SkySpark and BuildingFit for Data Center Analytics

SkySpark Analytics for Data Center Operations

Overview

Data Centers have unique operating needs, equipment systems, and of course, reliability requirements. Data centers require very controlled environments, and each client’s data processing needs require unique sets of data collection and monitoring rules. The use of SkySpark can help data center operators understand and improve operations, reduce costs, and insure reliability.

Beyond what is stored on the facility’s servers from outside sources, data centers also produce their own data about the operation of servers, HVAC systems, and power systems that make up the backbone of the building itself. While buildings of all types benefit from a “single drivers seat” to enable monitoring and management of these disparate systems, data centers present an opportunity to take that approach a step further to solve long-standing issues unique to these types of facilities. At the forefront of this approach is an increased focus on analytics made possible when all systems communicate properly.

With its ability to communicate over Modbus, BACnet and SNMP, and support for other standard equipment protocols, SkySpark helps bridge the gap between traditionally segregated “silos” of systems found in data center facilities enabling all essential equipment systems data to be combined for analysis.

Another challenge for data center operators is “what-if” analysis. If an operator has maintenance due and needs to take down a PDU (Power Distribution Unit) or router, how will the system respond? To solve this problem, an operator can enter a testing scenario where supporting components can be “failed” to determine how the remaining systems would respond.

For instance, if a UPS battery is scheduled for replacement, the system can verify that the opposite bus is correctly setup to power the affected servers and infrastructure. SkySpark then analyzes the data generated during the test to identify and present results.  Implementing SkySpark analytics can drive strategies and forecasting that allow data center operators to significantly improve the performance and reliability of their systems.

Tools for Data Analytics Needs of All Types

SkySpark is a fully programmable analytics platform. This allows building systems experts to implement their own rules that capitalize on their extensive and highly specialized knowledge of building and equipment systems and address the reality that all buildings and projects are unique.

As we know, the modern data center is an incredibly complex system. Managing equipment, maintaining networks, regulating energy and environmental controls, and providing both physical and cyber security, the data center faces a wide array of challenges in a typical day.

Keeping that complex system up and running is difficult enough without accounting for potential problems that might arise in the future. Fortunately, the last several years have seen significant improvements in predictive analytics, which can be a powerful tool for data centers seeking to provide consistently superior service.

Simply put, predictive analytics utilizes statistical algorithms and AI-driven machine learning techniques to analyze data gathered over time to anticipate future outcomes. The SkySpark library also includes Machine Learning functions that provide support for supervised learning for prediction and forecasting through regression-based approaches, and classification using Support Vector Machine (SVM) techniques.

Here are a few ways predictive analytics can benefit a data center:

Anticipate Changing Needs

Data centers are undergoing constant changes because the needs of their customers are constantly shifting. Companies frequently spin their computing and data storage requirements up or down based on demand, while new customers place new pressures upon existing server arrangements. Server racks need to be added or moved on short notice, especially for colocation centers trying to accommodate new equipment.

Every adjustment has knockdown effects throughout the facility’s infrastructure. Boosting deployment density affects power requirements, which in turn changes cooling demands, and so on. In such a complex system, it can sometimes be difficult to anticipate the potential consequences of any change.

Simulation programs driven by predictive analytics can solve this problem by running through, model-based simulations that take into account a variety of complex variables. They can anticipate how moving equipment will affect heat dispersion or network latency. Armed with this information, data center technicians can manage equipment and services with minimal disruption. By flagging potential issues, predictive analytics simulations can also help to avoid major problems that would otherwise require significant time and effort to troubleshoot and resolve.

Prepare for Disaster Scenarios

Unexpected problems such as faulty backup systems, power overloads, and cooling failures can not only lead to significant downtime but inflict physical damage to equipment that results in data loss. While natural disaster like hurricanes are certainly a concern, seemingly minor equipment failures can lead to catastrophic consequences if they’re not addressed promptly.

Predictive analytics can model a wide range of disaster scenarios, which helps data center personnel to formulate plans of action to resolve the problems. If cooling systems were to fail, for example, simulations can determine how much time can elapse before rising temperatures force other systems to shut down. While good data centers perform regular tests and conduct drills to test disaster readiness, there are some scenarios that are difficult to play out. After all, deliberately overloading a server to see how it affects the rest of the units in the rack would be an expensive and risky exercise. With predictive analytics, however, data centers can find out what they can expect to happen should such a scenario occur.

Better Power Management

Managing power is one of the most critical tasks carried out continuously in any data center. The slightest fluctuations can have tremendous consequences, affecting temperature levels and server performance. Power requirements are anything but static, especially for customers who need a lot of processing power to carry out their business operations.

Fortunately, today’s data centers are equipped with sensors and systems that carefully monitor power usage down to the server level. They can determine when power usage increases and what periods of time utilize less power. Armed with this data, predictive analytics can model power usage trends and anticipate future demands, which can improve reliability and lead to significant cost savings.

Improved Uptime

Delivering reliable services is the primary goal of any data center. If customers cannot count on a facility to provide reliable uptime, they are going to start looking elsewhere for their data needs. By deploying predictive analytics to predict network usage trends, data centers can structure their deployments and manage resources to ensure that customers always have access to their data and applications.

But usage patterns are only one aspect of prediction. Data centers rely on their equipment functioning at an optimum level to provide consistent services. Predictive analytics can estimate the lifespan of much of that equipment based on performance patterns. If a simulation indicates that a server is likely to fail within the next six months, the data center can make plans to replace it before it fails, which is not only more cost effective, but also ensures that there will be minimal disruption to services.

By harnessing the power of predictive analytics, data centers can better prepare for problems they may encounter in the future. Improved preparation helps to reduce costs, which allows facilities to invest in other areas to serve customers more effectively. Most importantly, predictive analytics can deliver measurable benefits in the form of better uptime, allowing data center customers to focus on growing their business with the reassurance that their data will continue to be secure and accessible.

Conclusion

Data centers require very controlled environments. The utilization of the SkySpark platform provides flexible, fast, and highly visual output of analytic information for both the data center and the client.  By using analytics and “sparks” as an alerting system, SkySpark can assist in providing:

  • Customized alert time for further client knowledge and control of sensitive environment.
  • Project manager’s information to know exactly where to look when alerts are detected.
  • Historic charts to measure averages per year and calculate actual cost savings against key performance indicators.

 

BuildingFit

Building Fit
+1-385-246-3759
contact@BuildingFit.com

1997 South 1100 East
Salt lake City, Utah, 84106

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