In this thesis, the problem of optimal resource allocation in real-time systems is studied. A recently proposed resource allocation approach called Q-RAM (Quality of Service based Resource Allocation Model) is investigated in detail. The goal of the Q-RAM based approaches is to minimize the execution speed in real-time systems while meeting resource constraints and maximizing total utility. Phased array radar system is an example of a system in which multiple tasks contend for multiple resources in order to satisfy their requirements. In this system, multiple targets are tracked (each a separate task) by the radar system simultaneously requiring processor and energy resources of the radar system. Phased array radar system is considered as an illustrative application area in order to comparatively evaluate the resource allocation approaches. For the problem of optimal resource allocation with single resource type, the Q-RAM algorithm appears incompletely specified, namely it does not have a termination criteria set that can terminate the algorithm in all possible cases. In the present study, first, the Q-RAM solution approach to the radar resource allocation problem with single resource type is extended to give a global optimal solution in all possible termination cases. For the case of multiple resource types, the Q-RAM approach can only generate near-optimal results. In this thesis, for the formulated radar resource allocation problem with multiple resource types, the Methods of Feasible Directions are considered as an alternative solution approach. For the multiple resource type case, the performances of both the Q-RAM approach and the Methods of Feasible Directions are investigated in terms of optimality and convergence speed with the help of Monte-Carlo simulations. It is observed from the results of the simulation experiments that the Gradient Projection Method produce results outperforming the Q-RAM approach in closeness to optimality with comparable execution times.

Recently, there has been a massive growth in the number of mobile users and their traffic. The data traffic volume almost doubles every year. Mobile users are currently running multiple applications that require higher bandwidth which makes users so limited to the service providers' resources. Increasing the utilization of the existing spectrum can significantly improve network capacity, data rates and user experience. Spectrum sharing enables wireless systems to harvest under-utilized swathes of spectrum, which would vastly increase the efficiency of spectrum usage. Making more spectrum available can provide significant gain in mobile broadband capacity only if those resources can be aggregated efficiently with the existing commercial mobile system resources. Carrier aggregation (CA) is one of the most distinct features of 4G systems including Long Term Evolution Advanced (LTE-Advanced). In this dissertation, a resource allocation with carrier aggregation framework is proposed to allocate multiple carriers resources optimally among users with elastic and inelastic traffic in cellular networks. We use utility proportional fairness allocation policy, where the fairness among users is in utility percentage of the application running on the user equipment (UE). A resource allocation (RA) with CA is proposed to allocate single or multiple carriers resources optimally among users subscribing for mobile services. Each user is guaranteed a minimum quality of service (QoS) that varies based on the user's application type. In addition, a resource allocation with user discrimination framework is proposed to allocate single or multiple carriers resources among users running multiple applications. Furthermore, an application-aware resource block (RB) scheduling with CA is proposed to assign RBs of multiple component carriers to users' applications based on a utility proportional fairness scheduling policy. We believe that secure spectrum auctions can revolutionize the spectrum utilization of cellular networks and satisfy the ever increasing demand for resources. Therefore, a framework for multi-tier dynamic spectrum sharing system is proposed to provide an efficient sharing of spectrum with commercial wireless system providers (WSPs) with an emphasis on federal spectrum sharing. The proposed spectrum sharing system (SSS) provides an efficient usage of spectrum resources, manages intra-WSP and inter-WSP interference and provides essential level of security, privacy, and obfuscation to enable the most efficient and reliable usage of the shared spectrum. It features an intermediate spectrum auctioneer responsible for allocating resources to commercial WSPs' base stations (BS)s by running secure spectrum auctions. In order to insure truthfulness in the proposed spectrum auction, an optimal bidding mechanism is proposed to enable BSs (bidders) to determine their true bidding values. We also present a resource allocation based on CA approach to determine the BS's optimal aggregated rate allocated to each UE from both the BS's permanent resources and winning auctioned spectrum resources. / Ph. D.

