Issue No. 01 - Jan.-March (2018 vol. 6)
Javier Diaz-Montes , Rutgers Discovery Informatics InstituteRutgers University
Manuel Diaz-Granados , Rutgers Discovery Informatics InstituteRutgers University
Mengsong Zou , Rutgers Discovery Informatics InstituteRutgers University
Shu Tao , IBM T.J. Watson Research Center
Manish Parashar , Rutgers Discovery Informatics InstituteRutgers University
Cloud computing is emerging as a viable platform for scientific exploration. Elastic and on-demand access to resources (and other services), the abstraction of “unlimited” resources, and attractive pricing models provide incentives for scientists to move their workflows into clouds. Generalizing these concepts beyond a single virtualized datacenter, it is possible to create federated marketplaces where different types of resources (e.g., clouds, HPC grids, supercomputers) that may be geographically distributed, are collectively exposed as a single elastic infrastructure. This presents opportunities for optimizing the execution of application workflows with heterogeneous and dynamic requirements, and tackling larger scale problems. In this paper, we introduce a framework to manage the end-to-end execution of data-intensive application workflows in dynamic software-defined resource federation. This framework enables the autonomic execution of workflows by elastically provisioning an appropriate set of resources that meet application requirements, and by adapting this set of resources at runtime as the requirements change. It also allows users to customize scheduling policies that drive the way resources federated and used. To demonstrate the benefits of our approach, we study the execution of two different data-intensive scientific workflows in a multi-cloud federation using different policies and objective functions.
Dynamic scheduling, Cloud computing, Computational modeling, Monitoring, Quality of service, Runtime
J. Diaz-Montes, M. Diaz-Granados, M. Zou, S. Tao and M. Parashar, "Supporting Data-Intensive Workflows in Software-Defined Federated Multi-Clouds," in IEEE Transactions on Cloud Computing, vol. 6, no. 1, pp. 250-263, 2018.