Data Centers in the age of the Industrial Internet
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The document discusses the transformative impact of the industrial internet on data centers, highlighting the integration of intelligent machines, advanced data analytics, and the workforce to optimize operations and generate significant savings. It outlines the challenges faced by data centers, including data management, scalability, and integration of diverse systems, while emphasizing the importance of high availability control and advanced analytics for improved performance. The conclusion stresses the need for a holistic approach to technology in data centers to enhance reliability, operational efficiency, and competitive advantage.
Data Centers in the age of the Industrial Internet
- 1. Data Centers in the age of the Industrial Internet
- 3. Industrial Internet – The Next Big Wave • The Industrial Internet brings together the advances of two transformative revolutions: the myriad machines, facilities, fleets and networks that arose from the Industrial Revolution, and the more recent powerful advances in computing, information and communication systems brought to the fore by the Internet Revolution • It is a wave of innovation that promises to change the way we do business and interact with the world of industrial machines Wave 1 Industrial Revolution Machines and factories that power economies of scale and scope Wave 2 Internet Revolution Computing power and rise of distributed information networks Wave 3 Industrial Internet Machine-based analytics: physics based, deep domain expertise, automated, predictive
- 4. Industrial Internet – What is it comprised of? Three essential elements embody the essence of Industrial Internet: • Intelligent Machines New ways of connecting the word’s myriad of machines, facilities, fleets and networks with advanced sensors, controls and software applications • Advanced data analytics use physics-based analytics, predictive algorithms, automation and deep domain expertise in material science, electrical engineering and other key disciplines required to understand how machines and larger systems operate Using advanced data analytics can improve fuel efficiency, help identify machinery maintenance issues and make other operational improvements that could add up to trillions of dollars in savings • People at work: connecting people, whether they be at work in industrial facilities, offices, hospitals or on the move, at any time to support more intelligent design, operations, maintenance as well as higher quality service and safety. Connecting and combining these elements offers new opportunities across firms and economies.
- 5. Industrial Internet – How does it work?
- 6. Data Collection – An essential element of the Industrial Internet
- 7. Where does the data come from? • The Industrial Internet starts with embedding sensors and other advanced instrumentation in an array of machines from the simple to the highly complex • This allows the collection and analysis of an enormous amount of data can be used to improve machine performance, and inevitably the efficiency of the systems and networks that link them • An essential component of GE’s Intelligent Platforms business is Control and Communication Systems that comprises advanced data collection and advanced analytics • The opportunity is huge considering the specific physical assets involved in various parts of the industrial system.
- 8. How ‘BIG’ is this data? • The industrial system is comprised of huge numbers of machines and critical systems • There are now millions of machines across the world ranging from simple electric motors to highly advanced computed cosmography (CT scanners) used in the delivery of health care • All of these pieces of equipment are associated with information (temperature, pressure, vibration and other key indicators) and are valuable to understanding performance of the unit itself and in relation to other machines and systems • Traditional statistical approaches use historical data gathering techniques where often there is more separation between the data, the analysis, and decision making. With advanced system monitoring, the ability to work with larger volumes of realtime data has been expanding
- 9. How ‘BIG’ is this data? Commercial Jet Aircraft • There are approximately 21,500 commercial jet aircraft and 43,000 jet engines in service around the world in 2011* • Commercial jets are most commonly powered by a twin jet engine configuration. These aircraft take approximately 3 departures per day, for a total of 23 million departures annually. • Each jet engine contains many moving parts; however, there are three major pieces of rotating equipment: a turbo fan, compressor, and turbine. Each of these components will be instrumented and monitored separately. In total, there are approximately 129,000 major pieces of spinning equipment operating in the commercial fleet today • While it is probably impossible to know precisely how many machines and devices, fleets, and networks exist within the world’s ever expanding industrial system, it is possible to look at some specific segments to get a feel for the scale of the industrial system
- 10. How ‘BIG’ is this data? Turbo fan 21,500 commercial jet aircraft 43,000 3 departures per day 23 million departures annually Compressor 3 Turbine major pieces of rotating equipment in each jet engine jet engines 129,000 major pieces of spinning equipment operating in the commercial fleet today Each of these components will be instrumented and monitored separately
- 11. How ‘BIG’ is this data?
- 12. How Would This Data Help? • The Industrial Internet promises to have a range of benefits spanning machines, facilities, fleets and industrial networks, which in turn influence the broader economy • Intelligent instrumentation enables individual machine optimization, which leads to better performance, lower costs and higher reliability • Intelligent decisioning will allow smart software to lock-in machine and systemlevel benefits • Further, the benefits of continued learning holds the key to the better design of new products and services— leading to a virtuous cycle of increasingly better products and services resulting in higher efficiencies and lower costs
- 13. How Would This Data Help?
- 14. Data Collection and Storage – Data Centers
- 15. Where does all this data sit? • Data collection is the first step in the process of transforming recorded data into information • Most data centers now have to deal with the massively increasing volumes of data collected from entities such as the industrial internet. For eg., data collection at GE from jet engines in one year has been more than that collected in 96 years! • By 2016, most new datacenters will be 40% smaller while supporting a 300% increased workload • Data centers facilities must be cost-effective, but flexible enough to enable virtualization, cloud computing, mobility, social media and collaboration applications.
