MECHANISM DESIGN WITH GENERAL UTILITIES

This thesis studies mechanism design from an optimization perspective. Our main contribution is to characterize fundamental structural properties of optimization problems arising in mechanism design and to exploit them to design general frameworks and techniques for efficiently solving the underlying problems. Not only do our characterizations allow for efficient computation, they also reveal qualitative characteristics of optimal mechanisms which are important even from a non-computational standpoint. Furthermore, most of our techniques are widely applicable to optimization problems outside of mechanism design such as online algorithms or stochastic optimization. Our frameworks can be summarized as follows. When the input to an optimization problem (e.g., a mechanism design problem) comes from independent sources (e.g., independent agents), the complexity of the problem can be exponentially reduced by (i) decomposing the problem into smaller subproblems, each one involving one input source, (ii) simultaneously optimizing the subproblems subject to certain relaxation of coupling constraints, and (iii) combining the solutions of the subproblems in a certain way to obtain an (approximately) optimal solution for the original problem. We use our proposed framework to construct optimal or approximately optimal mechanisms for several settings previously considered in the literature and to improve upon the best previously known results. We also present applications of our techniques to non-mechanism design problems such as online stochastic generalized assignment problem which itself captures online and stochastic versions of various other problems such as resource allocation and job scheduling

Scheduling of data-intensive workloads in a brokered virtualized environment

Providing performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, for which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource management solutions that consider the brokered nature of these workloads, as well as the special demands of their intra-dependent components. In this paper, we present an offline mechanism for scheduling batches of brokered data-intensive workloads, which can be extended to an online setting. The objective of the mechanism is to decide on a packing of the workloads in a batch that minimizes the broker's incurred costs, Moreover, considering the brokered nature of such workloads, we define a payment model that provides incentives to these workloads to be scheduled as part of a batch, which we analyze theoretically. Finally, we evaluate the proposed scheduling algorithm, and exemplify the fairness of the payment model in practical settings via trace-based experiments