Orthogonal frequency division multiple access (OFDMA) is becoming a widely deployed mechanism in broadband wireless networks due to its capability to combat the channel impairments and support high data rate. Besides, dealing with small units of spectrum, named sub-carriers, instead of whole spectrum, results in enhanced flexibility and efficiency of the resource allocation for OFDMA networks. Resource allocation and scheduling in the downlink of OFDMA networks supporting heterogeneous traffic will be considered in this thesis. The purpose of resource allocation is to allocate sub-carriers and power to users to meet their service requirements while maintaining fairness among users and maximizes resource utilization. To achieve these objectives, utility-based resource allocation schemes along with some state-of-the-art resource allocation paradigms such as power control, adaptive modulation and coding, sub-carrier assignment, and scheduling are adopted. On one hand, a utility-based resource allocation scheme improves resource utilization by allocating enough resources based on users' quality of service (QoS) satisfaction. On the other hand, resource allocation based on utilities is not trivial when users demand different traffic types with convex and nonconvex utilities. The first contribution of the thesis is the proposing of a framework, based on joint physical (PHY) and medium access (MAC) layer optimization, for utility-based resource allocation in OFDMA networks with heterogeneous traffic types. The framework considers the network resources limitations while attempting to improve resources utilization and heterogeneous users' satisfaction of service. The resource allocation problem is formulated by continuous optimization techniques, and an algorithm based on interior point and penalty methods is suggested to solve the problem. The numerical results show that the framework is very efficient in treating the nonconvexity problem and the allocation is accurate comparing with the ones obtained by a genetic search algorithm. The second contribution of the thesis is the proposing of an opportunistic fair scheduling scheme for OFDMA networks. The contribution is twofold. First, a vector of fair weights is proposed, which can be used in any scheduling scheme for OFDMA networks to maintain fairness. Second, the fair weights are deployed in an opportunistic scheduling scheme to compensate the unfairness of the scheduling. The proposed scheme efficiently schedules users by exploiting multiuser diversity gain, OFDMA resource allocation flexibility, and utility fair service discipline. It is expected that the research in the thesis contributes to developing practical schemes with low complexity for the MAC layer of OFDMA networks.

In a data centre, server clusters are typically used to provide the required processing capacity to provide acceptable response time performance to interactive applications. The workload of each application may be time-varying. Static allocation to meet peak demand is not an efficient usage of resources. Dynamic resource allocation, on the other hand, can result in efficient resource utilization while meeting the performance goals of individual applications. In this thesis, we develop a new interactive system model where the number of logon users changes over time. Our objective is to obtain results that can be used to guide dynamic resource allocation decisions. We obtain approximate analytic results for the response time distribution at steady state for our model. Using numerical examples, we show that these results are acceptable in terms of estimating the steady state probabilities of the number of logon users. We also show by comparison with simulation that our results are acceptable in estimating the response time distribution under a variety of dynamic resource allocation scenarios. More importantly, we show that our results are accurate in terms of predicting the minimum number of processor nodes required to meet the performance goal of an interaction application. Such information is valuable to resource provisioning and we discuss how our results can be used to guide dynamic resource allocation decisions.

In this thesis we investigate different algorithms for dynamic resource allocation in wireless networks. We introduce a general framework for modeling systems whichis applicable to many scenarios. We also analyze a specific scenario with adaptivebeamforming and show how it fits into the proposed framework. We then studytwo different resource allocation problems: Quality-of-Service (QoS) constraineduser scheduling and sum-rate maximization. For user scheduling, we select some“good” set of users that is allowed to use a specific resource. We investigatedifferent algorithms with varying complexities. For the sum-rate maximizationwe find the global optimum through an algorithm that takes advantage of thestructure of the problem by reformulating it as a D.C. program, i.e., a minimizationover a difference of convex functions. We validate this approach by showing that itis more efficient than an exhaustive search at exploring the space of solutions. Thealgorithm provides a good benchmark for more suboptimal algorithms to comparewith. The framework in which we construct the algorithm, apart from being verygeneral, is also very flexible and can be used to implement other low complexitybut suboptimal algorithms.

