This dissertation explores reifying the management science concept of organizations as a collection of decisions. Organizational management entails resource allocation activities that can be formulated in terms of elementary relational functions. All elasticity-type formulations, most generic "production" functions, and various projection models that organizations might require (such as sales forecasts) can all be represented by elementary relational functions. Therefore, information systems in organizations can be representative of relationships between decision requirements, as theorized in relational model bases. A relational model-base structure acts as an integrative device by relating an organization's elementary relational functions to each other, with all that is kept for any model being the current values for coefficients and the now prevailing parametric values for the state variables of the model.Anchoring management information systems around relational model bases is particularly appropriate for organizations that have some reliance on real-time management decision making by providing the answer to two requirements for such organizations: one being the requirement for more accurate and current real-time, operational decision making within the organization; the other being the integration of functions for decision-making purposes within an organization. Relational model bases thus enable more dynamic management and become a central information system type for organizations that have dynamic resource allocation requirements that can employ technical tactics around such relational model bases. The relational model base would reflect revealed needs in an organization as opposed to projected needs, easing an organization's reliance on forecasting and moving it toward real-time decision making. The case for the introduction of these information systems is further strengthened by the fact that relational model base-type structures are already operating in production environments within organizations. The methodology used in this dissertation involved modeling organizational decision requirements in particular organizational cases to determine the behavior of relational model bases within those prototypical organizations and the application of relational model bases to real-time decision making. The first organizational scenario is a recursive agribusiness e-commerce case, with the target application being precision agriculture. The second scenario is a non-recursive ecological economics case, with the target application being preservation of biodiversity through land (habitat) protection.

Network Processors are multicore processors capable of processing network packets at wire speeds of multi-Gbps. Due to their high performance and programmability, these processors have become the main computing elements in many demanding network processing equipments like enterprise, edge and core routers. With the ever increasing use of the internet, the processing demands of these routers have also increased. As a result, the number and complexity of the cores in network processors have also increased. Hence, efficiently managing these cores has become very challenging. This dissertation discusses two main issues related to efficient usage of large number of parallel cores in network processors: (1) How to allocate work to the processing cores to optimize performance? (2) How to meet the desired performance requirement power efficiently? This dissertation presents the design of a hash based scheduler to distribute packets to cores. The scheduler exploits multiple dimensions of locality to improve performance while minimizing out of order delivery of packets. This scheduler is designed to work seamlessly when the number of cores allocated to a service is changed. The design of a resource allocator is also presented which allocates different number of cores to services with changing traffic behavior. To improve the power efficiency, a traffic aware power management scheme is presented which exploits variations in traffic rates to save power. The results of simulation studies are presented to evaluate the proposals using real and synthetic network traces. These experiments show that the proposed packet scheduler can improve performance by as much as 40% by improving locality. It is also observed that traffic variations can be exploited to save significant power by turning off the unused cores or by running them at lower frequencies. Improving performance of the individual cores by careful scheduling also helps to reduce the power consumption because the same amount of work can now be done with fewer cores with improved performance. The proposals made in this dissertation show promising improvements over the previous work. Hashing based schedulers have very low overhead and are very suitable for data rates of 100 Gbps and even beyond. / text

Cognitive radios is one of the hot topics for emerging and future wireless commu- nication. It has been proposed as a suitable solution for the spectrum scarcity caused by the increase in frequency demand. The concept is based on allowing unlicensed users, called cognitive or secondary users, to share the unoccupied frequency bands with their owners, called the primary users, under constraints on the interference they cause to them. The objective of our work is to propose some enhancements to cognitive radio systems while taking into account practical constraints. Cogni- tive radios requires a capability to detect spectrum holes (spectrum sensing) and a scheduling flexibility to avoid the occupied spectrum and selectively use the empty spectrum (dynamic resource allocation). Thus, the work is composed of two main parts. The first part focuses on cooperative spectrum sensing. We compute in this part the analytical performance of cooperative spectrum sensing under non identical and imperfect channels. Different schemes are considered for the cooperation between users such as hard binary, censored information, quantized, and soft information. The second part focuses on the dynamic resource allocation. We first propose low-cost re- source allocation algorithms that use location information to estimate the interference to primary users to replace absence of instantaneous channel state information. We extend these algorithms to handle practical implementation constraints such as dis- 5 crete bit-loading and collocated subcarriers allocations. We then propose a reduced dimension approach based on the grouping of subcarriers into clusters and performing the resource allocation over clusters of subcarriers instead of single subcarriers. This approach is shown to reduce the computational complexity of the algorithm with lim- ited performance loss. In addition, it is valid for a generic set of resource allocation problems in presence of co-channel interference between users.

