SAS Workload Management
Go beyond Kubernetes – simplify your analytic workload management on SAS Viya.
Get the most out of your cloud infrastructure spending while ensuring the best experience across your entire analytics platform.
Balance cost with agility.
Optimize Kubernetes compute resources through resource-based load balancing and job prioritization. With SAS Workload Management, you can define priorities for different types of workloads, ensuring that the most important work gets done first – and without interrupting interactive sessions.
Improve throughput, availability and productivity.
SAS Workload Management lets you maximize workload processing efficiency, delivering greater throughput through parallel job processing. Your data scientists spend less time waiting for failed jobs to restart, so they can stay focused on producing the best models.
Simplify administration.
SAS Workload Management is powerful, yet easy to use. A graphical interface lets you centrally manage policies, programs, queues and priorities of SAS jobs running on Kubernetes. With centralized management of all resources, administrators spend less time learning new tools and bouncing between different applications for analytic workloads.
Key Features
Extending the power of Kubernetes by optimizing and prioritizing SAS workloads within a Kubernetes cluster.
Ability to optimize & prioritize analytic compute on Kubernetes
Delivers enterprise-class dynamic workload balancing for users and applications, including rule-based job queues and automatic management of SAS containers on Kubernetes.
Expanded high availability
Includes high availability for analytic workloads, including detection and restart of preempted jobs within the already highly available Kubernetes infrastructure.
Improved performance of analytic workloads
Processes analytics jobs faster by assigning jobs to the right job queue. Identifies serialized workloads that can be separated and processed in parallel.
Real-time monitoring & administration
Provides a web-based tool for monitoring and managing resources, users and jobs. Serves as an interface for configuring and managing high-availability services and defining alerts when thresholds are exceeded.
Runs on your cloud provider's Kubernetes cluster
Lets you manage a wide variety of SAS and open source jobs running on Kubernetes. Choose from leading cloud providers like Azure, AWS and Google, or run on-site with Red Hat OpenShift.
Cloud Providers
Conquer all your analytics challenges – from experimental to mission critical – with faster decisions in the cloud. The latest release of SAS Viya is now available on these cloud providers.
Running the latest version of SAS Viya natively on Microsoft Azure, the SAS Cloud manages your entire analytics platform for optimal performance and value.
Microsoft is our strategic partner and preferred cloud provider. With deep integration and a shared road map, SAS and Microsoft are shaping the future of AI and analytics in the cloud.
Designed to be cloud-native, SAS Viya is tested and approved to leverage the same cloud services used by millions of AWS users.
With a commitment to innovation and open-source cloud principles, SAS Viya brings native AI and advanced analytics to Google Cloud.
SAS Viya is bringing the latest DataOps, AI and ModelOps capabilities to Red Hat OpenShift – the leading enterprise Kubernetes platform, built for your open, hybrid cloud strategy.
Explore More on SAS Workload Management
White Paper
SAS Viya and the cloud: How SAS is changing the game it invented
Learn how SAS Viya takes full advantage of the cloud's scalability, providing a solution that delivers the latest, up-to-date capabilities.
Blog
What you need to know about the future of SAS Grid and SAS Viya. Hint: It’s amazing!
Learn about the powerful, advanced workload management capabilities of SAS Grid Manager, now available on SAS Viya.
Communities
Moving from SAS Grid to SAS Workload Management on SAS Viya
Find out how you can benefit from advanced workload management capabilities, priority-based queues for SAS jobs and more granular control on workload placement.
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