Energy-aware hierarchical scheduling of applications in large scale data centers

With the rapid advance of cloud computing, large scale data center plays a key role in cloud computing. Energy consumption of such distributed systems has become a prominent problem and received much attention. Among existing energy-saving methods, application scheduling can reduce energy consumption by replacing and consolidating applications to decrease the number of running servers. However, most application scheduling approaches did not consider the energy cost on network devices, which is also a big portion of power consumption in large data centers. In this paper we propose a Hierarchical Scheduling Algorithm for applications, namely HSA, to minimize the energy consumption of both servers and network devices. In HSA, a Dynamic Maximum Node Sorting (DMNS) method is developed to optimize the application placement on servers connected to a common switch. Hierarchical crossing-switch adjustment is applied to further reduce the number of running servers. As a result, both the number of running servers and the amount of data transfer can be greatly reduced. The time complexity of HSA is Θ(n + log(logn)), where n is the total number of the severs in the data center. Its stability is verified via simulations. Experiments show that the performance of HSA outperforms existing algorithms.