Model-Driven Placement of Compute Tasks and Data in a Networked Utility
An important problem in resource management for networked resource-sharing systems is the simultaneous allocation of multiple resources to an application. Self-optimizing systems must co-allocate resources in a way that reconciles competing demands and maximizes global system objectives under dynamic conditions. The authors propose a simple model-driven approach to estimate the performance of a candidate assignment of resources, and select the best candidate to meet local or global goals. In this work, they address the placement of batch compute tasks and data in a network of compute and storage sites.