This dissertation studies the competitive dynamics between two non-identical providers competing for customers seeking low-cost and quick service. Providers have generic delay functions where, asdemand received by each provider grows, so does delay in processing customers' requests. Given a pricing or capacity decision by each provider, customers determine the proportion of demand to send to each provider by minimizing generalized cost (monetary cost plus delaycost). This problem is formulated as a bilevel optimization, with providers competing at the upper level subject to the customers' decisions at the lower level. Occurrence of Nash equilibria between the providers is studied.First studied is the providers' problem of making decisions on capacities, while competing for a single customer. Conditions are derived for one provider to claim the entire market share, and for the occurrence of an equilibrium where both providers receive positivedemand. A numerical example in which no equilibrium exists is presented. Both the inelastic and elastic demand cases are studied for this scenario. In a second model, providers make pricing decisions with capacity fixed. Under some assumptions, it is shownthat a Nash equilibrium between providers always exists and a numerical example is presented. These models are then combined, in which providers make capacity decisions where prices equilibrate based on the results from the second model.Two competing customers with demand for a homogeneous product are then introduced, where providers choose prices as they compete for customers. This model is extended to an application along a highway corridor with a high-occupancy/toll (HOT) lane in parallel with a free road and transit line. A government agency chooses the transit service frequency while a private toll operator competes by choosing a toll to charge single-occupancy vehicles who wish to use the HOT lane.This scenario is also modeled as a bilevel program. For the lower level, a new dynamic equilibration process where homogeneous users make mode choice decisions based on previous generalized costs ofusing a particular mode is developed. Two numerical examples are presented showing a unique Nash equilibrium between the providers and an example in which multiple equilibria exist.

This dissertation examines a profit-maximizing project selection and scheduling problem. Assume that a set of potentially profitable projects are available, yet limited available resources may not allow all of them to be pursued. Profit profiles for projects are assumed to be non-increasing functions of project completion times, i.e. profit returns are sensitive to time-to-market. Decision needs to be made on which sub-set of projects should be chosen and how resources should be allocated to these projects such that the total profit is maximized.Formal mathematical models are formulated for various versions of the problem, including such ones incorporating a third team formation aspect. Structure of the problem is examined and insights are gained regarding prioritization of project, specifically. Although prioritization is sub-optimal in general, heuristic solution methods based on prioritization are pursued, since the scheduling sub-problem itself is NP-hard.A decomposition heuristic framework is first proposed to obtain good solutions using minimum computational time. Sets of test instances are generated using project network data from well-known source in the literature. Computational runs reveal that three priority rules achieve significantly better profits than the benchmarking random priority rule.Improving upon the prioritization based decomposition heuristic, an implicit enumeration is proposed. This algorithm does not examine all priority sequences, yet guarantees an optimal priority sequence when the computation is completed. Several fathoming rules are proposed to cut back computational time effectively. Comparison to the profits achieved by the best priority rule and the benchmarking random priority rule shows a significant improvement on profits, yet at a cost of reasonable added computation time.Future research areas include identifying general conditions under which prioritization of projects would lead us to an optimal solution. Developing better upper bounds for the implicit enumeration scheme is also of interest. The team formation aspect has yet to be treated computationally. It would also be of interest to consider how synergy deviation information may be fed back to the earlier stages of project selection and scheduling decision. Trade-off between profit and team synergy may also be considered in the future.

In the freshwater copepod, Macrocyclops albidus, food availability, rearing conditions and tapeworm infection clearly affected various life-history traits and their trade-offs. I found that low food availability clearly constrained resource allocations to several life-history (often phenotypically plastic) traits, whereas high food availability either allowed for adjustments in resource allocation patterns or allowed resources to be allocated to several traits without apparent trade-offs. Both male and female copepods allocated resources according to food availability; developing more slowly and achieving smaller adult body size when food was scarce. When food availability was low females were constrained and produced fewer eggs (in total and per clutch), and started reproduction later than females with more food available. Males under low food availability allocated relatively more to spermatophore size (current reproduction) with decreasing body size. In contrast, when food availability was high males allocated resources to body size as well as spermatophore size. Overall, at maturity, copepods of both sexes were more similar in size than in age, suggesting that large body size was more important for fitness than fast development. In nature the prevalence of copepods infected with cestode tapeworms was found to be low (0-3%). Female copepods, experimentally infected with the cestode Schistocephalus solidus, showed lower overall fecundity, especially when food availability was low. However, infected females produced a larger proportion of their life-time egg production early in life than non-infected females. This might be an adaptation to reduce future fitness costs of infection. Females grown under bad rearing conditions, but with high food availability, produced their first clutch earlier than females grown under good rearing conditions, indicating an adjustment in timing of reproduction. These findings contribute to our fundamental evolutionary understanding of how environmental conditions interact with life-history traits.

