Improving the Network Energy Efficiency in MapReduce Systems
Lin Wang, Fa Zhang, Zhiyong Liu · 2013
Apart from servers, the energy consumed by enormous amount of network devices in data centers also emerges as a big problem. Existing work on energy- efficient data center networking primarily focuses on traffic engineering to consolidate flows and shut down unused devices, not considering another important factor, virtual machine assignment, which has been shown to have a big influence on traffic engineering. Moreover, the lack of information about upper layer applications leads to misunderstand the traffic patterns of the network. This may result in poor effectiveness in the traffic-based optimization in practice. In this paper, we aim to achieve better network energy efficiency in MapReduce systems by combining virtual machine assignment and traffic engineering. By exploiting the characteristics of MapReduce applications, we provide a unified model to describe this problem. Due to its NP-hardness, a general framework is proposed to solve it, where virtual machines are first clustered and then different virtual machine assignments are generated greedily and a local search procedure is used to improve them. The local search procedure depends on the results of an energy-efficient routing provided by GEERA. GEERA is an approximate algorithm designed to select routing paths for flows. Experimental results confirm the efficiency of GEERA, as well as the overall framework. By using this framework, up to $20\%$ more energy savings can be achieved compared with sole traffic engineering solutions.