A datacenter is a pool of resources such as computational, storage, and servers interconnected using a communications network. Data Center Networking (DCN) holds a pivotal role, and it needs to be scalable and efficient to connect the growing number of servers so as to handle the intensive demands of cloud computing. Recently there has been a rapidly growing field of literature on DCNs, but it mainly focus on studying how to model and evaluate the resource provisioning and allocation algorithms for more effective and efficient resource management of a cloud system. Unfortunately, there are not many studies that reveal how the underlying networks topological connectivity can affect the DCNs performance, in areas such as energy consumption and service resilience. There is a saying that ‘it is not what you know but who you know’ i.e., algorithm, connectivity, which argues that people get ahead in life based on their connections, not on their skills or knowledge, and every day offers evidence of this proverb. This case also applies to DCNs. DCNs performance is not merely a function of resource provisioning and allocation, but also it is a network-wide activity. The structure and ties that link a data center to other data centers are also critical factors. In this thesis, the researcher has proposed a method for evaluating topological metrics (network robustness metrics and node centrality metrics) to identify critical nodes and edges in a network so that it measures the overall DCN network performance change (throughput, latency, packet drop ratio) according to the faults on the network. We have identified the energy changes according to the change of internal DCNs; the simulation study showed that the traffic load has a large impact on energy consumption. Apart from that, the state of the art for modern DCNs is elaborated that depicts the true picture of the current progress in this field where good research can actively contribute.