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Traditionally, network optimization is used to provide good configurations in real network system problems based on mathematical models and statistical assumptions. Recently, this paradigm is evolving, fueled by an explosion of availability of data. The modern trend in networking problems is to tap into the power of data to extract models and deal with uncertainty. This thesis proposes algorithmic frameworks for wireless networks, based both on classical or data-driven optimization and machine learning. We target two use cases, user association and cloud resource reservation.The baseline approach for user association, connecting wireless devices to the base station that provides the strongest signal, leads to very inefficient configurations even in current wireless networks. We focus on tailoring user association based on resource efficiency and service requirement satisfaction, depending on the underlying network demand. We first study distributed user association with priority QoS guarantees, then scalable centralized load balancing based on computational optimal transport and finally robust user association based on approximate traffic prediction.Moving to the topic of cloud resource reservation, we develop a novel framework for resource reservation in worst-case scenaria, where the demand is engineered by an adversary aiming to harm our performance. We provide policies that have ``no regret'' and guarantee asymptotic feasibility in budget constraints under such workloads. More importantly we expand to a general framework for online convex optimization (OCO) problems with long term budget constraints complementing the results …