The increased use of mobile wireless devices that we have recently been witnessing, such as smartphones, tablets, e-readers, and WiFi enabled devices in general, is driving an unprecedented increase in the amount of data traffic. This fast market adoption of the wireless technology along with the tremendous success of multimedia applications brought about higher capacity, connectivity, and Quality of Service (QoS) requirements that can no longer be met with traditional networking paradigms. As a result, heterogeneous wireless networks have recently emerged as a potential solution for meeting such new requirements. Hybrid wireless mesh networks and femtocell/macrocell networks are examples of these newly emerging heterogeneous networks. While mesh networks are viewed as the backbone/core network, femtocell and cellular networks are viewed as the access networks linking end-users with the backbone networks. In this dissertation, we address the problem of resource allocation in heterogeneous networks. We investigate both types of networks/architectures: next-generation wireless backbone networks or simply wireless mesh networks (WMNs) and next-generation wireless access networks or simply femtocell (FC) networks. WMNs were first introduced to foster the availability of Internet services anywhere and at anytime. However, capacity limitation has been a fundamental challenge to WMNs, mainly due to the interference arising from the wireless nature of the environment as well as to the scarcity of the radio/channel resources. To overcome this problem, we propose in this dissertation an efficient scheduling scheme that reduces interference among active links via wise time and frequency assignments to the wireless mesh routers. The developed scheme is traffic aware in that it maximizes the capacity of wireless links but while accounting for their traffic loads, thus meeting the end-to-end bandwidth requirements as much as possible. In the second part of this thesis, we focus on developing power allocation techniques for FC networks. FCs have recently emerged as a key networking solution that has great potential for improving the capacity and coverage of traditional macrocell (MC) networks through high-speed indoor coverage. Their deployment, however, has given rise to new interference challenges which are mainly due to the FCs' autonomous nature and to the unreliability of the wireless medium. Driven by this fact, in the second part of this thesis, we first design a fully-distributed estimation-based power allocation scheme that aims at fairly maximizing the capacity of FC networks. Second, we propose a novel distributed stochastic power control scheme that aims at maintaining the users' minimum= required QoS. Finally, we provide cross-layer performance analysis of two-tier FC networks, in which we characterize the uplink interference and study its impact on the data-link layer QoS performance in FC networks. / Graduation date: 2013

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

In this work we examine a state constrained resource allocation model a with finite time horizon. Therefore, we use the necessary conditions of the Pontrjagin's Maximum Principle to find candidates for the solution and verify them later on using the sufficient conditions given by the duality concept of Klötzler. Moreover, we proof that the solution of the corresponding infinite horizon model does not fulfill the overtaking criterion of Weizsäcker.

Mobile WiMAX multicast communication that is an efficient mechanism for one-to-many transmissions over a broadcast wireless channel is considered as a key technology for supporting emerging broadband multimedia services in the next generation wireless networks, such as Internet Protocol Television (IPTV). Therefore, it is critical to design an efficient multicast resource allocation scheme to support these multimedia services. This thesis proposes a new Band AMC multicast resource allocation scheme for pedestrian mobility environment by collaborating with forward error correction (FEC) coding technology. IPTV multicast is characterized by groups of users watching popular video programs over a wireless fading channel. The proposed scheme can flexibly select efficient channels and appropriate modulation to reduce the resource usage for multicast. Besides, the proposed scheme can provide quality of service (QoS), overcome packet loss and improve the performance over the traditional multicast techniques on Band AMC mode used in today¡¦s wireless OFDMA communication system.

Recent enthusiasm in grid computing has resulted in a tremendous amount of research in resource scheduling techniques for tasks in a workflow. Most of the work on resource scheduling is aimed at minimizing the total response time for the entire workflow and treats the estimated response time of a task running on a local resource as a constant. However in a dynamic environment such grid computing, the behavior of resources simply cannot be ensured. In this thesis, thus, we propose a probabilistic framework for resource scheduling in a grid environment that views the task response time as a probability distribution to take into consideration the uncertain factors. The goal is to dynamically assign resources to tasks so as to maximize the probability of completing the entire workflow within a desired total response time. We propose three algorithms for the dynamic resource scheduling in grid environment, namely the integer programming, the max-max heuristic and the min-max heuristic. Experimental results using synthetic data derived from a real protein annotation workflow application demonstrate that the proposed probability-based scheduling strategies have similar performance in an environment with homogeneous resources and perform better in an environment with heterogeneous resources, when compared with the existing methods that consider the response time as a constant. Of the two proposed heuristics, min-max generally yields better performance.