Estimating tail risk measures such as Value at Risk (VaR) and Conditional Tail Expectation (CTE) is a vital component in financial and actuarial risk management. The CTE is a preferred risk measure, due to coherence and a widespread acceptance in actuarial community. In particular we focus on the estimation of the CTE using both parametric and nonparametric approaches. In parametric case the conditional tail expectation and variance are analytically derived for the exponential distribution family and its transformed distributions. For small i.i.d. samples the exact bootstrap (EB) and the influence function are used as nonparametric methods in estimating the bias and the the variance of the empirical CTE. In particular, it is shown that the bias is corrected using the bootstrap for the CTE case. In variance estimation the influence function of the bootstrapped quantile is derived, and can be used to estimate the variance of any bootstrapped L-estimator without simulations, including the VaR and the CTE, via the nonparametric delta method. An industry model are provided by applying theoretical findings on the bias and the variance of the estimated CTE. Finally a new capital allocation method is proposed. Inspired by the allocation of the solvency exchange option by Sherris (2006), this method resembles the CTE allocation in its form and properties, but has its own unique features, such as managerbased decomposition. Through a numerical example the proposed allocation is shown to fail the no undercut axiom, but we argue that this axiom may not be aligned with the economic reality.

Orthogonal frequency division multiple access (OFDMA) is becoming a widely deployed mechanism in broadband wireless networks due to its capability to combat the channel impairments and support high data rate. Besides, dealing with small units of spectrum, named sub-carriers, instead of whole spectrum, results in enhanced flexibility and efficiency of the resource allocation for OFDMA networks. Resource allocation and scheduling in the downlink of OFDMA networks supporting heterogeneous traffic will be considered in this thesis. The purpose of resource allocation is to allocate sub-carriers and power to users to meet their service requirements while maintaining fairness among users and maximizes resource utilization. To achieve these objectives, utility-based resource allocation schemes along with some state-of-the-art resource allocation paradigms such as power control, adaptive modulation and coding, sub-carrier assignment, and scheduling are adopted. On one hand, a utility-based resource allocation scheme improves resource utilization by allocating enough resources based on users' quality of service (QoS) satisfaction. On the other hand, resource allocation based on utilities is not trivial when users demand different traffic types with convex and nonconvex utilities. The first contribution of the thesis is the proposing of a framework, based on joint physical (PHY) and medium access (MAC) layer optimization, for utility-based resource allocation in OFDMA networks with heterogeneous traffic types. The framework considers the network resources limitations while attempting to improve resources utilization and heterogeneous users' satisfaction of service. The resource allocation problem is formulated by continuous optimization techniques, and an algorithm based on interior point and penalty methods is suggested to solve the problem. The numerical results show that the framework is very efficient in treating the nonconvexity problem and the allocation is accurate comparing with the ones obtained by a genetic search algorithm. The second contribution of the thesis is the proposing of an opportunistic fair scheduling scheme for OFDMA networks. The contribution is twofold. First, a vector of fair weights is proposed, which can be used in any scheduling scheme for OFDMA networks to maintain fairness. Second, the fair weights are deployed in an opportunistic scheduling scheme to compensate the unfairness of the scheduling. The proposed scheme efficiently schedules users by exploiting multiuser diversity gain, OFDMA resource allocation flexibility, and utility fair service discipline. It is expected that the research in the thesis contributes to developing practical schemes with low complexity for the MAC layer of OFDMA networks.

In a data centre, server clusters are typically used to provide the required processing capacity to provide acceptable response time performance to interactive applications. The workload of each application may be time-varying. Static allocation to meet peak demand is not an efficient usage of resources. Dynamic resource allocation, on the other hand, can result in efficient resource utilization while meeting the performance goals of individual applications. In this thesis, we develop a new interactive system model where the number of logon users changes over time. Our objective is to obtain results that can be used to guide dynamic resource allocation decisions. We obtain approximate analytic results for the response time distribution at steady state for our model. Using numerical examples, we show that these results are acceptable in terms of estimating the steady state probabilities of the number of logon users. We also show by comparison with simulation that our results are acceptable in estimating the response time distribution under a variety of dynamic resource allocation scenarios. More importantly, we show that our results are accurate in terms of predicting the minimum number of processor nodes required to meet the performance goal of an interaction application. Such information is valuable to resource provisioning and we discuss how our results can be used to guide dynamic resource allocation decisions.

