AI Hardware Resource Monitoring in the Data Center Environment

Nanduri Vijaya Saradhi
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Abstract

Deploying an AI (Artificial Intelligence) model in the data center initiates more responsibilities to the backend services such as Monitoring. It is required to monitor the performance of AI systems regularly to ensure that they meet the requirements and will not encounter any system performance issues. This whitepaper focuses on the importance of monitoring AI systems, the monitoring model, how to measure the performance of the system hardware resources such as CPU, Memory, disk and GPU, and tools to be used to monitor the system resources. Organisations can take necessary proactive maintenance actions before an incident is caused due to performance bottlenecks in the AI systems, proving the importance of monitoring the AI system. The goal of continuous monitoring of AI systems is to ensure the effective operation of AI systems throughout their lifecycle to meet several objectives such as performance, anomaly detection, security monitoring, data compliance and continuous improvements. Performance measurement of critical resources such as GPU, Memory and Storage by using suitable tools and configuring the alerts when the thresholds are reached on the identified resource threads. These measurements will be utilized to strengthen the AI system that will be stable for any performance bottlenecks.
数据中心环境中的人工智能硬件资源监控
在数据中心部署人工智能(AI)模型会给后端服务(如监控)带来更多责任。需要定期监控人工智能系统的性能,以确保它们满足要求,不会遇到任何系统性能问题。本白皮书重点介绍监控人工智能系统的重要性、监控模型、如何测量 CPU、内存、磁盘和 GPU 等系统硬件资源的性能,以及用于监控系统资源的工具。组织可以在人工智能系统因性能瓶颈而导致事故之前采取必要的主动维护行动,这证明了监控人工智能系统的重要性。对人工智能系统进行持续监控的目的是确保人工智能系统在整个生命周期内有效运行,以实现性能、异常检测、安全监控、数据合规性和持续改进等多个目标。使用合适的工具对 GPU、内存和存储等关键资源进行性能测量,并在确定的资源线程达到阈值时配置警报。这些测量结果将用于加强人工智能系统,使其在遇到任何性能瓶颈时都能保持稳定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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