In this thesis, we propose an integrated approach for estimating the performance of virtual network resource allocation and a Genetic Algorithm (GA) based mechanism for online dynamic resource allocation in virtualized environment.The integrated approach is empowered by a novel loss network model with Dynamic Routing And Random Topology (DRART), which is combined with some existing models to create a synergy across different levels through an effective recursive process. Numerical results show the proposed integrated approach can provide accurate predictions on the performances of general virtual network embedding algorithms.We propose a virtual link mapping solution, i.e., Segment Based Genetic Algorithm (SBGA), which provides new definitions for genes and chromosomes in the Genetic Algorithm. Our SBGA approach enables parallel processing for searching optimal allocations. Our theoretical analysis shows that the execution time of our approach can be reduced to logarithmic time. To map virtual nodes and links to physical ones in one stage, we further develop an approach, named as GAOne. Our proposed GAOne approach applies the two-color graph coloring in graph theory to guide the crossover process in the Genetic Algorithm (GA) for valid solutions. Our simulation results show that the proposed GAOne approach is fast and efficient for online resource allocation applications in virtualized environment. i At this point, my Ph.D. journey is coming to the end. In the past six years, I've thought of giving up many times. Fortunately, I decided to persevere. First and foremost, I would like to express my deep and sincere gratitude to my supervisor Dr. Changcheng Huang. I really appreciate that I had the opportunity to work and study under his guidance. He taught me the research methodology and gave me invaluable suggestions. His guidance and encouragement helped me throughout my Ph.D. study. I am extremely grateful for his patience, enthusiasm and rigor.My sincere thanks go to my thesis committee: Dr.