  {"id":13686,"date":"2016-11-04T17:47:41","date_gmt":"2016-11-04T21:47:41","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/ai-helps-data-center-to-stay-cool-and-keeps-energy-bills-low\/"},"modified":"2016-11-04T22:11:19","modified_gmt":"2016-11-05T02:11:19","slug":"ai-helps-data-center-to-stay-cool-and-keeps-energy-bills-low","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/ai-helps-data-center-to-stay-cool-and-keeps-energy-bills-low\/","title":{"rendered":"AI helps data center to stay cool and keeps energy bills low"},"content":{"rendered":"<p><span style=\"font-weight: 400\">Data center is a facility that centralizes computer servers, storage and networking systems for certain IT needs. <\/span><span style=\"font-weight: 400\">As the world\u2019s economy shifts further into the digital era, the demand for data centers keep accelerating at a fast pace. In 2006, data centers in the U.S. consumed about 61 billion kilowatt-hours (kWh), which is roughly 1.5% of total U.S. electricity consumption. The energy used by data centers was more than doubled between 2000 and 2006. [1] If the energy-efficiency of data centers did not improve, it was predicted that by 2011 the energy consumption of data centers would exceed 100 billion kWh. That would have significant impact on the power grid and power generation as well as increase millions of tons of greenhouse gas emission [2]. The need for more efficient data centers is urgent and it calls for innovative solution.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Fortunately during the past decade all the major companies of data centers worked together to improve the efficiency and in 2014, data centers in the U.S. consumed an estimated of 70 kWh, at growth rate much lower than originally forecasted. Some of successful strategies and best practises include:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Consolidate smaller server operations into hyperscale data centers<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Adopt new energy-efficient servers and components<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Apply best cooling practices: liquid cooling, hot air containment, extensive monitoring and free cooling during cold season<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">As most data centers adopted these best practices, the improvement to energy efficiency starts to show diminishing return and the energy use is expected to slightly increase in the near future, at rate of 4% growth from 2014-2020. However a<\/span><span style=\"font-weight: 400\">ccording to the Uptime Institute&#8217;s 2014 Data Center Survey [3], the global average Power Usage Effectiveness (PUE) of the largest data centers is around 1.7, which means every 1kWh of energy used on IT equipment, 700Wh is consumed as facility overhead.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Definition of <\/span><span style=\"font-weight: 400\">Power Usage Effectiveness (PUE)<\/span><\/p>\n<p><a href=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/1-8.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-13597 size-full\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/1-8.png\" alt=\"1\" width=\"687\" height=\"93\" srcset=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/1-8.png 687w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/1-8-300x41.png 300w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/1-8-600x81.png 600w\" sizes=\"auto, (max-width: 687px) 100vw, 687px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400\">Google, the company with the largest data centers in the world, has always been focused on improving energy efficiency while sustaining the explosive growth of the Internet. Since it started reporting PUE publicly from 2008, the average PUE across the whole fleets continued to decrease and stabilized around 1.12.<\/span><\/p>\n<p><a href=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/2-11.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-13589\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/2-11.jpg\" alt=\"2\" width=\"642\" height=\"297\" srcset=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/2-11.jpg 642w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/2-11-300x139.jpg 300w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/2-11-600x278.jpg 600w\" sizes=\"auto, (max-width: 642px) 100vw, 642px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400\">History PUE values at Google [4]<\/span><\/p>\n<p><span style=\"font-weight: 400\">As the modern data center becomes more complicated with a wide variety of mechanical and electrical equipments, further reducing PUE is found extremely difficult due to following reasons:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Traditional formula-based engineering could not capture the complex, nonlinear interactions among various of equipments and their settings.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Although every day millions of data points are monitored by thousands of sensors installed in the data centers, analyzing such data is found beyond simple intuition and heuristics.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Rapid changes in internal requirements (usage\/traffic spike) and external conditions (weather change) make quick adaptation even more challenging for human operator.