Resource Scheduling is crucial to data centers. However, most previous works focus only on one-dimensional resource models which ignoring the fact that miltiple resources similtaneousry utilized, including CPU, memory and network bandwidth. As cloud computing allows uncoordinated and heterogeneous users to share a data center, competition for multiple resources has become increasingly severe. Motivated by the differences on integrated utilization obtained from different packing schemes, in this paper we take the scheduling problem as a multi-dimensional combinatorial optimization problem with constraint satisfaction. With NP hardness, we present Multiple attribute decision based Integrated Resource Scheduling (MIRS), and a novel heuristic algorithm to gain the approximate optimal solution. Refers to simulation results, in face of various workload sets, our algorithm has significant superiorities in terms of efficiency and performance compared with previous methods.