A new energy-aware task scheduling method for data-intensive applications in the cloud

Maximizing energy efficiency while ensuring the user's Service-Level Agreement (SLA) is very important for the purpose of environmental protection and profit maximization for the cloud service providers. In this paper, an energy and deadline aware task scheduling method for data-intensive applications is proposed. In this method, first, the datasets and tasks are modeled as a binary tree by a data correlation clustering algorithm, in which both the data correlations generated from the initial datasets and that from the intermediate datasets have been considered. Hence, the amount of global data transmission can be reduced greatly, which are beneficial to the reduction of SLA violation rate. Second, a "Tree-to-Tree" task scheduling approach based on the calculation of Task Requirement Degree (TRD) is proposed, which can improve energy efficiency of the whole cloud system by reducing the number of active machines, decreasing the global time consumption on data transmission, and optimizing the utilization of its computing resources and network bandwidth. Experiment results show that the power consumption of the cloud system can be reduced efficiently while maintaining a low-level SLA violation rate. A new data correlation clustering method is proposed to reduce data transmission.A "Tree-to-Tree" heuristic energy-aware task scheduling strategy is proposed.The utilization of network bandwidth and computing capacities can be improved.