Gradient-Based Scheduler for Scientific Workflows in Cloud Computing

Abstract

It is becoming increasingly attractive to execute workflows in the cloud, as the cloud environment enables scientific applications to utilize elastic computing resources on demand. However, despite being a key to efficiently managing application execution in the cloud, traditional workflow scheduling algorithms face significant 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 experiments are conducted on well-known scientific workflows 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 satisfiability and cost optimization, thereby verifying its advantages in addressing workflow scheduling problems.