An energy-aware bi-level optimization model for multi-job scheduling problems under cloud computing

Recently, how to reduce huge energy consumption of data centers has caught wide attention in cloud computing. One effective way is to improve the energy efficiency of servers. To achieve this goal, we propose a new energy-aware multi-job scheduling model based on MapReduce in this paper. In the proposed model, first, the variation of energy consumption with the performance of servers is taken into account; second, since network bandwidth is a relatively limited resource in cloud computing, 100 % data locality is guaranteed; last but not least, considering that task-scheduling strategies depend directly on data placement policies, we formulate the problem as an integer bi-level programming model. It is worth noticing that there are usually tens of thousands of tasks to be scheduled in the cloud, so this is a large-scale optimization problem. In order to solve it efficiently, a local search operator is specifically designed, based on which, a bi-level genetic algorithm is proposed in this paper. Finally, numerical experiments indicate the effectiveness of the proposed model and algorithm.