## 2 Literature Review  
Energy inefficiency in data centers has been recognized as a real challenge and a number of proposals have been presented in various energy conservation technique areas for computer networks and especially for data center networks. While we find a number of proposed schemes in the various energy optimization technique categories, to the best of our knowledge, there exists no unified scheme akin to our proposed method. A number of efforts have been reported for energy optimization using Virtual Machines (VMs). A mechanism for placement and communication between virtual machines is presented in [3]. This work considers the placement of VMs at the planning stages as well as traffic configuration within the DCN. The main focus of this work is load balancing and avoiding congestions for efficient energy usage. [PERSON] et al. [6] conducted a survey on various adaptive link rate methods for green communications and categorized the various schemes into groups. Their work is a useful guideline for researchers conducting research in this area, helping them in understanding the scope of the techniques proposed so far along with identifying shortcomings of each method. [PERSON] et al. [7] conducted a survey of existing approaches and trends in energy-aware fixed network infrastructures for future Internet and concluded that a considerable amount of energy can be saved by using energy efficient resource management methods and energy proportional network components. [PERSON] et al. [9] proposed a standard benchmark suit that can be applied on a switch to estimate its energy consumption at various traffic loads. This is useful in energy consumption estimation of various networks like Local Area Networks (LANs), Wide Area Networks (WANs) and data center networks. Some of the notable works based on resource consolidation are those by [PERSON] et al. [10] and [PERSON] et al. [11]. [PERSON] et al. proposed a power optimization technique for data center networks called CARPO. It is a correlation-aware power optimization algorithm that dynamically consolidates traffic flows on a subset of links and switches in a DCN. [PERSON] et al. studied real traffic from a production data center and verified that traffic can be consolidated on a subset of links and switches while turning off the extra switches without affecting performance. They observed that their computed subset can handle bandwidth demands most of the time [11]. This is closely related to our work and we use ElasticTree as a benchmark for performance analysis of our proposed mechanism. Proportional computing refers to the idea that the network must consume energy proportional to its traffic; there are two categories of proportional computing methods: Dynamic Voltage and Frequency Scaling (DVFS) and Adaptive Link Rate (ALR). ALR techniques are used to reduce energy consumption in devices by making them consume energy proportional to their load. Some of the adaptive link rate methods for Ethernet are suggested by [PERSON] et al. [15; 16]. [PERSON] et al. [17] suggested two schemes to reduce power consumption in network devices, the first method is to put network devices into sleep mode and the second method proposes rate adaption. Comprehending data center network traffic is of utmost importance for designing topology- and traffic-based solutions. Some of the important work in this direction is described in [19; 20]. [PERSON] et al. [19] observed DCN traffic patterns in detail through observation of data traces. They used Simple Network Management Protocol (SNMP) data to examine link utilization and packet losses at core, edge and aggregation layer. They observed that the average link utilization of aggregation layer links is only about 8% for 95% of the time while for edge and core links it is approximately 20% and 40%, respectively. They also conclude that the DCN traffic follows the on-off behavior. In recent years some notable work has been done in predictive scheduling on CPUs to conserve energy. [PERSON] et al.