Wireless resources are scarce, shared and time-varying making resource allocation mechanisms, e.g., scheduling, a key and challenging element of wireless system design. In designing good schedulers, we consider three types of performance metrics: system capacity, quality of service (QoS) seen by users, and the energy expenditures (battery lifetimes) incurred by mobile terminals. In this dissertation we investigate the impact of scheduling policies on these performance metrics, their interactions, and/or tradeoffs, and we specifically focus on flow-level performance under stochastic traffic loads. In the first part of the dissertation we evaluate interactions among flow-level performance metrics when integrating QoS and best effort flows in a wireless system using opportunistic scheduling. We introduce a simple flow-level model capturing the salient features of bandwidth sharing for an opportunistic scheduler which ensures a mean throughput to each QoS stream on every time slot. We show that the integration of QoS and best effort flows results in a loss of opportunism, which in turn results in a reduction of the stability region, degradation in system capacity, and increased file transfer delay. In the second part of the dissertation we study several ways in which mobile terminals can backoff on their uplink transmit power (thus slow down their transmissions) in order to extend battery lifetimes. This is particularly effective when a wireless system is underloaded, so the degradation in the users' perceived performance can be negligible. The challenge, however, is developing a mechanism that achieves a good tradeoff among transmit power, idling/circuit power, and the performance customers will see. We consider systems with flow-level dynamics supporting either real-time or best effort (e.g., file transfers) sessions. We show that significant energy savings can be achieved by leveraging dynamic spare capacity. We then extend our study to the case where mobile terminals have multiple transmit antennas. In the third part of the dissertation we develop a framework for user association in infrastructure-based wireless networks, specifically focused on adaptively balancing flow loads given spatially inhomogeneous traffic distributions. Our work encompasses several possible user association objective functions resulting in rate-optimal, throughput-optimal, delay-optimal, and load-equalizing policy, which we collectively denote [alpha]-optimal user association. We prove that the optimal load vector that minimizes this function is the fixed point of a certain mapping. Based on this mapping we propose an iterative distributed user association policy and prove that it converges to the globally optimal decision in steady state. In addition we address admission control policies for the case where the system cannot be stabilized. / text

Immersive applications, such as computer gaming, computer vision and video codecs, are an important emerging class of applications with QoS requirements that are difficult to characterize and control using traditional methods. This thesis proposes new techniques reliant on execution-time variance to both characterize and control program behavior. The proposed techniques are intended to be broadly applicable to a wide variety of immersive applications and are intended to be easy for programmers to apply without needing to gain specialized expertise. First, we create new QoS controllers that programmers can easily apply to their applications to achieve desired application-specific QoS objectives on any platform or application data-set, provided the programmers verify that their applications satisfy some simple domain requirements specific to immersive applications. The controllers adjust programmer-identified knobs every application frame to effect desired values for programmer-identified QoS metrics. The control techniques are novel in that they do not require the user to provide any kind of application behavior models, and are effective for immersive applications that defy the traditional requirements for feedback controller construction. Second, we create new profiling techniques that provide visibility into the behavior of a large complex application, inferring behavior relationships across application components based on the execution-time variance observed at all levels of granularity of the application functionality. Additionally for immersive applications, some of the most important QoS requirements relate to managing the execution-time variance of key application components, for example, the frame-rate. The profiling techniques not only identify and summarize behavior directly relevant to the QoS aspects related to timing, but also indirectly reveal non-timing related properties of behavior, such as the identification of components that are sensitive to data, or those whose behavior changes based on the call-context.

In ant societies, and, more in general, in insect societies, the activities of the individuals, as well as of the society as a whole, are not regulated by any explicit form of centralized control. On the other hand, adaptive and robust behaviors transcending the behavioral repertoire of the single individual can be easily observed at society level. These complex global behaviors are the result of self-organizing dynamics driven by local interactions and communications among a number of relatively simple individuals. The simultaneous presence of these and other fascinating and unique characteristics have made ant societies an attractive and inspiring model for building new algorithms and new multi-agent systems. In the last decade, ant societies have been taken as a reference for an ever growing body of scientific work, mostly in the fields of robotics, operations research, and telecommunications. Among the different works inspired by ant colonies, the Ant Colony Optimization metaheuristic (ACO) is probably the most successful and popular one. The ACO metaheuristic is a multi-agent framework for combinatorial optimization whose main components are: a set of ant-like agents, the use of memory and of stochastic decisions, and strategies of collective and distributed learning. It finds its roots in the experimental observation of a specific foraging behavior of some ant colonies that, under appropriate conditions, are able to select the shortest path among few possible paths connecting their nest to a food site. The pheromone, a volatile chemical substance laid on the ground by the ants while walking and affecting in turn their moving decisions according to its local intensity, is the mediator of this behavior. All the elements playing an essential role in the ant colony foraging behavior were understood, thoroughly reverse-engineered and put to work to solve problems of combinatorial optimization by Marco Dorigo and his co-workers at the beginning of the 1990's. From that moment on it has been a flourishing of new combinatorial optimization algorithms designed after the first algorithms of