Service capability aware big data workflow scheduling approach in cloud datacentre.

With the increasing application of cloud computing, big data workflow scheduling in cloud datacentre also become an important focus of research. How to guarantee minimal scheduling length is the main challenge in scheduling workflow in cloud-based environments. The main limitation of proposed approaches stems is that they overlook the service capability support levels of the virtual machines and service capability requirement levels of the different tasks in a workflow, thus risking resulting in extremely poor processing efficiency. We propose a service dynamic level scheduling algorithm in cloud datacentre (Cloud-SDLS) that consists of three stages: virtual machines' service capability support computation, tasks' service capability requirement computation, and service dynamic level scheduling. Experimental results show that the proposed algorithms effectively satisfy the QoS in service capability requirement. It is significant to shorten workflow completion time in practice.