Force deployment and optimal resource allocation has been an area of considerable research interest in conventional warfare. In the modern scenario, with significant advances in technology related to communication and computation, sophisticated decision-making in these situations has become feasible. This has generated renewed interest in formulating decision-making problems in these areas and seeking optimal solutions to them. This thesis addresses one such problem in which the defending forces need to optimally Partition their resources between several attacking forces of differing strengths. The basic model considered for resource allocation is Lanchester attrition models. Lanchester models are deterministic differential equations that model attrition to forces in convict. In this thesis we address a resource allocation problem where the resource allocation is done using different approaches. First, we developed a (2,1) model using the Lanchester square law model for attrition. For this model we assumed that the attacking force consists of two types of forces and the defending force consists of only one type of force. The objective is to optimally partition the defending force against the two attacking forces so as to maximize the surviving defending force strength and to minimize the attacking force strength. The objective function considered in this thesis is the weighted sum of the surviving defending force strength and the destroyed attacking force strength. We considered a resource allocation problem in which allocation of resources are done using four different approaches. The simplest is the case when allocation is done initially and no further action is taken Iv Abstract v (Time Zero Allocation (TZA)). For the TZA allocation scheme, when any of attacking forces gets destroyed, the corresponding defending force which was engaging that attacking force will stop interacting further. This situation rarely happens in reality. Hence to make this scenario more realistic, we considered another allocation scheme in which allocation is followed by redistribution of resources depending on certain decisive events (Time Zero Allocation with Redistribution (TZAR)). In TZA and TZAR schemes, the allocation of defending force is done only at the initial time. Deviating from this assumption, we considered another allocation scheme in which a constant allocation ratio is used continuously over time till the end of the convict (Continuous Constant Allocation (CCA). To account for the redistribution of resources we extended this allocation scheme to the case in which continuous constant allocation is followed by redistribution of the resources (Continuous Constant Allocation with Redistribution (CCAR)). In each of these formulations we define the conditions for an optimal resource partitioning and allocation. We were able to obtain analytical expression for resource partitioning in almost all of these cases. Next, in order to consider situations in which area fire is required, we developed a (2,1) model using Lanchester linear law model for attrition. Here we considered a resource allocation problem in which the resource allocation is done using ideas similar to the square law case. In the Linear law, the resources will get destroyed completely only at infinite time, hence a situation for redistribution of resources does not arise for this law. We considered Time Zero Allocation and Continuous Constant Allocation schemes for this law. We obtained analytical results for the TZA scheme. For the CCA scheme, closed form solutions are difficult to obtain but numerical solutions were obtained. The above schemes were extended to an (n, 1) model for resource allocation using Lanchester square and linear laws. Here the defending forces have to determine an optimal partitioning of available resources to counter attacks from an adversary from n different fronts. For the square law model, we considered both TZA and CCA schemes for resource allocation. As the number of force types increases, the equations becomes much more complicated and the analytical solutions are difficult to obtain. We were able to obtain analytical solutions for some of the situations that occurs during the conflict. For the linear law, we considered only the TZA scheme since, even for the simpler (2,1) model, the analytical solutions are difficult to obtain for the CCA scheme. The resource allocation strategies developed in this thesis contribute to the growing research in the field of conflicts. The thesis concludes with a discussion on some future Extensions of this work.

Made available in DSpace on 2016-03-15T19:31:16Z (GMT). No. of bitstreams: 1 Roberto Borges Kerr.pdf: 1260455 bytes, checksum: cdd7f0a9ec7f2b8e88d2c76d72925e9c (MD5) Previous issue date: 2008-09-29 / The Theory of Financial Decision Making tries to understand and explain how individuals and their agents make choices among alternatives that have uncertain payoffs over multiple time periods. The theory that explains how and why these decisions are made allows serveral models, presented in this thesis. However, the ortodox theory does not recognize the qualitative importance and quantitative implications of the interactions among irriversibility, uncertainty, and optimal point in time for investment. Decision making involves almost always three important characteristics; the investment is partially or completely irreversible, there is uncertainty about the stream of future cash flows, and there is a window of time for the decision to be made. These characteristics have to be taken into consideration in determining the optimal time for investment, because flexibility has value. The objective of this thesis is to demonstrate that the real options approach to uncertainty in resource allocation and investment decision making is able to capture the value of managerial flexibility properly and produces better results in modeling the optimal time to cut a stand of trees in a forestry investment project. The theory of real options is used to model the optimal tree harvesting decision. The linear complementarity partial differential inequalities were solved using the numerical method known as fully implicit finite difference method, with the projected over relaxation (PSOR) algorithm, using a software developed specially for this purpose. / A Teoria das Decisões Financeiras procura entender e explicar como indivíduos e seus agentes tomam decisões de consumo, poupança e investimento dentre as alternativas disponíveis. O estudo do consumo e de decisões de investimento, feitas por indivíduos e empresas, permite diversos modelos, apresentados neste trabalho. Entretanto, a teoria de investimento ortodoxa não reconhece a importância qualitativa e as implicações quantitativas da interação entre irreversibilidade, incerteza e a decisão ótima do ponto no tempo. A maioria das decisões de investimento compartilha em maior ou menor grau três características importantes, o investimento é parcialmente ou completamente irreversível, há incerteza quanto aos fluxos de caixa futuros do investimento, há alguma margem de tempo para que a decisão seja tomada. Estas três características têm que ser levadas em conta na determinação da decisão ótima de investimento, pois a flexibilidade tem valor. O objetivo deste trabalho é demonstrar que a abordagem das opções reais é capaz de quantificar adequadamente a flexibilidade gerencial na avaliação de um projeto de investimento de capital sob incerteza e produz melhores resultados na modelagem da decisão ótima de corte de um povoamento de árvores em um projeto de reflorestamento. A decisão ótima de colheita foi modelada como uma opção real do tipo americano, as inequações diferencias do problema de complementaridade linear foram resolvidas pelo método das diferenças finitas totalmente implícitas e o sistema linear de equações simultâneas foi resolvido por meio de uma técnica interativa denominada projected over relaxation (PSOR), com a ajuda de um software especialmente desenvolvido para este fim.