Link weight assignment and loop-free routing table update for link state routing protocols in energy-aware internet

In order to lessen the greenhouse effects and diminish environmental pollution, reducing energy usage is important in designing next generation networks. Shutting down the network devices that carry light load and redirecting their traffic flows to other routes is the most common way to reduce network energy consumption. Since traffic demands among node pairs vary in different time periods, an energy efficient network has to dynamically determine the optimal active links to adapt itself to network traffic changes. However, in current IP networks, shutting down and/or turning on links would trigger link state routing protocols to reconverge to a new topology. Since the convergence time would take tens of seconds, routing table inconsistencies among routers would result in network disconnection and even worse, generating traffic loops during the convergence interval. Removing routing images inconsistent among routers to prevent loops is a critical issue in energy efficient network and this issue is still not considered in the green network design yet. The contribution of the paper is presented in two parts. First, we propose a comprehensive approach to determine a network topology and a link metric for each time period. Traffic engineering is considered in our design such that flows going on the energy-aware network are within a predetermined percentage of the link capacity such that no congestion occurs in a statistical manner. Second, to avoid transient loops during time period changes, we propose a Distributed Loop-free Routing Update (DLRU) scheme to determine the correct sequence for updating the routing table. A scrupulous proof was also presented to ensure the loop-free property of the DLRU. In this paper, we formulate an integer linear programming to determine this multi-topology and link weight assignment problem. Due to its NP-hard property, we propose an efficient algorithm, termed Lagrangian Relaxation and Harmonic Series (LR&HS) heuristic. Numerical results demonstrate that the proposed LRHS approach outperforms the other approaches on several benchmark networks and random networks by providing up to 35%-50% additional energy saving in our experimental cases.