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.

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.

Quality-of-service (QoS) guarantees will play a critically important role in future mobile wireless networks. In this dissertation, we study a set of QoS-driven resource allocation problems for mobile wireless communications and networks. In the first part of this dissertation, we investigate resource allocation schemes for statistical QoS provisioning. The schemes aim at maximizing the system/network throughput subject to a given queuing delay constraint. To achieve this goal, we integrate the information theory with the concept of effective capacity and develop a unified framework for resource allocation. Applying the above framework, we con-sider a number of system infrastructures, including single channel, parallel channel, cellular, and cooperative relay systems and networks, respectively. In addition, we also investigate the impact of imperfect channel-state information (CSI) on QoS pro-visioning. The resource allocation problems can be solved e±ciently by the convex optimization approach, where closed-form allocation policies are obtained for different application scenarios. Our analyses reveal an important fact that there exists a fundamental tradeoff between throughput and QoS provisioning. In particular, when the delay constraint becomes loose, the optimal resource allocation policy converges to the water-filling scheme, where ergodic capacity can be achieved. On the other hand, when the QoS constraint gets stringent, the optimal policy converges to the channel inversion scheme under which the system operates at a constant rate and the zero-outage capacity can be achieved. In the second part of this dissertation, we study adaptive antenna selection for multiple-input-multiple-output (MIMO) communication systems. System resources such as subcarriers, antennas and power are allocated dynamically to minimize the symbol-error rate (SER), which is the key QoS metric at the physical layer. We propose a selection diversity scheme for MIMO multicarrier direct-sequence code- division-multiple-access (MC DS-CDMA) systems and analyze the error performance of the system when considering CSI feedback delay and feedback errors. Moreover, we propose a joint antenna selection and power allocation scheme for space-time block code (STBC) systems. The error performance is derived when taking the CSI feedback delay into account. Our numerical results show that when feedback delay comes into play, a tradeoff between performance and robustness can be achieved by dynamically allocating power across transmit antennas.

Multichannel marketing is the practice of simultaneously offering information, goods, services, and support to customers through two or more synchronized channels. In this dissertation, I develop an integrated framework of multichannel marketing and develop models to assist managers in their marketing resource allocation decisions. In the first essay of the dissertation, I investigate the factors that drive customers multichannel shopping behavior and identify its consequences for retailers. In the second essay, I build on this work and develop a model that enables firms to optimize their allocation of marketing resources across different customer-channel segments. In the first essay, I develop a framework comprising the factors that drive consumers’ channel choice, the consequences of channel choice, and their implications for managing channel equity. The results show that customer-channel choice is driven in a nonlinear fashion by a customer demographic variable such as age and is also influenced by consumer shopping traits such as number of categories bought and the duration of relationship with a retailer. I show that by controlling for the moderating effects of channel-category associations, the influence of customers’ demographics and shopping traits on their channel choices can vary significantly across product categories. Importantly, the results show that multichannel shoppers buy more often, buy more items, and spend considerably more than single channel shoppers. The channel equity of multichannel customers is nearly twice that of the closest single channel customers (online or offline). In the second essay, I propose a model for optimal allocation of marketing efforts across multiple customer-channel segments. I first develop a set of models for consumer response to marketing efforts for each channel-customer segment. This set comprises four models, the first for purchase frequency, the second for purchase quantity, the third for product return behavior, and the fourth for contribution margin of purchase. The results show that customers’ responses to firm marketing efforts vary significantly across the customer-channel segments. They also suggest that marketing efforts influence purchase frequency, purchase quantity and monetary value in different ways. The resource allocation results show that profits can be substantially improved by reallocating marketing efforts across the different customer-channel segments.

In order to maximize the capacities of resources constraints in the multiple projects environment, it is firstly necessary to make sure where the resources constraints are, and to schedule them. And then, add a set of time buffer to protect the bottle-neck resources. For some purposes, the project schedule is not easy to be altered in enterprises. Instead of adding a set of time buffer, we use others ways to protect resource constraints and to improve capacities. 4 cases are discussed in this research respectively, and the characteristics in this research contain whether the project plan is announced at the year beginning, whether the number of team members is fixed, and whether the project is outsourced or in-house. The main purpose is to investigate how the enterprises arrange the resources in 3 different periods: the projects at the year beginning, new projects joined during a year , and new demands in the existing projects during a year. The research results show: (1) Enterprises usually recruit employees at the beginning of the year, which prevents from the unqualified human resource as the projects going. (2) The teams with fixed member are allocated members in the projects which are the same domain. It¡¦s not easy to support between projects of different domains in the same team, except IT support. It¡¦s also difficult to support between teams, because the relationship of teams is competitive. (3) In the established team in terms of projects, enterprises assign team members in project which are the same domain by the function. It¡¦s easy to support between members with the same domain. It¡¦s not easy to support between members with different domain, except IT support. (4) Carrying out outsourcing projects in the enterprises, complete project in the different period to explore the resource constraints. (5) Carrying out in-house projects in the enterprise, reduce insignificance project scope or to reschedule insignificance project to explore the resource constraints. (6) Carrying outsourcing and in-house projects in the enterprises, reduce insignificance project scope or to reschedule insignificance project to explore the resource constraints.

The purpose of this dissertation is to understand how individuals’ preferences for resource allocation affect their attitudinal and behavioral responses towards the organization. Building on the three main allocation norms (equity norm, equality norm and need norm) and taking the perspective of the recipient of an allocation, a model that predicts the antecedents of norm preference and consequences of using different allocation norms by the organization is presented and tested with a sample of Turkish registered nurses. Results show that recipients have differential preferences for allocation norms depending on resource type being allocated and characteristics of the environment. Furthermore, the fit or misfit between recipients’ preferred allocation norm and the allocation norm used by the organization affects recipients’ justice perceptions about the organization, their outcome satisfaction and performance.

Thesis (M.A.)--West Virginia University, 2002. / Title from document title page. Document formatted into pages; contains vi, 56 p. Vita. Includes abstract. Includes bibliographical references (p. 40-43).

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.