Video traffic constitutes a major part of the Internet traffic nowadays. Yet most video delivery services remain best-effort, relying on server bandwidth over-provisioning to guarantee Quality of Service (QoS). Cloud computing is changing the way that video services are offered, enabling elastic and efficient resource allocation through auto-scaling. In this thesis, we propose a new framework of cloud workload management for multimedia delivery services, incorporating demand forecast, predictive resource allocation and quality assurance, as well as resource pricing as inter-dependent components. Based on the trace analysis of a production Video-on-Demand (VoD) system, we propose time-series techniques to predict video bandwidth demand from online monitoring, and determine bandwidth reservations from multiple data centers and the related load direction policy. We further study how such quality-guaranteed cloud services should be priced, in both a game theoretical model and an optimization model.Particularly, when multiple video providers coexist to use cloud resources, we use pricing to control resource allocation in order to maximize the aggregate network utility, which is a standard network utility maximization (NUM) problem with coupled objectives. We propose a novel class of iterative distributed solutions to such problems with a simple economic interpretation of pricing. The method proves to be more efficient than the conventional approach of dual decomposition and gradient methods for large-scale systems, both in theory and in trace-driven simulations.

Background: Funding practices in Ontario's acute care sector have undergone a substantive shift away from ‘lump-sum funding’ towards a combination of population-needs and performance-based financing (MOHLTC, 2013). In contrast very little is known about how funds are distributed across the province’s public health sector, specifically the 36 public health units (PHUs) that are mandated to deliver health promotion and disease prevention programs across Ontario. In fact the funding arrangement utilized by the public health sector has remained unchanged for several years, despite the growing burden of responsibilities on PHUs in terms of evolving population health needs and more expansive programmatic and performance expectations. Current literature on the processes, variables and overarching principles that govern the distribution of funds across PHUs remains considerably limited. Objectives: The objectives of this study were to develop a better understanding of how PHUs in Ontario are currently funded, and to examine what principles public health professionals believe should guide the distribution of resources across PHUs. The project sought to identify the fundamental principles that public health professionals believe should inform future thinking around public health funding. Methods: The perspectives of public health professionals who have proximal links to the current public health funding process served as the basis of the data discovery component for this study. A total of 14 in-depth interviews were conducted with a number of public health professionals to gather their insights on the current funding arrangement, and explore what principles they believe should be used to guide allocation decisions in the public health sector. Interviews were followed by a web survey to examine how public health professionals rank principles and perceive trade-offs between competing principles. Results: Public health professionals proposed a total of 12 principles to guide the distribution of resources across PHUs. These principles were grounded in three core social value judgments (need, equity, and transparency and accountability). The study provides important insights into the fundamental principles that public health professionals believe should guide allocation decisions and illustrates the complexity associated with distributing limited resources across health units, as well as possible directions for future research on this topic.

Video traffic constitutes a major part of the Internet traffic nowadays. Yet most video delivery services remain best-effort, relying on server bandwidth over-provisioning to guarantee Quality of Service (QoS). Cloud computing is changing the way that video services are offered, enabling elastic and efficient resource allocation through auto-scaling. In this thesis, we propose a new framework of cloud workload management for multimedia delivery services, incorporating demand forecast, predictive resource allocation and quality assurance, as well as resource pricing as inter-dependent components. Based on the trace analysis of a production Video-on-Demand (VoD) system, we propose time-series techniques to predict video bandwidth demand from online monitoring, and determine bandwidth reservations from multiple data centers and the related load direction policy. We further study how such quality-guaranteed cloud services should be priced, in both a game theoretical model and an optimization model.Particularly, when multiple video providers coexist to use cloud resources, we use pricing to control resource allocation in order to maximize the aggregate network utility, which is a standard network utility maximization (NUM) problem with coupled objectives. We propose a novel class of iterative distributed solutions to such problems with a simple economic interpretation of pricing. The method proves to be more efficient than the conventional approach of dual decomposition and gradient methods for large-scale systems, both in theory and in trace-driven simulations.

