Multi-Tree Genetic Programming Hyper-Heuristic for Dynamic Flexible Workflow Scheduling in Multi-Clouds

Abstract

Multi-cloud is a promising paradigm due to its advantages such as avoiding vendor lock-in and optimising costs. This paper focuses on dynamic flexible workflow scheduling with minimum total monetary cost in multi-clouds, considering multiple categories of services for each cloud with different configurations and billing methods. Existing studies generally ignore the characteristics and states of each individual cloud when making schedules, which may be ineffective regarding cost savings and quality of service. To address this issue, we propose to introduce a cloud selection decision on top of the existing task selection and resource selection decisions to help us select appropriate resource for task in an overall cost-effective cloud. To automatically learn the task, cloud and resource selection rules simultaneously, we propose a new genetic programming with multi-tree representation based on a customised discrete event-driven dynamic workflow scheduling simulator. Simulation results based on two real-world data traces show that the proposed algorithm performs significantly better than the state-of-the-art algorithms in terms of reducing the rental costs and deadline deviation, and improving the success rate. The results also show that the superiority of the proposed algorithm lies in the ability to select an appropriate cloud resource for a task.

Zaixing Sun

Ph.D

I am currently pursuing a PhD degree in computer science and technology with the School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China. I am also a visiting student with the Evolutionary Computation Research Group, Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand. My research interests include cloud computing, intelligent optimisation and scheduling.