Title: New benchmarking techniques in resource allocation problems: theory and applications in cloud systems     Date: Monday, May 2, 2022 Time: 1:00 PM EST Location: Room 404, Groseclose, ISyE. Bluejeans link: https://bluejeans.com/8449626028/     Sebastian Perez-Salazar Ph.D. Candidate Algorithms, Combinatorics, and Optimization School of Industrial and Systems Engineering Georgia Institute of Technology     Committee:   Dr. Mohit Singh, School of Industrial and Systems Engineering, Georgia Institute of Technology Dr. Alejandro Toriello, School of Industrial and Systems Engineering, Georgia Institute of Technology Dr. Santanu Dey, School of Industrial and Systems Engineering, Georgia Institute of Technology Dr. Siva Theja Maguluri, School of Industrial and Systems Engineering, Georgia Institute of Technology Dr. Prasad Tetali, Department of Mathematical Sciences, Carnegie Mellon University   Advisors: Mohit Singh & Alejandro Toriello, School of Industrial and Systems Engineering, Georgia Institute of Technology   Reader: Dr. Siva Theja Maguluri, School of Industrial and Systems Engineering, Georgia Institute of Technology   Thesis draft is available at: https://drive.google.com/file/d/1qAofk2mCFTwr0L7PNArXbAS61GDBoQJg/view?usp=sharing    thesis.pdf drive.google.com     Abstract: Motivated by different e-commerce applications such as allocating virtual machines to servers and online ad placement, we study new models that aim to capture unstudied tensions faced by decision-makers. In online/sequential models, future information is often unavailable to decision-makers---e.g., the exact demand of a product for next week. Sometimes, these unknowns have regularity, and decision-makers can fit random models. Other times, decision-makers must be prepared for any possible outcome. In practice, several solutions are based on classical models that do not fully consider these unknowns. One reason for this is our present technical limitations. Exploring new models with adequate sources of uncertainty could be beneficial for both the theory and the practice of decision-making. For example, cloud companies such as Amazon WS face highly unpredictable demands of resources. New management planning that considers these tensions have improved capacity and cut costs for the cloud providers. As a result, cloud companies can now offer new services at lower prices benefiting thousands of users. In this thesis, we study three different models, each motivated by an application in cloud computing and online advertising.   From a technical standpoint, we apply either worst-case analysis with limited information from the system or adaptive analysis with stochastic results learned after making an irrevocable decision. A central aspect of this work is dynamic benchmarks as opposed to static or offline ones. Static and offline viewpoints are too conservative and have limited interpretation in some dynamic settings. A dynamic criterion, such as the value of an optimal sequential policy, allows comparisons with the best that one could do in dynamic scenarios. Another aspect of this work is multi-objective criteria in dynamic settings, where two or more competing goals must be satisfied under an uncertain future. We tackle the challenges introduced by these new perspectives with fresh theoretical analyses, drawing inspiration from linear and nonlinear optimization and stochastic processes.