- 16. Managing Data Centers – Key Challenges Key Challenges involved in managing data centers include: Standalone subsystems • Visibility into baseline infrastructure eg. paralleling switchgear, standby power generation, alternative energy sources, automatic transfer switches, UPS systems and chillers which may not easily integrate with each other, due to disparate systems, so failures have to be managed locally • Identifying trends, uncovering root causes and implementing strategies for improvement cannot happen due to difficulty to collect, correlate and analyze data from these systems Inability to scale solution • Scalability is a challenge with standalone . Adding redundancy eliminates single points of failure and increases reliability, but also increases complexity and systemic risk, threatening reliability altogether • If a data center needs to scale its technology infrastructure, extensive reprogramming and reconfiguration is often required, which also increases costs and time to solution
- 17. Managing Data Centers – Key Challenges Proprietary technologies • Systems that leverage proprietary technologies generally face a higher likelihood of failure when these systems are interfaced with components from other manufacturers or software from third-party suppliers Integration of advanced hardware and software technologies can deliver insight through information for the highest level of efficiency, reliability, and uptime — enabling a sustainable competitive advantage
- 18. Data Center Strategy and Key Components
- 19. Key Components of a Holistic Data Center Strategy • As downtime costs continue to rise, forward-looking strategies must address various infrastructure challenges and encompass both hardware and software solutions that are scalable, open, and tightly integrated—working together as a comprehensive system • Integrating existing infrastructures with high availability control and advanced software capabilities such as monitoring or alarming can significantly increase operational performance by reducing human error, improving system availability and performance, and reducing energy consumption • Following are five key components that are critical to helping data centers shift toward long-term maintainability, efficiency, and reliability for facility optimization
- 20. Key Components of a Holistic Data Center Strategy 1. High availability control Availability control is at the core of data center performance and helps data centers ensure data protection, continuous operations, and recovery, in the event of an outage A high availability solution that synchronizes systems at the beginning and end of each logic scan execution can keep all variable data the same—providing fast, full system synchronization and bumpless switchover for maximized reliability Why high availability control? Reliability: Performance: Precision: Insure consistent critical control system availability Quick response to catastrophic failures No loss of data during system failover Control system malfunctions must not impact uptime Concurrent maintainability Eliminate single point of failure
- 21. Key Components of a Holistic Data Center Strategy 2. Advanced data collection • Continuous operation and performance improvements of all data center systems are only as good as the runtime data collected for analysis and action from all the infrastructure systems A key challenge for many data centers today is the difficulty of integrating many disparate hardware and software systems and standalone products into a common data collection and management strategy • 3. Advanced analytics • With the data collected, advanced analytics can then help extract knowledge from the data, which is critical to driving corrective action for maximized performance and reliability • Advanced analytics can provide data centers with critical context to otherwise static historical and real-time data, increasing data integrity and enabling better decision making for improved facility management and performance
- 22. Key Components of a Holistic Data Center Strategy 4. Critical alarm response • Leveraging next-generation alarm response management software can help data centers reduce costs and risk by ensuring the correct response to the small subset of critical alarms—increasing system availability and reducing liability exposure and costs • Alarm response management software can help operators make better decisions by providing information and guidance with the exact responses needed to address critical alarms. It also helps track performance and allows managers to review results and improve response instructions.
- 23. Key Components of a Holistic Data Center Strategy Why advanced data collection, analytics and critical alarm response? Reliability: Insight: Precision: Measure and analyze system performance metrics Quick response to catastrophic failures Data collection to assess downtime events Concurrent maintainability Eliminate human error Energy optimization Leverage redundancy depth to detect and repair failures while maintaining continuous system operation
- 24. Key Components of a Holistic Data Center Strategy 5. Integration of all systems • As improving efficiency of power supply systems is a key goal, it is only through an integrated, holistic approach that data centers can gain real-time views across systems and global comparative analytics for complete facility risk assessment • The selected technology for managing power supply systems and infrastructure should be open and flexible for seamless integration with a data center’s current systems as well as its future technologies because integration enables critical understanding into the overall state of the facility
- 25. Key Components of a Holistic Data Center Strategy Total System Integration • High Availability Control •Power Generation ‐ Conventional ‐ Alternatives ‐ Back-up •Power Quality ‐ UPS ‐ Monitoring & Optimization •Electrical Distribution ‐ Switchgear ‐ Intelligent Control ‐ ATS •Cooling: IT & Facility ‐ HVAC ‐ Automation ‐ Drives •Facilities Infrastructure ‐ Security ‐ Lamps ‐ Lighting Control • Facility Management Integration, Data Acquisition, Analysis, Visualization, Optimize & Control – Real-time views across systems – Global comparative views & analysis – Prioritized / dollarized analysis for improvement decision making – Advanced Control Strategies •IT Infrastructure ‐ Racks ‐ Servers ‐ Software
- 26. GE Data Center – An Example
- 27. GE Data Center Challenges • 30,000 feet2 raised floor, 3,800 servers, 2/5MVA UPS • Reducing water & electrical consumption • Improving maintainability • Cooling • Disparate & overlapping hardware & software systems for redundancy & reliability • 11% energy savings • 20% water savings • 50% chemicals reduction Operational Results • Complete facility visualization • Insight into prioritized actions through actionable data • Automatic PUE & EPA compliance data collection • Multiple maintenance contracts eliminated • High Availability control of data center cooling system
- 28. Conclusion • Data centers need to capitalize on the real opportunity available to maximize their system reliability for continuous operations by moving toward an integrated and holistic technology approach • High availability redundant control, combined with a set of critical software capabilities, allows data center users to precisely monitor and control all critical systems; understand cross-functional synergies, constraints, and performance and cost metrics; and immediately respond to critical events with corrective action • By implementing the right mix of enabling technologies that provide critical capabilities, data centers can position themselves to attain the highest level of reliability, availability, and operational efficiency—optimizing the management of their facility for a sustainable competitive advantage
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