The use of relay nodes to improve the performance of broadband wireless access (BWA) networks has been the subject of intense research activities in recent years. Relay enhanced BWA networks are anticipated to support multimedia traffic (i.e., voice, video, and data traffic). In order to guarantee service to network users, efficient resource distribution is imperative. Wireless multihop networks are characterized by two inherent dynamic characteristics: 1) the existence of wireless interference and 2) mobility of user nodes. Both mobility and interference greatly influence the ability of users to obtain the necessary resources for service. In this dissertation we conduct a comprehensive research study on the topic of resource allocation in the presence of interference and mobility. Specifically, this dissertation investigates the impact interference and mobility have on various aspects of resource allocation, ranging from fairness to spectrum utilization. We study four important resource allocation algorithms for relay enhanced BWA networks. The problems and our research achievements are briefly outlined as follows. First, we propose an interference aware rate adaptive subcarrier and power allocation algorithm using maximum multicommodity flow optimization. We consider the impact of the wireless interference constraints using Signal to Interference Noise Ratio (SINR). We exploit spatial reuse to allocate subcarriers in the network and show that an intelligent reuse of resources can improve throughput while mitigating the impact of interference. We provide a sub-optimal heuristic to solve the rate adaptive resource allocation problem. We demonstrate that aggressive spatial reuse and fine tuned-interference modeling garner advantages in terms of throughput, end-to-end delay and power distribution. Second, we investigate the benefits of decoupled optimization of interference aware routing and scheduling using SINR and spatial reuse to improve the overall achievable throughput. We model the routing optimization problem as a linear program using maximum concurrent flows. We develop an optimization formulation to schedule the link traffic such that interference is mitigated and time slots are reused appropriately based on spatial TDMA (STDMA). The scheduling problem is shown to be NP-hard and is solved using the column generation technique. We compare our formulations to conventional counterparts in the literature and show that our approach guarantees higher throughput by mitigating the effect of interference effectively. Third, we investigate the problem of multipath flow routing and fair bandwidth allocation under interference constraints for multihop wireless networks. We first develop a novel isotonic routing metric, RI3M, considering the influence of interflow and intraflow interference. Second, in order to ensure QoS, an interference-aware max-min fair bandwidth allocation algorithm, LMX:M3F, is proposed where the lexicographically largest bandwidth allocation vector is found among all optimal allocation vectors while considering constraints of interference on the flows. We compare with various interference based routing metrics and interference aware bandwidth allocation algorithms established in the literature to show that RI3M and LMX:M3F succeed in improving network performance in terms of delay, packet loss ratio and bandwidth usage. Lastly, we develop a user mobility prediction model using the Hidden Markov Model(HMM) in which prediction control is transferred to the various fixed relay nodes in the network. Given the HMM prediction model, we develop a routing protocol which uses the location information of the mobile user to determine the interference level on links in its surrounding neighborhood. We use SINR as the routing metric to calculate the interference on a specific link (link cost). We minimize the total cost of routing as a cost function of SINR while guaranteeing that the load on each link does not exceed its capacity. The routing protocol is formulated and solved as a minimum cost flow optimization problem. We compare our SINR based routing algorithm with conventional counterparts in the literature and show that our algorithm reinforces routing paths with high link quality and low latency, therefore improving overall system throughput. The research solutions obtained in this dissertation improve the service reliability and QoS assurance of emerging BWA networks.

