As your organization strives to achieve greater innovation, insight, and agility, your IT department must transform itself to handle new compute-intensive and data-intensive workloads. A modern data center must accommodate new technologies ranging from high-performance computing and big data analytics to virtualization, containerization, and cloud. And of course, all of this must be achieved while keeping budgets reined in. What you need is a Dynamic Data Center.
Organizations everywhere are asking themselves three important questions. How to accelerate innovation to better serve their customers, how to leverage data to gain better insight into their business and operations, and how to increase their agility to take advantage of new ideas and new opportunities.
These business imperatives are driving the need to process an emerging set of computational and analytical workloads with very different characteristics from traditional ones. These workloads include data analytics and sequencing, deep learning, real-time modeling and simulation, rapid application development and deployment, and computer-aided design (CAD) and engineering (CAE).
Whether they’re trying to find their unique competitive edge, predict the next must-have customer trend, or unearth the latest scientific revelation, organizations that are turning to these advanced technologies find their fortunes resting on IT’s ability to run increasingly sophisticated computational and analytical workloads in a more agile and dynamic way. IT teams are faced with the rapid adoption of technologies ranging from high-performance computing and big data analytics to virtualization, containerization, and cloud computing.
IT teams can benefit from a unifying thread that connects all of these technologies: clustering. Clustered systems running the Linux operating system, have become the de facto standard for building out capabilities in each of these emerging areas. Two of the most popular types of workloads to run on Linux clusters are high-performance computing and big data analytics.
Increasingly, organizations are identifying both HPC and big data analytics as business-critical disciplines with HPC bringing a real-time, theoretical, simulation, and modeling-based dimension to decision-making, while big data analytics examines actual data sets gathered over an extended period of time.
To achieve this blended approach, organizations need the ability to run clusters of different types across a common infrastructure. This may mean running separate clusters side by side, sharing a common storage layer. However, greater efficiency can be achieved by using the same computing resources and repurposing them dynamically to accommodate different types of workloads.
As organizations strive to be more agile in response to rapid-fire business requirements, mainstream data centers are adopting virtualization, containerization, and cloud computing technologies. Whether implemented separately or in combination, each of these offers IT the potential to use resources more efficiently, while also delivering services more responsively. The ability to spin up a virtualized clusters within minutes, tailored to a user’s specified level of computing needs and length of required access, can deliver massive improvements in IT resource efficiency and user satisfaction.
Linux-clustered systems can be a challenge to deploy, manage, and monitor. Some IT organizations may lack the bandwidth, and operational procedures required to absorb clustered IT infrastructure into the data center.
IT teams face the challenge of deploying Linux-clustered systems and must move quickly to implement effective management disciplines across them. Commercial enterprises, especially, require an approach to managing clustered infrastructure that balances flexible and agile execution of business-critical workloads with efficient and effective utilization of IT resources. Over time, most organizations determine that a vendor-independent, commercial-grade cluster management platform is required to achieve this optimal balance.