Biogeography-Based Optimization (BBO) Algorithm for Energy and Performance-Aware Service Workflow Scheduling in a Cloud Computing Environment

Cloud computing is viewed as a new model of service provisioning in distributed systems that encourages researchers to take advantage on executing scientific applications such as workflows. One of the most important issues in clouds is the optimal workflow scheduling, i.e., scheduling resources when each resource may be used by more than one task, and may be needed at different points in time for satisfying users QoS requirements. Existing heuristic and evolutionary algorithms mainly focus on optimizing the time and cost without paying much attention to energy consumption. In this paper, we propose a new approach based on the use of biogeography based heuristic to optimize scheduling performance by (a) formulating a model for task-resource mapping to minimize the overall energy consumption using dynamic voltage scaling (DVS) technique; and (b) designing a heuristic that uses biogeography-based optimization (BBO) to solve task resource mapping based on the proposed model. Our approach is validated by simulating a complex workflow application.