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style="font-family:宋体;color:#333333;font-size:13pt;">机器学习和人工智能是当今<span>IT</can/can/span>专业人员的热门话题,而在企业的数据中心,它们拥有真正的前景。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">机器学习软件可比你或你的团队更快预测情况,甚至可能更快地解决它们。这些系统是当今混合数据中心环境的合理扩展,是数据中心基础设施中不断增长的一部分。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">IDC</can/can/span><span style="font-family:宋体;color:#333333;font-size:13pt;">预测,到<span>2022</can/can/span>年,在数据中心,<span>50%</can/can/span>的<span>IT</can/can/span>资产将使用嵌入式<span>AI</can/can/span>功能自主运行。数据中心的机器学习可以优化大部分整体运营,包括规划和设计、工作负载、正常运行时间和成本管理。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <b><span style="font-family:宋体;color:#333333;font-size:13pt;">数据中心机器学习用例</can/can/span></can/can/b><span style="font-family:宋体;color:#333333;font-size:13pt;"></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">机器学习能够从场景和数据集中学习,并且可以构建即时响应,而不需要人为干预或依赖于一组有限的预编程动作。该技术可以帮助你更好地了解你的数据中心系统,更有效地管理它们并防止意外停机。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">创建更高效的数据中心。企业可以使用机器学习来自动管理其数据中心的物理环境,并通过软件对物理设施和数据中心架构进行实时修改,而不是警报。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">谷歌使用其<span>AI</can/can/span>系统自动管理其数据中心的冷却,并持续分析<span>21</can/can/span>个变量,例如空气温度、功率负载和内部气压。在<span>2018</can/can/span>年,该公司利用机器学习将冷却所需的能源减少<span>40%</can/can/span>,并且实现<span>1.06</can/can/span>分的电力使用效率。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">降低运营风险。对于数据中心运营,防止停机是关键任务,而机器学习可以帮助你更轻松地预测和预防停机。数据中心机器学习软件监控关键设备<span>(</can/can/span>例如电源管理和冷却系统<span>)</can/can/span>的实时性能数据,并预测硬件何时出现故障。这使你可以对这些系统执行预防性维护,并防止代价高昂的停机。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">基于机器学习的风险分析可提高数据中心的正常运行时间,这主要是通过构建不同配置以增加弹性<span>;</can/can/span>识别预防性维护的机会<span>;</can/can/span>以及识别潜在的网络安全风险,甚至在它们出现前。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">通过智能数据减少客户流失。企业可在数据中心利用机器学习来更好地了解客户并潜在地预测消费者行为。作为客户成功计划的延伸,机器学习可以分析数据中心内收集后未使用的大量信息。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">当机器学习软件与客户关系管理<span>(CRM)</can/can/span>系统连接时,这个基于<span>AI</can/can/span>的数据中心可以搜索和检索存储的历史数据库<span>(</can/can/span>在传统上不用于<span>CRM)</can/can/span>中的数据,并允许<span>CRM</can/can/span>系统制定不同的战略以挖掘潜在或实现客户成功。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">从以下软件选项开始<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">由于机器学习可以比人类更快地运行,因此它可以在几秒内分析数<span>TB</can/can/span>的历史数据,并将参数应用于其决策,当你在跟踪数据中心中的所有活动时,这非常有用。如果你希望将机器学习部署到数据中心,可以从以下几个用例和软件产品开始。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">电力和能源管理。能源管理是企业最容易利用数据中心机器学习实现的领域之一,并可立即获得显着收益。谷歌使用<span>DeepMind</can/can/span>实现约<span>30%</can/can/span>的能源节约,从而降低了相关成本。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">Maya HTT</can/can/span><span style="font-family:宋体;color:#333333;font-size:13pt;">的数据中心基础架构管理软件<span>Datacenter Clarity LC</can/can/span>利用基于<span>AI</can/can/span>的工具分析各个服务器,以检测异常情况以及识别优化机会。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">例如,它可以识别工作负载并将其从效率较低的服务器重新路由到能源和工作效率较高而利用率较低的服务器<span>;</can/can/span>而你会看到关于更换旧服务器的通知,你可以在它成为问题之前进行升级。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">日志管理。大多数数据中心系统都会生成日志,但如果这些日志没有被利用,那么就没有价值。还有企业使用的任何边缘或外围设备,这里涉及大量日志。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">机器学习可以集中和分析这些日志,并可创建对你的团队有价值且易于使用的报告。开源技术<span>(</can/can/span>例如<span>Elasticsearch)</can/can/span>以及来自<span>Splunk</can/can/span>的付费选项可以帮助分析和提取机器学习例程收集的任何数据。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span style="font-family:宋体;color:#333333;font-size:13pt;">根本原因分析。当你遇到任何性能问题时,你必须能够快速确定根本原因并加以解决。<span>Hewlett Packard Enterprise</can/can/span>的<span>InfoSight</can/can/span>产品中的<span>AI</can/can/span>预测引擎可帮助你近乎实时地发现并解决在内部数据中心和云部署中的问题。<span></can/can/span></can/can/span> </can/can/p> <p style="background:white;" class="MsoNormal"> <span 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