Clustering-assisted gradient-based optimizer for scheduling parallel cloud workflows with budget constraints

Cloud computing has gradually become one of the most popular platforms for executing scientific applications due to its elastic and on-demand resource provisional capabilities. But, how to effectively schedule a set of parallel workflows to minimize the makespan under their individual budget constraints remains a critical problem. This work proposes a Clustering-assisted Gradient-Based Optimizer (C-GBO) to improve the performance for scheduling workflows in cloud environments. First, it designs a novel individual encoding mechanism including task-VM mapping and task-priority sub-strings to further optimize the makespan by updating both sub-strings simultaneously, especially each element representing task execution order in a task-priority sub-string can take any values within the pre-specified range but not subject to control dependencies among tasks. Second, to address the original GBO’s easiness of falling into local optima brought by its only one best guiding solution, it divides individuals into different groups as their position information by the K-means algorithm and selects the best guiding solution for each group with 50