HPCwire – The Future of Workload Management

October 12, 2012 – Chad Harrington

The essayist Paul Valery once quipped, “The trouble with our times is that the future is not what it used to be.” Surely, there is truth in that. The future of workload management continues to evolve; it is definitely not what it used to be.

As we look toward the future of workload management, we see three major trends: application insight, big data awareness, and HPC clouds. The trends are inter-related and we’ll discuss each in turn.

Application Insight

First, workload managers need to have greater insight into the applications they run. The more deeply the workload manager can understand the workload, the more efficiently it can schedule, manage, and adapt the computing environment. Today’s workload managers understand basic workload requirements and can track an application’s progress. However, there is more that can be done. In the future, we’ll see more emphasis on understanding an application’s purpose and key metrics. If the workload manager understands the application’s current and future needs, it can make much more optimal decisions. Metrics such as I/O bandwidth, memory allocation, storage space, CPU and GPU cycles, etc., all help the workload manager understand an application in order to optimally manage it.

Application-specific metrics, such as simulations per second, genes matched per second, etc., are more important than generic CPU and memory metrics. They best describe an application’s performance. By monitoring these application-specific metrics, the workload manager can understand how system-level variables impact application performance. For instance, an application-aware workload manager could observe that a particular application’s performance degrades substantially when it runs at the same time as a another specific application. Armed with this data, the workload manager can make sure those two conflicting applications do not run at the same time.

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