Workflow scheduling in cloud computing environment with classification ordinal optimisation using SVM

Every day, civilisation generates more and more data. The processing cost and performance issues of this massive set of data have become a challenge in distributed computing. Processing multitasks workloads for big-data in a dynamic environment requires real-time scheduling and the additional complication of generating optimal schedules in a large search space with high overhead. In this paper, we propose an adaptive workflow management system that uses ordinal optimisation to acquire suboptimal schedules in much less time. We then introduce a prediction-based workflow scheduler model that predicts the execution time of the next coming workflow by using a support vector machine (SVM). We used a real application Montage workflow for large-volume image data, and the experimental results show that our classification ordinal optimisation (COO) outperforms other existing methods.