在ST Telemedia GDC用于数据中心冷却的AI试点
Inside ST Telemedia GDC’s AI Pilot for Data Center Cooling
Wylie Wong，Aug 10 2021
今年春天，ST Telemedia GDC启动了一项试点，研究基于人工智能的系统是否可以通过比设施工程师更有效地管理冷却系统来减少其新加坡数据中心的能耗。
This spring, ST Telemedia Global Data Centres launched a pilot to see whether an AI-based system could reduce energy use of the cooling system in one of its Singapore data centers by managing it more efficiently than human facilities engineers could.
STT GDC的CTO Daniel Pointon最近告诉DCK，该试点已经产生了一些预期的初步结果。根据初始模型，冷却系统的能耗比以前降低了约10%。
And the pilot has already produced some promising early results, Daniel Pointon, STT GDC’s CTO, recently told DCK. The cooling system stands to use about 10 percent less energy than before, according to initial modeling.
Pointon说：“这是相当重要，我们并没有要求客户关闭服务器或使用更少的电力，我们只是更有效地做同样的工作。”
That’s “pretty substantial, given that we are not asking customers to shut down servers or use less power,” Pointon said. “We are just doing the same job more efficiently.”
如果考虑到STT GDC的规模和在整个行业中部署类似解决方案的节能潜力，它会变得更加重要。这家总部位于新加坡的主机托管商是投资公司ST Telemedia的子公司，在亚洲和英国运营着130个数据中心。
It becomes a lot more substantial if you consider STT GDC’s scale and the potential for energy savings if a similar solution is deployed across its entire portfolio. The Singapore-based colocation provider, a subsidiary of the investment company ST Telemedia, says it operates 130 data centers in Asia and the UK.
测试人工智能在数据中心冷却管理中潜力的试点是STT GDC进一步承诺将减少其整体碳足迹并到2030年实现碳中和的一部分。其最大客户已做出了减少碳排放的承诺，在此推动下，为了达到可观的程度，越来越多的专业数据中心运营方采取了比以前更大胆的举措来解决他们的碳足迹问题。
The pilot to test AI’s potential in data center cooling management is part of STT GDC’s broader pledge to reduce its overall carbon footprint and become carbon-neutral by 2030. Pushed to a substantial degree by their largest customers who have made ambitions carbon-reduction pledges of their own, more and more specialist data center providers have been taking bolder steps than before to address their carbon footprint.
AI可减少数据中心制冷能耗
AI Can Reduce Data Center Cooling Energy Use
人工智能在数据中心管理中的应用还为时过早。仅少数公司有过尝试，最著名的例子是谷歌使用其DeepMind子公司开发的人工智能算法来管理其超大规模计算设施中的制冷系统。一位谷歌高管在2018年告诉我们，该方法使数据中心冷却系统的能源使用量减少了30%。
It’s very early days for AI in data center management. Few have tried it, but the most famous example is Google’s use of AI algorithms developed by its DeepMind subsidiary to manage cooling systems in its hyperscale computing facilities. A Google executive told us in 2018 that the approach resulted in a 30 percent reduction in energy use by data center cooling systems.
冷却只是STT GDC人工智能实验的开始。Pointon希望公司在未来研究使用人工智能进行预测性维护、容量管理、安全分析和自动化业务流程。
Cooling is just the start of STT GDC’s experimentation with AI. Pointon expects the company to investigate using AI for predictive maintenance, capacity management, security analytics, and automating business processes in the future.
STT GDC聘请瑞士工业自动化巨头ABB在新加坡进行为期12个月的试点。Pointon表示，在试点的第一阶段，ABB正在收集数据、构建 AI 模型并开发控制逻辑。
STT GDC hired the Swiss industrial automation giant to conduct the pilot in Singapore over a 12-month period. In the first phase of the pilot, ABB is gathering data, building the AI model, and developing the control logic, Pointon said.
