption in a server. Experimental results of the proposed research show an average of 66% power saving in the server’s consumption of energy using Deep Q-Learning (DQL).
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