Big Data Optimization for Modern Communication Networks
MetadataShow full item record
The unprecedented big data in modern communication networks presents us opportunities and challenges. An efficient analytic method for the sheer volume of data is of significant importance for smart grid evolution, intelligent communication network management, efficient medical data management, personalized business model design and smart city development. Meanwhile, the huge volume of data makes it impractical to collect, store and processing in a centralized fashion. Moreover, the massive datasets are noisy, incomplete, heterogeneous, structured, prone to outliers, and vulnerable to cyber-attacks. Overall, we are facing a problem in which the classic resources of computation such as time, space, and energy, are intertwined in complex ways with the massive data sources, and new computational mathematical models as well as methodologies must be explored. With the rapid development of the modern communication networks comes the need of novel algorithms for large-scale data processing and optimization. In this thesis, we investigate the application of big data optimization methods for smart grid security and mobile data traffic management. Firstly, we review the parallel and distributed optimization algorithms based on an alternating direction method of multipliers for solving big data optimization problems. The mathematical backgrounds of the algorithms are given, and the implementations on large-scale computing facilities are also illustrated. Next, the applications of big data processing techniques for smart grid security are studied from two perspectives: how to exploit the inherent structure of the data, and how to deal with the huge size of the data sets. Explored problems are the sparse optimization approach for false data injection detection, and the distributed parallel approach for the security-constrained optimal power flow problem, respectively. Finally, we consider big data optimization methods for data traffic management in mobile cloud computing by two specific application cases: the mobile data offloading in a software defined network at the network edge, and the management of mobile cloud service request allocation and response routing. It is shown by numerical results that effective management and processing of ‘big data’ have the potential to significantly improve smart grid security as well as resource utilization and service quality of the mobile cloud computing.