Optimizing IGP link weights for energy-efficiency in multi-period traffic matrices

• Published in: Computer communications Vol. 61; pp. 79 - 89
• Main Authors: Moulierac, Joanna, Phan, Truong Khoa
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2015; Elsevier
• Subjects: Computer networks; Computer Science; Computer simulation; Energy conservation; Energy-aware routing; Green networking; Links; Networking and Internet Architecture; Networks; Quality of service architectures; Robust network optimization; Traffic engineering; Traffic flow; Energy-aware routing; Robust network optimization; Green networking; Traffic engineering
• ISSN: 0140-3664; 1873-703X
• DOI: 10.1016/j.comcom.2015.01.004

Recently, due to the increasing power consumption and worldwide gases emissions in ICT (Information and Communication Technology), energy efficient ways to design and operate backbone networks are becoming a new concern for network operators. Since these networks are usually overprovisioned and since traffic load has a small influence on power consumption of network equipments, the most common approach to save energy is to put unused line cards that drive links between neighboring routers into sleep mode. To guarantee QoS, all traffic demands should be routed without violating capacity constraints and the network should keep its connectivity. From the perspective of traffic engineering, we argue that stability in routing configuration also plays an important role in QoS. In details, frequent changes in network configuration (link weights, slept and activated links) to adapt with traffic fluctuation in daily time cause network oscillations. In this work, we propose a novel optimization method to adjust the link weights of Open Shortest Path First (OSPF) protocol while limiting the changes in network configurations when multi-period traffic matrices are considered. We formally define the problem and model it as Mixed Integer Linear Program (MILP). We then propose an efficient heuristic algorithm that is suitable for large networks. Simulation results with real traffic traces on three different networks show that our approach achieves high energy saving while keeping the networks in stable state (less changes in network configuration).