With the rapid development of Web-based technologies, our daily life has become intensely involved with Internet. Combined with the maturity of wireless network technologies, the transmission of multimedia data using mobile communication equipments will surely become the next step of Internet usage. More and more real-time data and massive amount of information are being transmitted on the Internet, making the bandwidth a scarce resource. To resolve the congestion of Internet, therefore, the efficient management and distribution of limited and valuable resource is more important than the enhancement of it. Our research posed a dynamic resource allocation method, which exploited the reward-penalty concept in order to find the most efficient allocation solution under the constraint of limited resources. The method enabled the users who need to use resource to achieve the necessary resources and their guarantee of quality. The system resource managers or service providers could make the best arrangement of their constrained resources and gain the highest reward through two essential procedures: Admission Control and Resource Allocation. Users themselves, on the other hand, ¡§smoothly¡¨ adjust the resource they had to match the resources they gained. Our algorithm provided existent users with what they requested while at the same time maximized the benefit of the system and made the most efficient arrangement of resources in regards to new requests. The consequences of simulation experiments showed that our system, which was based on reward-penalty model, is apparently superior to the so-called one based on reward model. The results also showed that CB method took users¡¦ reward rate as well as their penalty rate into account while maintaining admission control.

The progresses of technology bring up the bandwidth of the network, that can afford the increasing data amount from text to multimedia and make the network application development change with every passing day and become more varied. Recently, the rising of the wireless network attracts the public¡¦s attention. Compared with the traditional network, the wireless network has the advantage in its convenience and extensibility, but it has shortcoming in its bandwidth and stability. Because of the limited resources of the wireless network, introducing QoS (Quality of Service) can use the resources more efficiently. QoS guarantee the ability to achieve the special network applications¡¦ requests by using the network components or technology. QoS can differentiate between different classes of network services and allocate the system resources much better. Our research adopts the reward-penalty model to differentiate between different kinds of service requests to maximize the system¡¦s earning. It is decided by three QoS parameters, reward rate, delay penalty rate and drop penalty. In the reward-penalty model, the admission control¡¦s goal is to maximize the system¡¦s benefit. The purpose of our research is to design an efficient and dynamic resource allocation method, including admission control and resource allocation, to find the most efficient solution under the constraint of limited resources and smoothly adjust the resources of the existing users to promise the QoS. The consequences of simulation experiments show that MDI, posed in our research, has a better performance than other algorithms. Under different network environments, e.g. arrival rate, request bandwidth, transmission time, MDI is better and more stable than other algorithms. MDI can adjust QoS parameters, e.g. reward rate, delay penalty rate, drop penalty, to achieve different system¡¦s goal, like low delay rate or low drop rate. Thus it can be seen that MDI is able to not only make an efficient use of system resources but also adjust the QoS parameters to counter the change of the network environment in order to have a better performance.

Construction projects are complicated and fraught with so many risks that many projects are unable to meet pre-defined project objectives. Managers of construction projects require decision support tools that can be used to identify, analyze and implement measures that can mitigate the effects of project risks. Several risk analysis techniques have been developed over the years to enable construction project managers to make useful decisions that can improve the chances of project success. These risk analysis techniques however fail to simultaneously address risks relating to cost, schedule and quality. Also, construction projects may have scarce resources and construction managers still bear the responsibility of ensuring that project goals are met. Certain projects require trade-offs between technical and managerial risks and managers need tools that can help them do this. This thesis evaluates the usefulness of the Advanced Programmatic Risk Analysis and Management Model (APRAM) as a decision support tool for managing construction projects. The development of a visitor center in Midland, Texas was used as a case study for this research. The case study involved the implementation of APRAM during the concept phase of project development to determine the best construction system that can minimize the expected cost of failure. A risk analysis performed using a more standard approach yielded an expected cost of failure that is almost eight times the expected cost of failure yielded by APRAM. This study concludes that APRAM is a risk analysis technique that can minimize the expected costs of failure by integrating project risks of time, budget and quality through the allocation of resources. APRAM can also be useful for making construction management decisions. All identified component or material configurations for each alternative system however, should be analyzed instead of analyzing only the lowest cost alternative for each system as proposed by the original APRAM model. In addition, it is not possible to use decision trees to determine the optimal allocation of management reserves that would mitigate managerial problems during construction projects. Furthermore, APRAM does not address the issue of safety during construction and assumes all identifiable risks can be handled with money.

Recent enthusiasm in grid computing has resulted in a tremendous amount of research in resource scheduling techniques for tasks in a (scientific) workflow. There are many factors that may affect the scheduling results, one of which is whether the application is computing-intensive or data-intensive. Most of the grid scheduling researches focus on a single aspect of the environments. In this thesis, we base on our previous work, a probability-based framework for dynamic resource scheduling, and consider data transmission overhead in our scheduling algorithms. The goal is to dynamically assign resources to tasks so as to maximize the probability of completing the entire workflow within a desired total response time. We propose two algorithms for the dynamic resource scheduling in grid environment, namely largest deadline completion probability (LDCP) and smallest deadline completion probability (SDCP). Furthermore, considering the data transmission overhead, we propose a suite of push-based scheduling algorithms, which schedule all the immediate descendant tasks when a task is completed. These are algorithms will be compared to the pull-demand scheduling algorithms in our previous work and workflow-based algorithms proposed by other researchers. We use GridSim toolkit to model the grid environment and evaluate the performance of the various scheduling algorithms.

Includes bibliographical references (p. 54-56).