dcsimg

Essential Elements in Building an Agile Data Center

  • Essential Elements in Building an Agile Data Center-

    Predictive Data Analytics

    IT organizations and service providers typically can't predict what virtualized workloads they might add or modify next. They can only guess at performance or capacity requirements and often buy more capacity than they really need.

    Data analytics is crucial as it provides IT pros with information to make better decisions about application behaviors and storage needs. Predictive analytics in particular make it possible for data center professionals to trend their use of capacity and performance, and anticipate future needs. Advanced tools will also allow the user to model scenarios, so they can precisely assess the impact of changes to their virtual footprint.

    Importantly, this type of analysis doesn't take minutes or hours — modern technologies like Elasticsearch make it possible to crunch thousands or even millions of data points in seconds. The bottom line is that predictive analytics replaces guesswork with visibility and precision.

1 | 2 | 3 | 4 | 5 | 6 | 7

Essential Elements in Building an Agile Data Center

  • 1 | 2 | 3 | 4 | 5 | 6 | 7
  • Essential Elements in Building an Agile Data Center-5

    Predictive Data Analytics

    IT organizations and service providers typically can't predict what virtualized workloads they might add or modify next. They can only guess at performance or capacity requirements and often buy more capacity than they really need.

    Data analytics is crucial as it provides IT pros with information to make better decisions about application behaviors and storage needs. Predictive analytics in particular make it possible for data center professionals to trend their use of capacity and performance, and anticipate future needs. Advanced tools will also allow the user to model scenarios, so they can precisely assess the impact of changes to their virtual footprint.

    Importantly, this type of analysis doesn't take minutes or hours — modern technologies like Elasticsearch make it possible to crunch thousands or even millions of data points in seconds. The bottom line is that predictive analytics replaces guesswork with visibility and precision.

Today, about 75 percent of all workloads in data centers are virtualized and this number is only expected to grow. The biggest challenge IT admins face is that conventional storage is ill-equipped to deal with virtualization because the storage is built for physical workloads.  

Problems arise as legacy storage, with logical unit numbers (LUNs) and volumes that might house tens or hundreds of individual virtual machines (VMs), causes resident VMs to fight over limited resources. This is a phenomenon called the "noisy neighbor." While one common solution is to throw more high-performance flash storage at the problem, this alone cannot fix the problem. It simply postpones dealing with the underlying problem (LUNs). Costs can spiral out of control as an all-flash storage architecture dedicated to LUNs and volumes does not necessarily overcome the pain points of managing virtual workloads.

While many companies aspire to build cloud-scale infrastructures with agility and automation for diverse virtualized workloads, they have been forced to choose between limited scale-out that requires a large number of disks or expensive and inefficient scale-out. According to Chuck Dubuque, senior director of product and solution marketing for Tintri, five key areas that are critical for successful data center modernization efforts include speed, quality of service (QoS), disaster recovery, predictive data analytics, and manageability at scale.