The global value creation networks have experienced increased volatility and dynamic behavior in recent years, resulting in an acceleration of a trend already evident in the shortening of product and technology cycles. In addition, the manufacturing industry is demonstrating a trend of allowing customers to make specific adjustments to their products at the time of ordering. Not only do these changes require a high level of flexibility and adaptability from the cyber-physical systems, but also from the employees and the supervisory production planning. As a result, the development of control and monitoring mechanisms becomes more complex. It is also necessary to adjust the production process dynamically if there are unforeseen events (disrupted supply chains, machine breakdowns, or absences of staff) in order to make the most effective and efficient use of the available production resources. In recent years, reinforcement learning (RL) research has gained increasing popularity in strategic planning as a result of its ability to handle uncertainty in dynamic environments in real time. RL has been extended to include multiple agents cooperating on complex tasks as a solution to complex problems. Despite its potential, the real-world application of multi-agent reinforcement learning (MARL) to manufacturing problems, such as flexible job-shop scheduling, has been less frequently approached. The main reason for this is most of the applications in this field are frequently subject to specific requirements as well as confidentiality obligations. Due to this, it is difficult for the research community to obtain access to them, which presents substantial challenges for the implementation of these tools. ...

Quality-of-Service (QoS) to users is a critical requirement of resource allocation in wireless networks and has drawn significant research attention over a long time. However, the QoS requirements differ vastly based on the wireless network paradigm. At one extreme, we have a millimeter wave small-cell network for streaming data that requires very high throughput and low latency. At the other end, we have Machine-to-Machine (M2M) uplink traffic with low throughput and low latency. In this dissertation, we investigate and solve QoS-aware resource allocation problems for diverse wireless paradigms. We first study cross-layer dynamic spectrum allocation in a LTE macro-cellular network with fractional frequency reuse to improve the spectral efficiency for cell-edge users. We show that the resultant optimization problem is NP-hard and propose a low-complexity layered spectrum allocation heuristic that strikes a balance between rate maximization and fairness of allocation. Next, we develop an energy efficient downlink power control scheme in a energy harvesting small-cell base station equipped with local cache and wireless backhaul. We also study the tradeoff between the cache size and the energy harvesting capabilities. We next analyzed the file read latency in Distributed Storage Systems (DSS). We propose a heterogeneous DSS model wherein the stored data is categorized into multiple classes based on arrival rate of read requests, fault-tolerance for storage etc. Using a queuing theoretic approach, we establish bounds on the average read latency for different scheduling policies. We also show that erasure coding in DSS serves the dual purpose of reducing read latency and increasing the energy efficiency. Lastly, we investigate the problem of delay-efficient packet scheduling in M2M uplink with heterogeneous traffic characteristics. We classify the uplink traffic into multiple classes and propose a proportionally-fair delay-efficient heuristic packet scheduler. Using a queuing theoretic approach, we next develop a delay optimal multiclass packet scheduler and later extend it to joint medium access control and packet scheduling for M2M uplink. Using extensive simulations, we show that the proposed schedulers perform better than state-of-the-art schedulers in terms of average delay and packet delay jitter. / PHD

