# An SDN Focused Approach for Energy Aware Traffic Engineering in Data Centers
## 1 Introduction  
The problem of climate change due to global warming is already identified and the research community as well as industry are working on methods to limit its impact. The following areas have been identified as the main contributors of CO\({}_{2}\) emissions: (a) energy production (29%), (b) transportation (27%), (c) industry (21%), (d) agriculture (9%) and (e) information communication technologies (ICT) (9%) [1]. In the area of ICT, a large increase that can reach a total of 15% is expected by the end of 2020 due to the deployment of 5G networks, mass introduction of IoT (Internet of Things) devices, IP traffic generated by video streaming and augmented reality applications. Data centers (DCs) in internet service providers (ISPs) account for more than 45% of the power consumption [2]. Although a marginal 10% of it is caused by network equipment [3], data center networking is responsible for 0.36% of the total power consumption [4]. This figure is expected to rise even more because of NFV (Network Functions Virtualization) and software defined networking (SDN) based service deployment [5]. In 5G networks, the number of DCs is expected to rise in order to support mobile edge computing (MEC) and user plane intensive applications [6]. Also, the majority of legacy applications will be migrated to cloud infrastructure increasing the workloads served by DCs. Data center networks are characterized by traffic patterns and volumes that significantly vary from typical IP based networks that reside in ISP's premises. Typically, DC networks are overprovisioned with a large number or redundant devices and links. Depending on the deployed topology, access and aggregation layer links rarely exceed 10% of utilization due to the high number of redundant links on these layers [7]. Links and devices on higher layers of the topology (closer to root or data center routers) tend to have higher levels of utilization and thus consume more energy. Accordingly, only 60% of installed links could potentially serve traffic even during a busy hour [8]. If we consider the utilization of available resources during a whole day and not only during the busy hour, DC network infrastructure stays in idle state serving zero to minimal traffic (mostly for management purposes and maintaining the routing algorithms) for almost 70% of the day. Even on higher traffic volumes, the average link utilization is not surpassed for the remaining 25% of the day. Since DC networks are mainly constructed to provide a high level of resilience during a busy hour, we identify the opportunity to minimize the power consumption of network devices and links between them for the largest part of the day. In this paper we address the problem of minimizing the power consumption within DCs. The main obstacle in developing a practical optimization method for DCs is the requirement for global knowledge of network topology and the flows matrix between the hosts. DC networking presents high variability mostly from virtual machines (VMs) instantiation that have ephemeral life span in cloud environments. Thus, there is a need to continually monitor for new traffic flows and the optimization algorithm should act accordingly. Building a distributed algorithm for this purpose is not appropriate since it would require the introduction of new or updated protocols. Nevertheless, SDN architectures are already deployed in production environments and the centralized implementation of SDN controllers (SDN-C) now allows practical implementations of such optimization. Taking for granted that all modern DCs follow the SDN architecture, we show that, via energy aware traffic engineering, an SDN [9] application can successfully address the problem. Depending on hardware properties and traffic conditions, links can be put on a lower power state or completely shut off via traffic steering. We formulate the optimization process as a mixed integer programming (MIP) problem [10] that models power consumption taking into account all relevant constraints.