Cost minimization for bag-of-tasks workflows in a federation of clouds

Abstract

We address the problem of resource allocation for bag-of-tasks (BoT) workflows in a federation of clouds and formulate it as an integer linear programming problem. The proposed model minimizes financial cost including fees for running VMs and fees for data transfer, and fulfills deadline and resource constraints in the clouds. We also formulate the problem of BoT scheduling in the hybrid clouds, and compare the financial cost in the federation of clouds with that in the hybrid clouds. Moreover, this paper discusses sensitivity analysis to investigate stability in the related allocation problem. Numerical results show that the resource allocation in the federation is considerably preferred to that in the hybrid clouds in terms of stability and cost-saving. In this paper, we also propose an approach named GRASP-FC for obtaining an approximate optimal solution of BoT scheduling in the federation. GRASP-FC is an extension of greedy randomized adaptive search procedure (GRASP), and it can be of great interest from the computational points of view.