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / In clusters and virtualized data centers, the resources must be managed effectively by maximising the SLA fulfilment while minimising the cost. This work proposes a strategy for dynamic resource allocation in virtualized computing environments in order to reduce energy consumption without compromising performance requirements concerning availability and SLA violation. The proposed algorithms, based on classical heuristics, perform virtual machines migration between distinct hosts according to the variation in resources demand. These algorithms were evaluated from measurements in a real environment composed by heterogeneous virtualized hosts. We evaluate their performance in four different scenarios based on the CPU and memory utilization, number of migrations and energy consumption. In general, the results show that the algorithms responsible for consolidation and distribution of virtual machines between hosts are able to reduce energy consumption and dissipate the idle and overload points. / Em clusters e data centers virtualizados, os recursos precisam ser gerenciados com eficÃcia na busca de um trade-off entre a garantia de um atendimento satisfatÃrio à demanda por Qualidade de ServiÃo (QoS) e a reduÃÃo dos custos operacionais por parte dos provedores. Este trabalho propÃe uma estratÃgia para alocaÃÃo dinÃmica de recursos em ambientes computacionais virtualizados com vistas à reduÃÃo do consumo de energia, sem promover sobrecargas que podem comprometer o desempenho dos serviÃos ofertados. Os algoritmos propostos, baseados em heurÃsticas clÃssicas, realizam migraÃÃo de mÃquinas virtuais entre servidores distintos conforme variaÃÃo na demanda por recursos. Estes algoritmos foram verificados e validados por mediÃÃes em um ambiente real composto por servidores virtualizados heterogÃneos. O desempenho da proposta à avaliado em quatro cenÃrios distintos a partir das mÃtricas utilizaÃÃo de CPU, utilizaÃÃo de memÃria, nÃmero de migraÃÃes e consumo de energia. Os resultados mostraram que os algoritmos responsÃveis pela consolidaÃÃo e distribuiÃÃo das mÃquinas virtuais sÃo capazes de reduzir o consumo de energia e dissipar os pontos de Ãcio e sobrecarga do ambiente.

The advent of 5th generation of mobile networks (5G) will introduce some new challenges for the transport network. Different strategies can be employed by the network providers to address these challenges with the aim to achieve an efficient utilization of network resources. The most feasible option to achieve this goal is to introduce intelligence in the transport infrastructure by designing a flexible and programmable transport network. Network function virtualization (NFV) and dynamic resource sharing (DRS) are two possible techniques for realizing a flexible transport network. NFV allows to dynamically push network functions to different locations in the network, while DRS allows for sharing transport resources in a flexible manner. Both of these strategies can be realized by employing a programmable control framework based on software defined networking (SDN), which has implications on both the network data and control planes. However, this thesis specifically focuses on the data plane aspects of NFV and the control plane aspects of DRS. Considering the network caching as a specific example of network function, the data plane aspects of NFV are studied in terms of different architectural options for cache placement in order to see which options are the most efficient in terms of network power consumption and cost. The results presented in this thesis show that placing large-sized caches farther in the network for a large group of users is the most efficient approach. The control plane aspects of DRS are analyzed in terms of which provisioning strategy should be used for sharing a limited amount of transport resources. The analysis is presented for both a single-tenant case (i.e., where the role of service and network provider is played by the same entity), and a multi-tenant case (i.e., where a network provider manages the resources assigned to different service providers in an intelligent way). The results show that DRS performs much better than the conventional static approach (i.e., without sharing of resources), which translates into significant cost savings for the network providers. / <p>QC 20161115</p>

Hoy en día existen varias tecnologías que coexisten en una misma zona formando un sistema heterogéneo. Además, este hecho se espera que se vuelva más acentuado con todas las nuevas tecnologías que se están estandarizando actualmente. Hasta ahora, generalmente son los usuarios los que eligen la tecnología a la que se van a conectar, ya sea configurando sus terminales o usando terminales distintos. Sin embargo, esta solución es incapaz de aprovechar al máximo todos los recursos. Para ello es necesario un nuevo conjunto de estrategias. Estas estrategias deben gestionar los recursos radioeléctricos conjuntamente y asegurar la satisfacción de la calidad de servicio de los usuarios. Siguiendo esta idea, esta Tesis propone dos nuevos algoritmos. El primero es un algoritmo de asignación dinámica de recusos conjunto (JDRA) capaz de asignar recursos a usuarios y de distribuir usuarios entre tecnologías al mismo tiempo. El algoritmo está formulado en términos de un problema de optimización multi-objetivo que se resuelve usando redes neuronales de Hopfield (HNNs). Las HNNs son interesantes ya que se supone que pueden alcanzar soluciones sub-óptimas en cortos periodos de tiempo. Sin embargo, implementaciones reales de las HNNs en ordenadores pierden esta rápida respuesta. Por ello, en esta Tesis se analizan las causas y se estudian posibles mejoras. El segundo algoritmo es un algoritmo de control de admisión conjunto (JCAC) que admite y rechaza usuarios teniendo en cuenta todas las tecnologías al mismo tiempo. La principal diferencia con otros algorimos propuestos es que éstos últimos toman las dicisiones de admisión en cada tecnología por separado. Por ello, se necesita de algún mecanismo para seleccionar la tecnología a la que los usuarios se van a conectar. Por el contrario, la técnica propuesta en esta Tesis es capaz de tomar decisiones en todo el sistema heterogéneo. Por lo tanto, los usuarios no se enlazan con ninguna tecnología antes de ser admitidos. / Calabuig Soler, D. (2010). Common Radio Resource Management Strategies for Quality of Service Support in Heterogeneous Wireless Networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7348