This research focuses on the role of water governance in building resilience and fostering sustainability in socio-ecological systems (SES). Water governance refers to the structures, processes and actors – and the dynamic interactions among them – that facilitate and influence decisions affecting water resources and aquatic ecosystems in terms of their collective influence on sustainability of SES. As human water demands grow and the impacts of climate change set in, water governance regimes are increasingly challenged to provide sufficient water to support livelihood and economic activities while also protecting the life-supporting functions of freshwater ecosystems. The objective of this thesis was to understand and assess whether governance arrangements for water allocation in Ontario are effectively addressing this challenge. A broad literature review focused on three overlapping bodies of literature – (1) sustainability, resilience and systems thinking, (2) governance and planning, and (3) water policy and management. From this review, a conceptual framework was developed to guide understanding and assessing the effectiveness of water governance arrangements to enhance resilience and foster sustainability. The framework includes seven criteria: socio-ecological system integrity; equity; efficiency; transparency and accountability; participation and collaboration; precaution and adaptation; and, integration. A case study of water allocation was undertaken in Whiteman’s Creek watershed, a sub-watershed of the Grand River in southwestern Ontario, where water scarcity is a persistent concern and where conditions are anticipated to worsen under climate change, posing problems for both human livelihoods and the integrity of the creek ecosystem. Data for the case study were collected through content analysis of documents, records and websites and through semi-structured interviews with key informants. The conceptual framework was used to synthesize the data into a narrative from which recommendations for strengthening water governance were proposed. Water governance is increasingly taking on forms more distributed or polycentric in structure and more inclusive, collaborative and participatory than previous models built largely on top-down, centralized decision making. This shift is viewed by many as a critical element for building resilience and sustainability. While the governance regime for water allocation in Whiteman’s Creek reflects these general trends, the case study findings suggest that Ontario’s existing water governance system is not capable to deal effectively with more frequent and prolonged drought conditions anticipated in Whiteman’s Creek as the climate changes. Introduction of decentralized governance arrangements over the past decade, primarily the Ontario Low Water Response (OLWR) plan, has enhanced capacity in Whiteman’s Creek to cope with recurring low water conditions. Yet when pressed with extreme drought conditions, as experienced during the period of field work for this thesis, the challenge of satisfying both instream water needs and withdrawal uses reveals weaknesses in the governance system, including unclear decision-making criteria (e.g., related to hydrological thresholds), uncertainty related to roles and responsibilities of various actors, and generally limited capacity for precaution and adaptation. Recommendations are proposed for improving water governance in Whiteman’s Creek, and in Ontario more broadly. Ecologically-based thresholds should be integrated into water management regimes to ensure sufficient water is secured to sustain aquatic ecosystem integrity and to provide clarity on limits to permitted allocation and OWLR thresholds. More broadly, a focus on building adaptive capacity and engaging in anticipatory planning will be central to building resilience and fostering sustainability in Whiteman’s Creek.

