The proliferation of new online Internet services has substantially increased the energy consumption in wired networks, which has become a critical issue for Internet service providers. In this paper, we target the network-wide energy-saving problem by leveraging speed scaling as the energy-saving strategy. We propose a distributed routing scheme-HDEER-to improve network energy efficiency in a distributed manner without significantly compromising traffic delay. HDEER is a two-stage routing scheme where a simple distributed multipath finding algorithm is firstly performed to guarantee loop-free routing, and then a distributed routing algorithm is executed for energy-efficient routing in each node among the multiple loop-free paths. We conduct extensive experiments on the NS3 simulator and simulations with real network topologies in different scales under different traffic scenarios. Experiment results show that HDEER can reduce network energy consumption with a fair tradeoff between network energy consumption and traffic delay.

The popularization of cloud computing has raised concerns over the energy consumption that takes place in data centers. In addition to the energy consumed by servers, the energy consumed by large numbers of network devices emerges as a significant problem. Existing work on energy-efficient data center networking primarily focuses on traffic engineering, which is usually adapted from traditional networks. We propose a new framework to embrace the new opportunities brought by combining some special features of data centers with traffic engineering. Based on this framework, we characterize the problem of achieving energy efficiency with a time-aware model, and we prove its NP-hardness with a solution that has two steps. First, we solve the problem of assigning virtual machines (VM) to servers to reduce the amount of traffic and to generate favorable conditions for traffic engineering. The solution reached for this problem is based on three essential principles that we propose. Second, we reduce the number of active switches and balance traffic flows, depending on the relation between power consumption and routing, to achieve energy conservation. Experimental results confirm that, by using this framework, we can achieve up to 50 percent energy savings. We also provide a comprehensive discussion on the scalability and practicability of the framework.

Despite some proposals for energy-efficient topologies, most of the studies for saving energy in data center networks are focused on traffic engineering, i.e., consolidating flows and switching off unnecessary network devices. The major weakness of this approach is network oscillation brought by the frequent change of network topology when traffic fluctuates very fast. In this paper, we propose to incorporate rate adaptation into green data center networks. With rate adaptive network devices, we aim at approaching network-wide energy proportionality by routing optimization. We formalize the problem with an integer program and propose an efficient approximation algorithm - TSRR, solving the problem quickly while guaranteeing a constant performance ratio. Extensive range of simulations confirm that more than 40% of the energy can be saved while introducing very slight stretch on network delay.