Multi-objective scheduling of many tasks in cloud platforms
We propose an ordinal optimized method for multi-objective many-task scheduling. We prove the suboptimality of the proposed method through mathematical analysis. Our method significantly reduces scheduling overhead by introducing a rough model. Our method delivers a set of semi-optimal good-enough scheduling solutions. We demonstrate the effectiveness of the method on a real-life workload benchmark.
ordinal optimization cloud computing many-task computing performance evaluation virtual machines workflow scheduling