GRID delivers fast, high availability clinical research

Many biopharmaceutical companies are opting for modern grid architectures for their SAS computing infrastructures. They’ve used SAS for decades in clinical research to clean and analyze clinical study data in order to determine safety and efficacy for drugs in their pipeline. During this time, clinical trial sponsors and CROs built traditional server-based SAS computing infrastructures to analyze and report clinical trials data.

But recently, many companies have discovered the flexibility, scalability and cost advantages of a modern grid architecture.

What’s driving the need for a new approach?

While the legacy server-based architecture met the SAS infrastructure requirements of clinical development for decades, a number of factors evolved to prompt many companies to examine a more modern GRID architecture.

Practically every biopharmaceutical company and CRO provides a SAS computing infrastructure for their clinical development organization. Usually, the SAS infrastructure consists of one or more servers located either in a single data center or scattered across the globe based on the business requirements of the organization.

These servers may have widely different speeds and capacities, and may not even have the same SAS software configuration. This lack of consistency places a burden on the user community to completely understand the pros and cons of each server in the configuration when submitting their jobs for processing.

Imagine being the new employee who unknowingly submits a long running, resource intensive “rush job” to the slowest available server and storage configuration – and then wonders why the job is taking so long to complete!

And some companies are running their clinical development SAS infrastructure on a server and storage platform that includes aging or end-of-life hardware. As a result, these companies are typically dealing with issues such as compute or storage capacity shortages, leading to performance issues with jobs running too slow. In addition, single points of failure in the server or storage infrastructure can increase downtime.

Modern clinical development is expanding the business requirements for the clinical development SAS infrastructure through:

  • More data.
  • More complex questions and analyses.
  • Greater need for speed and faster time to insights.

Modern grid architecture provides the cure

Many biopharmaceutical sponsors and CROs are choosing to transition to a flexible, modern grid architecture for their clinical development SAS computing infrastructure. A well-planned grid architecture can result in a SAS computing infrastructure that can grow incrementally and cost effectively, provide high availability, and meet changing business demands with dynamic workload balancing.

After implementing a grid architecture, SAS Grid Manager provides workload balancing to determine where and how each SAS job gets distributed to the grid based on the attributes of each job and the available processing capacity across the grid.

In addition to workload balancing, SAS Grid Manager provides:

  • High availability when using a grid architecture that limits single points of failure.
  • Flexible, low-cost scalability by adding commodity computer infrastructure as needed.
  • The ability to scale with minimal disruption.
  • Job execution time improvements with grid-enabled code.

If you want to learn more about how to implement grid computing in the life sciences industry, this white paper provides some excellent insights into what you can accomplish to create a modern SAS programming environment in a global pharmaceutical company. A grid architecture may be what you need to help your company respond in a cost-effective manner to increasing data volumes, more complex analyses and the need for faster time to insight.

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    Welcome to the SAS Health and Life Sciences blog. We explore how the health care ecosystem – providers, payers, pharmaceutical firms, regulators and consumers – can collaboratively use information and analytics to transform health quality, cost and outcomes.
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