This thesis considers two kinds of problems, motivated by practical applications in data center operations and maintenance. Data centers are the brain of the internet, each hosting as many as tens of thousands of IT devices, making them a considerable global energy consumption contributor (more than 1 percent of global power consumption). There is a large body of work at different layers aimed at reducing the total power consumption for data centers. One of the key places to save power is addressing the thermal heterogeneity in data centers by thermal-aware workload distribution. The corresponding optimization problem is challenging due to its combinatorial nature and the computational complexity of thermal models. In this thesis, a holistic theoretical approach is proposed for thermal-aware workload distribution which uses linearization to make the problem model-independent and easier to study. Two general optimization problems are defined. In the first problem, several cooling parameters and heat recirculation effects are considered, where two red-line temperatures are defined for idle and fully utilized servers to allow the cooling effort to be reduced. The resulting problem is a mixed integer linear programming problem which is solved approximately using a proposed heuristic. Numerical results confirm that the proposed approach outperforms commonly considered baseline algorithms and commercial solvers (MATLAB) and can reduce the power consumption by more than 10 percent. In the next problem, additional operational costs related to reliability of the servers are considered. The resulting problem is solved by a generalization of the proposed heuristics integrated with a Model Predictive Control (MPC) approach, where demand predictions are available. Finally, in the second type of problems, we address a problem in inventory management related to data center maintenance, where we develop an efficient dynamic programming algorithm to solve a lot-sizing problem. The algorithm is based on a key structural property that may be of more general interest, that of a just-in-time ordering policy. / Thesis / Doctor of Philosophy (PhD) / Data centers, each hosting as many as tens of thousands of IT devices, contribute to a considerable portion of energy usage worldwide (more than 1 percent of global power consumption). They also encounter other operational costs mostly related to reliability of devices and maintenance. One of the key places to reduce energy consumption is through addressing the thermal heterogeneity in data centers by thermal-aware work load distribution for the servers. This prevents hot spot generation and addresses the trade-off between IT and cooling power consumption, the two main power consump tion contributors. The corresponding optimization problem is challenging due to its combinatorial nature and the complexity of thermal models. In this thesis, we present a holistic approach for thermal-aware workload distribution in data centers, using lin earization to make the problem model-independent and simpler to study. Two quite general nonlinear optimization problems are defined. The results confirm that the proposed approach completed by a proposed heuristic solves the problems efficiently and with high precision. Finally, we address a problem in inventory management related to data center maintenance, where we develop an efficient algorithm to solve a lot-sizing problem that has a goal of reducing data center operational costs.