这家托管服务提供商之前安装了数千个传感器来测量其制冷系统的各部位，包括整个数据中心内的温度、湿度和气流——在机架前、管道系统中和屋顶。还有传感器测量制冷系统的每个组件的能耗。
The colocation provider previously installed thousands of sensors measuring different aspects of its cooling systems, including temperature, humidity, and airflow throughout its data centers – in front of racks, in the ductwork, and on the rooftop. There are also sensors measuring energy use by each component of the cooling system.
“所有这些传感器都为我们提供了一个良机，可以真正利用人工智能的力量并有意义地利用所有这些信息，并帮助我们做出关于我们应该如何运行各种设备以获得最佳结果的操作决策，”他说。
“All those sensors give us a great opportunity to really harness the power of AI and to make meaningful use of all that information and to help us make operational decisions about the way we should operate various pieces of equipment for optimal outcomes,” he said.
尚未将制冷管理交给AI
Not Handing Cooling Management Over to AI Just Yet
分析使操作员能够微调数据中心控制系统的设定点。
The analytics enable operators to tweak set points of the data center control systems.
“例如，它可以对泵的运行速度和水流量进行微调，或者微调冷却塔或精密空调内的风扇速度，”Pointon解释说。“这是对一个大系统进行大量微调的组合，并根据不断变化的情况实时动态进行这些调整节省开支。”
“For example, it can make slight adjustments to the speed the pumps run and how much water we push around, or slight adjustments to fan speeds in our cooling towers or inside our CRAC units,” Pointon explained. “Those combinations of a lot of small adjustments over a big system and making those adjustments dynamically in real time based on changing situations [can] unlock savings.”
在试点的第二阶段，ABB将在新加坡数据中心运行人工智能模型。STT GDC 不会让系统自动调整设定值。Pointon说，数据中心经理将分析AI的建议，并在试用期间手动进行更改。
In the second phase of the pilot ABB will run the AI model at the Singapore data center. STT GDC won’t let the system adjust set points automatically. The data center’s managers will analyze the suggestions from AI and manually make changes during the pilot, Pointon said.
他说：“我们不会立即放手，并且完全相信该试点可以为我们经营业务。”“我们将查看人工智能提出的控制建议，然后从我们人类视角看是否可行，或者这是否会导致我们在其他目标如可靠性方面犯错误？然后去落实一些更有意义的建议。”
“We are not going to let it loose right away and put all the faith that this pilot can run our business for us,” he said. “We will look at the control suggestions that AI makes and then we will put a human lens on top of that to see [whether it] make sense, or will that cause us to trip up on our other objectives like reliability? And then [we will] go execute some of those more meaningful suggestions.”
如果试点成功，下一步STT GDC将在位于中国、印度、英国、新加坡、印度尼西亚、泰国和韩国的数据中心部署人工智能技术。Pointon说，随着时间的推移，人工智能可以更多地集成到数据中心运营中，并从简单地提出建议转为自动运行系统。眼下，他不想太超前，想先看看试运行的效果。
If the pilot is successful, the next step would be deployment of the AI technology throughout STT GDC’s data centers, located in China, India, the UK, Singapore, Indonesia, Thailand, and South Korea. Over time AI could become more integrated into data center operations and go from simply making suggestions to automatically running the systems, Pointon said.For now, he doesn’t want to get ahead of himself and wants to see the results of the pilot first.
“除了关注能源方面，我们还必须利用该试点做出一些判断，例如这对可靠性、成本和为客户提供的服务水平意味着什么。我不想预先判断结果。我们将完成这个试点。目前它看起来非常有意义。”
“As well as looking at the energy side of it, we’ve got to use this pilot to make some judgments [such as], what does this mean for reliability, costs, and our service levels for our customers. I don’t want to prejudge the outcome. We are going to finish this pilot. But it’s looking really positive.”
DeepKnowledge
翻译：
王磊
阿里巴巴集团有限公司数据中心运维工程师
校对：
Eric
DKV（Deep Knowledge Volunteer）创始成员
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