Modeling and experimenting combined smart sleep and power scaling algorithms in energy-aware data center networks
Recent surveys have shown that the energy consumption in a data center considerably contributes to its operation costs. A remarkable part of the large energy volume consumed in data centers today is due to the over-provisioning of such network resources as switches, links, and servers to meet the stringent requirements on reliability. Therefore performance and energy issues are important factors for the design of large multi-tier data centers that can support multiple services. However, the design, analysis, and experiments of such a large and complex system often suffer from the lack of appropriate experimental infrastructures. In this paper, we firstly propose a new energy saving scheme that combines smart sleeping and power scaling algorithms. An energy analysis model is then proposed to calculate the energy saving bounds in case of low and high traffic utilization. We also present a platform for in-depth analysis of energy-aware data center networks, which is a combination of hardware testbed and emulation. Based on OpenFlow technology, the experimental platform is designed to capture details of energy consumed by all network components such as links, ports, and switches under different scenarios. Analytical and emulation results show that the combined algorithm improves the energy saving under the varied traffic utilization.
data center network testbed openflow energy efficiency cloud computing