A Seeding-based GA for Location-Aware Workflow Deployment in Multi-cloud Environment
Tao Shi, Hui Ma, Gang Chen · 2019
To gain technical and economic benefits, various enterprises are increasingly moving their workloads to the cloud. Multi-cloud environment makes it possible to coordinate access and utilize multiple cloud resources. When business application developers host their business process in the cloud, they face the issue of choosing which cloud to deploy the instance-intensive business workflows. However, the existing studies rarely consider the optimization techniques for organizing cloud services with respect to various criteria, such as cost and performance. In this paper, we propose a seeding-based GA approach to address the multi-cloud workflow deployment problem, i.e. selecting and leasing virtual machines (VMs) to minimize deployment cost and response time. Experimental results show that the proposed GA approach with seeding strategy outperforms the existing approach proposed in the literature and standard GA algorithm.