Workflow scheduling in the cloud is a challenging multi-objective optimization problem where an efficient scheduling algorithm is required to optimize both performance and cost. Despite the huge body of work on designing workflow scheduling algorithms, the differences in the experiment settings, VM instances, sets of baseline algorithms, and the choice of reference point for hypervolume calculation make it hard to determine the best-performing methods. In this work, we aim to determine the current state-of-the-art approach by implementing and benchmarking recently published algorithms on a unified benchmark consisting of a large set of real-world DAGs and Amazon EC2 instance types. The experiments show that VMALS and CMSWC are the current state-of-the-art algorithms demonstrating superior quality of Pareto front in terms of hypervolume on all tested DAGs.