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We first present a non-cooperative auction game to solve the bandwidth allocation problem for non-cooperative channel access in a wireless network. The Nash equilibrium is obtained as a solution of the game. To address this problem of bandwidth sharing under unknown information, we further develop a Bayesian auction game model and then Bayesian Nash equilibrium is then obtained. Next, we present a framework based on coalitional game for cooperative channel access for carry-and-forward-based data delivery. Each mobile node helps others to carry and then forward their data. A coalitional game is proposed to find a stable coalition structure for this cooperative data delivery. We next present static and dynamic coalitional games for carry-and-forward-based data delivery when the behavior of each mobile node is unknown by others. In the dynamic game, each mobile node can update its beliefs about other mobile nodes’ types when the static coalitional game is played repeatedly.

In this thesis, resource allocation and multiple access in cognitive radio (CR) and compressed sensing (CS)-based wireless networks are studied. Energy-efficiency oriented design becomes more and more important in wireless systems, which motivates us to propose a location-aware power strategy for single user and multiple users in CR systems and a CS-based processing in wireless sensor networks (WSNs) which reduces the number of data transmissions and energy consumption by utilizing sparsity of the transmitted data due to spatial correlation and temporal correlation. In particular, the work on location-aware power allocation in CR system gives a brief overview of the existing power allocation design in the literature and unifies them into a general power allocation framework. The impact of the network topology on the system performance is highlighted, which motivates us to propose a novel location-aware strategy that intelligently utilizes frequency and space opportunities and minimizes the overall power consumption while maintaining the quality of service (QoS) of the primary system. This work shows that in addition to exploring the spectrum holes in time and frequency domains, spatial opportunities can be utilized to further enhance energy efficiency for CR systems. Then the work of resource allocation is extended to finding the power strategy and channel allocation optimization for multiple secondary users in an orthogonal frequency division multiplexing (OFDM) based cognitive radio network. Three different spectrum access methods are considered and utilized adaptively according to the different locations of the secondary users, and we unify these spectrum access methods into a general resource allocation framework. An interference violation test is proposed to decide the parameters in this framework that indicate the set of licensed channels to be sensed. The proposed scheme intelligently utilizes frequency and space opportunities, avoids unnecessary spectrum sensing and minimizes the overall power consumption while maintaining the quality of service of the primary system. The uncertainty of channel state information between the secondary users (SUs) and the primary users (PUs) is also taken into account in the study of power and channel allocation optimization of the SUs. Simulation results validate the effectiveness of the proposed method in terms of energy efficiency and show that enhanced performance can be obtained by utilizing spatial opportunities. The work on CS-based WSNs considers the application of compressed sensing to WSNs for data measurement communication and reconstruction, where N sensor nodes compete for medium access to a single receiver. Sparsity of the sensor data in three domains due to time correlation, space correlation and multiple access are being utilized. A CS-based medium access control (MAC) scheme is proposed and an in depth analysis on this scheme from a physical layer perspective is provided to reveal the impact of communication signal-to-noise ratio on the reconstruction performance. We show the process of the sensor data converted to the modulated symbols for physical layer transmission and how the modulated symbols recovered via compressed sensing. This work further identifies the decision problem of distinguishing between active and inactive transmitters after symbol recovery and provides a comprehensive performance comparison between carrier sense multiple access and the proposed CSbased scheme. Moreover, a network data recovery scheme that exploits both spatial and temporal correlations is proposed. Simulation results validate the effectiveness of the proposed method in terms of communication throughput and show that enhanced performance can be obtained by utilizing the sensed signal’s temporal and spatial correlations. / Graduate