Major improvements have propelled the development of worldwide Internet systems during the past decade. To meet the growing demand in massive data processing, a large number of geographically-distributed data centers begin to surge in the era of data deluge and information explosion. Along with their remarkable expansion, contemporary cloud networks are being challenged by the growing concerns about global warming, due to their substantial energy consumption. Hence, the infrastructure of future data centers must be energy-efficient and sustainable. Fortunately, supporting technologies of smart grids, big data analytics and machine learning, are also developing rapidly. These considerations motivate well the present thesis, which mainly focuses on developing interdisciplinary approaches to offer sustainable resource allocation for future cloud networks, by leveraging three intertwining research subjects. The modern smart grid has many new features and advanced capabilities including e.g., high penetration of renewable energy sources, and dynamic pricing based demand-side management. Clearly, by integrating these features into the cloud network infrastructure, it becomes feasible to realize its desiderata of reliability, energy-efficiency and sustainability. Yet, full benefits of the renewable energy (e.g., wind and solar) can only be harnessed by properly mitigating its intrinsically stochastic nature, which is still a challenging task. This prompts leveraging the huge volume of historical data to reduce the stochasticity of online decision making. Specifically, valuable insights from big data analytics can enable a markedly improved resource allocation policy by learning historical user and environmental patterns. Relevant machine learning approaches can further uncover “hidden insights” from historical relationships and trends in massive datasets. Targeting this goal, the present thesis systematically studies resource allocation tasks for future sustainable cloud networks under uncertainty. With an eye towards realistic scenarios, the thesis progressively adapts elegant mathematical models, optimization frameworks, and develops low complexity algorithms from three different aspects: stochastic (Chapters 2 and 3), robust (Chapter 4), and big data-driven approaches (Chapter 5). The resultant algorithms are all numerically efficient with optimality guarantees, and most of them are also amenable to a distributed implementation.
University of Minnesota M.S. thesis. September 2016. Major: Electrical/Computer Engineering. Advisor: Georgios Giannakis. 1 computer file (PDF); viii, 139 pages.
Resource Allocation for Green Cloud Networks under Uncertainty: Stochastic, Robust and Big Data-driven Approaches.
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