Currently, with the booming development of Next Generation Networks (NGNs), there is an urgent request for reducing energy in telecommunication networks due to its environmental impact and potential economic benefits. However, the most existing green networking approaches take no or less consideration on network survivability aspect. This thesis aims to tackle the trade-off problem between energy efficiency and network survivability.

In this thesis, we optimize this trade-off problem by using energy aware survivable routing approaches. This sort of trade-off problem falls in the class of capacitated multi-commodity minimum cost flow (CMCF) problems i.e., the problem in which multiple commodities have to be routed over a graph with some constraints. Generally speaking, this problem is also categorized as combinatorial optimization, which can be precisely modelled using Integer Linear Programming (ILP) formulation. The ILP is a mathematical method for determining the best feasible solution to achieve an optimal objective such as maximum profit or lowest cost by given the mathematical models for a list of requirements and constraints represented as linear relationships. Using ILP formulas, we propose three energy aware survivable routing models, which are Energy Aware Backup Protection 1+1 (EABP 1+1), Energy Aware Backup Protection 1:1 (EABP 1:1), Energy Aware Shared Backup Protection (EASBP). From energy saving aspect, we integrate several energy efficient approaches into them, such as energy aware routing, sleeping mode, and energy consumption rating strategies. For network survivability concern, EABP 1+1, EABP 1:1, and EASBP are embedded with 1+1 backup protection, 1:1 backup protection, and shared backup protection respectively.

Moreover, for performance comparison, the three models have been implemented in IBM ILOG CPLEX Optimization Studio and solved by CPLEX 11.1 Solver. Moreover, since the CPLEX Optimization Studio can only produce theoretical results, we have developed and integrated the three energy aware survivable routing models into TOTEM (TOolbox for Traffic Engineering Methods) network simulator for better visualization.

We have conducted extensive case studies to validate these three models. The most energy efficient model – EABP 1:1 has been found, it could save up to 90% of energy consumption compared with the worst-case multi-commodity flow (MCF) algorithm, due to the combinational use of energy aware routing, sleeping mode strategies and energy consumption rating. In addition, the sleeping mode is an effective approach to reduce energy cost, and EABP 1:1 can save up to half energy usage than EABP 1+1 by introducing sleeping mode. However these two models consume a significant amount of capacity for network survivability purpose. Therefore EASBP has been proposed and the numerical results have confirmed that it is the best solution to tackle the trade-off problem between energy reduction and network survivability. This model consumes significantly less capacity with a small sacrifice on energy expenditure, especially under the condition of large traffic demands flowing in network.