Gradient-Based Scheduler for Scientific Workflows in Cloud Computing.

It is becoming increasingly attractive to execute work-flows in the cloud, as the cloud environment enables scientific applications to utilize elastic computing re-sources on demand. However, despite being a key to ef-ficiently managing application execution in the cloud, traditional workflow scheduling algorithms face sig-nificant challenges in the cloud environment. The gradient-based optimizer (GBO) is a newly proposed evolutionary algorithm with a search engine based on the Newton's method. It employs a set of vectors to search in the solution space. This study designs a gradient-based scheduler by using GBO for workflow scheduling to minimize the usage costs of workflows under given deadline constraints. Extensive experi-ments are conducted on well-known scientific work-flows of different sizes and types using WorkflowSim. The experimental results show that the proposed scheduling algorithm outperforms five other state-of-the-art algorithms in terms of both the constraint sat-isfiability and cost optimization, thereby verifying its advantages in addressing workflow scheduling prob-lems.