A Lightweight Model for Right-Sizing Master-Worker Applications

When running a parallel application at scale, a resource provisioning policy should minimize over-commitment (idle resources) and under-commitment (resource contention). However, users seldom know the quantity of resources to appropriately execute their application. Even with such knowledge, over- and under-commitment of resources may still occur because the application does not run in isolation. It shares resources such as network and filesystems. We formally define the capacity of a parallel application as the quantity of resources that may effectively be provisioned for the best execution time in an environment. We present a model to compute an estimate of the capacity of master-worker applications as they run based on execution and data-transfer times. We demonstrate this model with two bioinformatics workflows, a machine learning application, and one synthetic application. Our results show the model correctly tracks the known value of capacity in scaling, dynamic task behavior, and with improvements in task throughput.