Network densification has become a dominant theme for capacity enhancement in cellular networks. However, it increases the operational complexity and expenditure for mobile network operators. Consequently, the essential features of Self-Organising Networks (SON) are considered to ensure the economic viability of the emerging cellular networks. This thesis focuses on quantifying the benefits of self-organisation in Cloud Radio Access Network (C-RAN) by proposing a flexible, energy efficient, and capacity optimised system. The Base Band Unit (BBU) and Remote Radio Head (RRH) map is formulated as an optimisation problem. A self-optimised C-RAN (SOCRAN) is proposed which hosts Genetic Algorithm (GA) and Discrete-Particle-Swarm-Optimisation algorithm (DPSO), developed for optimisation. Computational results based on different network scenarios demonstrate that DPSO delivers excellent performances for the key performance indicators compared to GA. The percentage of blocked users is reduced from 10.523% to 0.409% in a medium sized network scenario and 5.394% to 0.56% in a vast network scenario. Furthermore, an efficient resource utilisation scheme is proposed based on the concept of Cell Differentiation and Integration (CDI). The two-stage CDI scheme semi-statically scales the number of BBUs and RRHs to serve an offered load and dynamically defines the optimum BBU-RRH mapping to avoid unbalanced network scenarios. Computational results demonstrate significant throughput improvement in a CDI-enabled C-RAN compared to a fixed C-RAN, i.e., an average throughput increase of 45.53% and an average blocked users decrease of 23.149% is experienced. A power model is proposed to estimate the overall power consumption of C-RAN. Approximately 16% power reduction is calculated in a CDI-enabled C-RAN when compared to a fixed C-RAN, both serving the same geographical area. Moreover, a Divide-and-Sort load balancing scheme is proposed and compared to the SOCRAN scheme. Results show excellent performances by the Divide-and-Sort algorithm in small networks when compared to SOCRAN and K-mean clustering algorithm.

Cloud computing is an emerging trend where clients are billed for services on a pay-per-use basis. Service level agreements define the formal negotiations between the clients and the service providers on common metrics such as processing power, memory and bandwidth. In the case of service level agreement violations, the service provider is penalised. From service provider's point of view, providing cloud services efficiently within the negotiated metrics is an important problem. Particularly, in large-scale data center settings, manual administration for resource allocation is not a feasible option. Service providers aim to maximize resource utilization in the data center, as well as, avoiding service level agreement violations. On the other hand, from the client's point of view, the cloud must continuously ensure enough resources to the changing workloads of hosted application environments and services. Therefore, an autonomous cloud manager that is capable of dynamically allocating resources in order to satisfy both the client and the service provider's requirements emerges as a necessity. In this thesis, we focus on the autonomous resource allocation in cloud computing environments. A distributed resource consolidation manager for clouds, called IMPROMPTU, was introduced in our previous studies. IMPROMPTU adopts a threshold based reactive design where each unique physical machine is coupled with an autonomous node agent that manages resource consolidation independently from the rest of the autonomous node agents. In our previous studies, IMPROMPTU demonstrated the viability of Multiple Criteria Decision Analysis (MCDA) to provide resource consolidation management that simultaneously achieves lower numbers of reconfiguration events and service level agreement violations under the management of three well-known outranking-based methods called PROMETHEE II, ELECTRE III and PAMSSEM II. The interesting question of whether more efficient single synthesizing criterion and outranking based MCDA methods exist was left open for research. This thesis addresses these limitations by analysing the capabilities of IMPROMPTU using a comprehensive set of single synthesizing criterion and outranking based MCDA methods in the context of dynamic resource allocation. The performances of PROMETHEE II, ELECTRE III, PAMSSEM II, REGIME, ORESTE, QUALIFEX, AHP and SMART are investigated by in-depth analysis of simulation results. Most importantly, the question of what denotes the properties of good MCDA methods for this problem domain is answered. / Graduate / 0984