Location-Aware and Budget-Constrained Application Replication and Deployment in Multi-Cloud Environment

Abstract

To gain technical and economic benefits, enterprise application providers are increasingly moving their workloads to the cloud. With the increasing number of cloud resources from multiple cloud providers at different locations with differentiated prices, application providers face the challenge to select proper cloud resources to replicate and deploy applications to maintain low response time and high quality of user experience without running into the risk of over-spending. In this paper, we study the global-wide cloud application replication and deployment problem considering the application average response time, including particularly application execution time and network latency, subject to the budgetary control. To address the problem, we propose a GA-based approach with domain-tailored solution representation, fitness measurement, and population initialization. Extensive experiments using the real-world datasets demonstrate that our proposed GA-based approach significantly outperforms common application placement strategies, i.e., NearData and NearUsers, and our recently proposed hybrid GA approach.