Modeling and Synthesizing Task Placement Constraints in Google Compute Clusters
Evaluating the performance of large compute clusters requires benchmarks with representative workloads. At Google, performance benchmarks are used to obtain performance metrics such as task scheduling delays and machine resource utilizations to assess changes in application codes, machine configurations, and scheduling algorithms. Existing approaches to workload characterization for high performance computing and grids focus on task resource requirements for CPU, memory, disk, I/O, network, etc. Such resource requirements address how much resource is consumed by a task.