## 1 Introduction  
Data centers comprise of large computational and storage systems interconnected through a communication network serving a large number of popular services in the Internet such as search engines (e.g., Google), Internet commerce (e.g., Amazon and e-Bay), web based e-mail (e.g., Yahoo mail), social networking (e.g., Myspace and Facebook) and video sharing (e.g., YouTube). In addition to computational and storage systems, the data centers also consist of power supply equipment, communication network, air conditioning, security systems and other related devices and can span over an area as large as a small town. Data centers are expanding rapidly in order to meet the traffic demands due to the large increase in Internet usage, resulting in ever increasing energy consumption. About 20% of energy consumption in a data center occurs in the Data Center Network (DCN) which lies at the core of a data center since it connects a large number of servers at various hierarchies through switches [1]. [PERSON] et al. stated that in a typical Google data center the network power is approximately 20% of the total power when the servers are utilized at 100%, but it increases to 50% when the utilization of servers decreases to 15%, which is quite typical in production data centers [2]. [PERSON] et al. stated that as data centers continue to grow in size, often housing tens of thousands or even hundreds of thousands of servers, the scale of data center networks has skyrocketed and in now in the same league as computing server's costs [3]. In large data centers, the DCN can connect hundreds or thousands of servers to support various applications and cloud computing. It is a general practice that the network devices of a data center are always kept in an \"on\" state, resulting in around 67.7 W energy consumption even in an idle state. The ever-increasing DCN cost and energy consumption have spurred the interest of the networking community to develop energy-efficient protocols and to devise methods to reduce energy consumption in DCNs striving for green data centers. Major techniques for network energy optimization or green networking can broadly be categorized into four groups: (a) resource consolidation; (b) virtualization; (c) selective connectedness; and (d) proportional computing [4; 5; 6]. Resource consolidation refers to finding a minimum power network subset which meets the current traffic demands. The resource consolidation methods are based on the identification of redundancy in networking resources due to over-provisioning and carefully designing algorithms for shutting down the redundant resources to consolidate network traffic on a selected subset of network components [7; 8; 9; 10; 11]. Virtualization techniques operate multiple services on a single hardware thereby utilizing hardware smartly to conserve energy [12]. Selective connectedness refers to the techniques used for identifying network components for potential future traffic and keeping them in a sleep state or in low power mode instead of shutting them down [13]. Proportional computing refers to energy consumption proportional to resource utilization which includes data rate adjustments of links and ports based on various criteria [14; 15; 16; 17]. A DCN is usually provided with extra resources to handle the worst-case workloads that seldomly occur, which means that under normal conditions the DCN capacity remains underutilized [18]. This results in energy wastage due to energy consumption by those nodes which spend most of the time in an idle state. Adapting DCN capacity to its workload can result in a significant amount of power savings. This paper presents Adaptive TrimTree; a mechanism for a green DCN which proposes a combination of resource consolidation, selective connectedness and proportional computing methods to achieve optimal energy consumption with minimal performance compromises. The mechanism first identifies two network subsets called 'active network subset' and \"passive network subset\".