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">As each data center has different design and environment, a working model for one data center does not transfer to another one. <\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400\">Hence<\/span><span style=\"font-weight: 400\">, a general intelligence framework is necessary to understand the data center\u2019s interactions and to provide optimal management of various equipments. Based on its success in DeepMind\u2019s <\/span><span style=\"font-weight: 400\">AI<\/span><span style=\"font-weight: 400\"> algorithm, Google trained a system of neural networks with different operating scenarios and parameters within its data centers. <\/span><span style=\"font-weight: 400\">About 120 variables in the data centers (such as the fans and pump speeds, temperature and system efficiency) are feeded into the neural networks to allow the AI to understand the dynamics of data centers and work out the most efficient methods of cooling. [5]<\/span><\/p>\n<p><a href=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/3-9.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-13676\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/3-9.png\" alt=\"3\" width=\"1000\" height=\"421\" srcset=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/3-9.png 1000w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/3-9-300x126.png 300w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/3-9-768x323.png 768w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/3-9-600x253.png 600w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/a><\/p>\n<p>PUE comparison during a typical day of testing, between AI recommendations on and off [6]<\/p>\n<p><span style=\"font-weight: 400\">The result of the AI system is impressive. It is able to reduce the amount of energy used for cooling by 40%, which is equal to 15% of overall PUE overhead. <\/span><\/p>\n<p><span style=\"font-weight: 400\">The potential impact of such intelligent system is enormous for data centers. Since most non-google data centers have significant higher degree of PUE overhead, the room for improvement is even bigger. If adopting such intelligent system can reduce the average PUE from 1.7 to 1.6, a similar 15% cut in the above scenario, 4 billion kWh of energy can be saved each year or 2.8 million tons of greenhouse gas can be eliminated. If all the non-google data centers can reach Google\u2019s PUE of 1.12, 24 billion kWh of energy or 16.8 million tons of greenhouse gas can be eliminated.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Conclusion:<\/span><\/p>\n<p><span style=\"font-weight: 400\">While many companies have made big strides in increasing data center efficiency, there are still significant opportunities for further improvement. The recent attempt by Google to utilize advanced technology like DeepMind\u2019s to optimize data center operations showed impressive result in reducing energy consumption and demonstrated a great potential on how AI can help human to optimize the system and address the urgent challenge of climate change.<\/span><\/p>\n<p>Word count: 770<\/p>\n<p><span style=\"font-weight: 400\">References:<\/span><\/p>\n<p><span style=\"font-weight: 400\">[1] <\/span><span style=\"font-weight: 400\">United States Data Center Energy Usage Report, Ernest Orlando Lawrence\u00a0<\/span><span style=\"font-weight: 400\">Berkeley National Laboratory <\/span><\/p>\n<p><a href=\"https:\/\/eta.lbl.gov\/publications\/united-states-data-center-energy-usag\"><span style=\"font-weight: 400\">https:\/\/eta.lbl.gov\/publications\/united-states-data-center-energy-usag<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400\">[2] Based on the estimated increase of 30 billion kWh and Greenhouse Gas Equivalencies Calculator <\/span><\/p>\n<p><a href=\"https:\/\/www.epa.gov\/energy\/greenhouse-gas-equivalencies-calculator\"><span style=\"font-weight: 400\">https:\/\/www.epa.gov\/energy\/greenhouse-gas-equivalencies-calculator<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400\">[3] 2014 Data Center Industry Survey, UpTime Institute<\/span><\/p>\n<p><a href=\"https:\/\/journal.uptimeinstitute.com\/2014-data-center-industry-survey\/\"><span style=\"font-weight: 400\">https:\/\/journal.uptimeinstitute.com\/2014-data-center-industry-survey\/<\/span><\/a><\/p>\n<p>[4]\u00a0Efficiency: How we do it, Google<\/p>\n<p><a href=\"https:\/\/www.google.com\/about\/datacenters\/efficiency\/internal\/\">https:\/\/www.google.com\/about\/datacenters\/efficiency\/internal\/<\/a><\/p>\n<p><span style=\"font-weight: 400\">[5] Machine Learning Applications for Data Center Optimization, Jim Gao, Google <\/span><\/p>\n<p><a href=\"http:\/\/research.google.com\/pubs\/pub42542.html\"><span style=\"font-weight: 400\">http:\/\/research.google.com\/pubs\/pub42542.html<\/span><\/a><\/p>\n<p>[6]\u00a0DeepMind AI Reduces Google Data Centre Cooling Bill by 40%, Google<\/p>\n<p><a href=\"https:\/\/deepmind.com\/blog\/deepmind-ai-reduces-google-data-centre-cooling-bill-40\/\">https:\/\/deepmind.com\/blog\/deepmind-ai-reduces-google-data-centre-cooling-bill-40\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Data center is a facility that centralizes computer servers, storage and networking systems for certain IT needs. As the world\u2019s economy shifts further into the digital era, the demand for data centers keep accelerating at a fast pace. In 2006, [&hellip;]<\/p>\n","protected":false},"author":2426,"featured_media":14258,"comment_status":"open","ping_status":"closed","template":"","categories":[1869,476],"class_list":["post-13686","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-ai","category-data-centers"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/climate-change-challenge-2016\/","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI helps data center to stay cool and keeps energy bills low - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/ai-helps-data-center-to-stay-cool-and-keeps-energy-bills-low\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI helps data center to stay cool and keeps energy bills low - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"Data center is a facility that centralizes computer servers, storage and networking systems for certain IT needs. 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