13
REDUCING COST
13
Schneider Electric ’ s Data Center Science Center recently calculated that typical data centre physical infrastructure energy losses have been cut by 80 % over the last 10 years . This has been enabled by improvements in UPS efficiencies , cooling technologies including economisation , as well as cooling practices such as air containment . Data centres are cheaper now , too , on a £/ watt basis . The big question is how influential artificial intelligence and machine learning will be in continuing this trend of increased performance at lower cost .
Artificial intelligence ( AI ) and machine learning ( ML ) are two terms often used interchangeably or considered to be synonyms . Simply put , AI refers to the concept that a machine or system can be ‘ smart ’ in carrying out tasks and operations based on programming and the input of data about itself or its environment . ML is fundamentally the ability of a machine or system to automatically learn and improve its operation or functions without human input . ML could , therefore , be thought of as the current state of the art for a machine with AI .
Much of today ’ s physical infrastructure equipment in data centres incorporates some form of AI including UPSs and cooling units etc . These have programmed firmware and algorithms that dictate how the equipment operates and behaves as conditions change . For example , cooling control systems actuate valves , fans , and pumps in a coordinated , logical way to achieve user-defined set points as environmental conditions change over time .
In addition , like most IoT infrastructure , power and cooling equipment is equipped with sensors . These devices collect a large amount of useful data about the machines and their environment . This information can be used to determine machine operations and responses to emerging conditions and events . It can also be used by smart systems like Building Management Systems ( BMS ), Power Monitoring Systems ( PMS ), and Data Centre Infrastructure Management ( DCIM ) software to extract useful insights about data centre status and trending such as capacity , reliability , and efficiency .
ML in data centres is an exciting new concept that ’ s currently being researched by manufacturers including Schneider Electric . The company believes that increasing the intelligence and automation of physical infrastructure equipment and management systems , and integrating it with the IT load , will serve to make data centres more reliable and efficient both in terms of energy use and operations .
The difference is DMaaS An important component in Data Centre Management as a Service ( DMaaS ) is that it enables optimisation of the IT layer by simplifying , monitoring , and servicing data centre physical infrastructure from the edge to the enterprise . It utilises cloud-based software for DCIM-like monitoring and information analysis , to offer real-time operational visibility , alarming and shortened resolution times .
Although DCIM tools have previously been made available on a Software as a Service ( Saas ) basis , DMaaS differs from this model in a number of ways . DMaaS has simplified this process of implementing monitoring software throughout the facility . Once monitoring is underway , the service aggregates and analyses large sets of anonymised data directly from data centre infrastructure equipment via a secure and encrypted connection . This data can be further enhanced using big data analytics with the primary goal of
www . networkseuropemagazine . com