Scheduling Matters: Area-Oriented Heuristic for Resource Management

Parallel and distributed systems that provide compute resources on demand are convenient, cost-effective, and becoming increasingly common. Boosting workload performance in such environments through scheduling has been of great interest, as users and providers aim to increase parallelism and reduce execution times. For modern data centers, leaving a smaller carbon footprint while maintaining high performance and low cost is becoming the next big challenge. With this in mind, we analyze the relative impacts on resource utilization of three well-motivated platform-oblivious scheduling heuristics. We simulate over 50,000 DAG workflow executions and measure performance, cost, and resource utilization under the three scheduling heuristics. Our results provide insights to better enable high-performance execution of workflows and advanced capacity planning while increasing resource utilization and reducing costs.