Although project portfolio management (PPM) has been an active research area over the past 50 years, budget allocation models that consider competition are sparse. Firms faced with the project portfolio management problem must not only consider their current projections for the returns from their projects’ target markets, but must also anticipate that these returns can depend significantly on the investment decisions made by their competitors. In this thesis, we develop four Competitive PPM (CPPM) models wherein firms allocate resources between multiple projects and project returns are influenced by the actions taken by competitors. In the first two CPPM problems, we assume all-or-nothing project investment decisions where firms fully commit to either a project targeting a mature or an emerging market and the investment amount is fixed (first model) or a decision variable (second model). In the final two CPPM problems, firms have a fixed budget which they allocate in a continuous manner between two markets (third model) or multiple markets (fourth model). The returns each firm obtains from investments into these markets are assumed to follow an s-shaped curve (first model), the Inada (1963) conditions (third model), or are determined based on linear demand functions (second and fourth model). In the first model, two competing firms consider investing into two separate projects targeting a mature and an emerging market. We assume that firms have symmetric investment opportunities for each market and each firm simultaneously decides whether to invest in the mature or the emerging market. The returns from these markets are assumed to follow an s-shaped curve and depend on both firms’ investment decision. We characterize the variety of interactions that may emerge in symmetric environments (e.g., Prisoner’s Dilemma or Game of Chicken). For each game, we outline the CPPM strategy that can offer higher returns by exploiting first-mover advantages, cooperation opportunities and aggressive choices. We also discuss the market conditions that lead to these games. In the second model, a similar CPPM setting is considered where two symmetric firms face two target markets. However, we assume that demand for the emerging market is uncertain and may expand through firms’ market entry (positive diffusion effects), while demand for the mature market is known with certainty and cannot expand. Firms decide when to invest, in which market to invest, and how much to invest into this market. Our analysis reveals that the existence of multiple investment opportunities may induce firms to delay their investment even in the absence of demand uncertainty, and that high diffusion effects coupled with low demand uncertainty can drive firms to invest early even if both firms could increase returns by delaying their investment. We then study the asymmetric case where firms differ with respect to their costs and diffusion effects and show some counter-intuitive results. In the third CPPM problem, we consider continuous budget allocations and prove that while a monopoly firm bases its budget allocation decision solely on the marginal returns of the two markets, duopoly firms also account for their average returns from the two markets. This drives duopoly firms, in particular the firm with the smaller budget, to invest more heavily into the mature market. We show that as a firm’s budget increases, the share of its budget that is invested into the mature market decreases while its competitor’s investment into the mature market increases. This chapter also explores how changes to the market parameters and market uncertainty affect the resource allocation decision of firms under competition. Considering the special case of identical budgets, we prove that as the number of competing firms increases (with a fixed total budget), firms allocate an even greater share of their budget into the mature market. The fourth model considers a general case where a number of budget-constrained firms engage in production decisions for multiple markets under competition. Each firm decides how much to produce for each market, subject to its budget constraint. We prove that firms produce greater quantities for markets with higher than average base demand and that these quantities are increasing in the number of competitors (assuming identical production capacities). With asymmetric production capacities, we numerically illustrate how firms with large production capacities may, instead, increase production into lower than average base demand markets. Furthermore, we characterize the increase in return firms can expect from budget increases and conjecture that if some markets are not served by all firms, the remaining firms reduce their production into those markets where some firms are not producing.

Within the artificial intelligence subfield of multiagent systems, one challenge that arises is determining how to efficiently allocate resources to all agents in a way that maximizes the overall expected utility. In this thesis, we explore a distributed solution to this problem, one in which the agents work together to coordinate their requests for resources and which is considered to be ex-ante rational: in other words, requiring agents to be willing to give up their current resources to those with greater need by reasoning about what is for the common good. Central to our solution is allowing for preemption of tasks that are currently occupying resources; this is achieved by introducing a concept from adjustable autonomy multiagent systems known as a transfer of control (TOC) strategy. In essence a TOC strategy is a plan of an agent to acquire resources at future times, and can be used as a contingency plan that an agent will execute if it loses its current resource. The inclusion of TOC strategies ultimately provides for a greater optimism among agents about their future resource acquisitions, allowing for more generous behaviours, and for agents to more frequently agree to relinquish current resources, resulting in more effective preemption policies. Three central contributions arise. The first is an improved methodology for generating transfer of control strategies efficiently, using a dynamic programming approach, which enables a more effective employment of TOCs in our resource allocation solution. The second is an important clarification of the value of integrating learning techniques in order for agents to acquire improved estimates of the costs of preemption. The last is a validation of the overall multiagent resource allocation (MARA) solution, using simulations which show quantifiable benefits of our novel approach. In particular, we consider in detail the emergency medical application of mass casualty incidents and are able to demonstrate that our approach of integrating transfer of control strategies results in effective allocation of patients to doctors: ones which in simulations re- sult in dramatically fewer patients in a critical healthstate than are produced by competing MARA algorithms. In short, we offer a principled solution to the problem of preemption, allowing the elimination of a source of inefficiencies in fully distributed multiagent resource allocation systems; a faster method for generation of transfer of control strategies; and a convincing application of the system to a real world problem where human lives are at stake.