Dorigo's et al., and of related scientific events. In 1999 the ACO metaheuristic was defined by Dorigo, Di Caro and Gambardella with the purpose of providing a common framework for describing and analyzing all these algorithms inspired by the same ant colony behavior and by the same common process of reverse-engineering of this behavior. Therefore, the ACO metaheuristic was defined a posteriori, as the result of a synthesis effort effectuated on the study of the characteristics of all these ant-inspired algorithms and on the abstraction of their common traits. The ACO's synthesis was also motivated by the usually good performance shown by the algorithms (e.g., for several important combinatorial problems like the quadratic assignment, vehicle routing and job shop scheduling, ACO implementations have outperformed state-of-the-art algorithms). The definition and study of the ACO metaheuristic is one of the two fundamental goals of the thesis. The other one, strictly related to this former one, consists in the design, implementation, and testing of ACO instances for problems of adaptive routing in telecommunication networks. This thesis is an in-depth journey through the ACO metaheuristic, during which we have (re)defined ACO and tried to get a clear understanding of its potentialities, limits, and relationships with other frameworks and with its biological background. The thesis takes into account all the developments that have followed the original 1999's definition, and provides a formal and comprehensive systematization of the subject, as well as an up-to-date and quite comprehensive review of current applications. We have also identified in dynamic problems in telecommunication networks the most appropriate domain of application for the ACO ideas. According to this understanding, in the most applicative part of the thesis we have focused on problems of adaptive routing in networks and we have developed and tested four new algorithms. Adopting an original point of view with respect to the way ACO was firstly defined (but maintaining full conceptual and terminological consistency), ACO is here defined and mainly discussed in the terms of sequential decision processes and Monte Carlo sampling and learning. More precisely, ACO is characterized as a policy search strategy aimed at learning the distributed parameters (called pheromone variables in accordance with the biological metaphor) of the stochastic decision policy which is used by so-called ant agents to generate solutions. Each ant represents in practice an independent sequential decision process aimed at constructing a possibly feasible solution for the optimization problem at hand by using only information local to the decision step. Ants are repeatedly and concurrently generated in order to sample the solution set according to the current policy. The outcomes of the generated solutions are used to partially evaluate the current policy, spot the most promising search areas, and update the policy parameters in order to possibly focus the search in those promising areas while keeping a satisfactory level of overall exploration. This way of looking at ACO has facilitated to disclose the strict relationships between ACO and other well-known frameworks, like dynamic programming, Markov and non-Markov decision processes, and reinforcement learning. In turn, this has favored reasoning on the general properties of ACO in terms of amount of complete state information which is used by the ACO's ants to take optimized decisions and to encode in pheromone variables memory of both the decisions that belonged to the sampled solutions and their quality. The ACO's biological context of inspiration is fully acknowledged in the thesis. We report with extensive discussions on the shortest path behaviors of ant colonies and on the identification and analysis of the few nonlinear dynamics that are at the very core of self-organized behaviors in both the ants and other societal organizations. We discuss these dynamics in the general framework of stigmergic modeling, based on asynchronous environment-mediated communication protocols, and (pheromone) variables priming coordinated responses of a number of ``cheap' and concurrent agents. The second half of the thesis is devoted to the study of the application of ACO to problems of online routing in telecommunication networks. This class of problems has been identified in the thesis as the most appropriate for the application of the multi-agent, distributed, and adaptive nature of the ACO architecture. Four novel ACO algorithms for problems of adaptive routing in telecommunication networks are throughly described. The four algorithms cover a wide spectrum of possible types of network: two of them deliver best-effort traffic in wired IP networks, one is intended for quality-of-service (QoS) traffic in ATM networks, and the fourth is for best-effort traffic in mobile ad hoc networks. The two algorithms for wired IP networks have been extensively tested by simulation studies and compared to state-of-the-art algorithms for a wide set of reference scenarios. The algorithm for mobile ad hoc networks is still under development, but quite extensive results and comparisons with a popular state-of-the-art algorithm are reported. No results are reported for the algorithm for QoS, which has not been fully tested. The observed experimental performance is excellent, especially for the case of wired IP networks: our algorithms always perform comparably or much better than the state-of-the-art competitors. In the thesis we try to understand the rationale behind the brilliant performance obtained and the good level of popularity reached by our algorithms. More in general, we discuss the reasons of the general efficacy of the ACO approach for network routing problems compared to the characteristics of more classical approaches. Moving further, we also informally define Ant Colony Routing (ACR), a multi-agent framework explicitly integrating learning components into the ACO's design in order to define a general and in a sense futuristic architecture for autonomic network control. Most of the material of the thesis comes from a re-elaboration of material co-authored and published in a number of books, journal papers, conference proceedings, and technical reports. The detailed list of references is provided in the Introduction.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-07-31T13:58:16Z No. of bitstreams: 1 PAULO DITARSO MACIEL JÚNIOR - TESE PPGCC 2013..pdf: 21161997 bytes, checksum: 33b051924023dbbac092de80229a7705 (MD5) / Made available in DSpace on 2018-07-31T13:58:16Z (GMT). No. of bitstreams: 1 PAULO DITARSO MACIEL JÚNIOR - TESE PPGCC 2013..pdf: 21161997 bytes, checksum: 33b051924023dbbac092de80229a7705 (MD5) Previous issue date: 2013-06-14 / CNPq / Capes / Com o surgimento do paradigma de computação na nuvem e a busca contínua para reduzir o custo de operar infraestruturas de Tecnologia da Informação (TI), estamos vivenciando nos dias de hoje uma importante mudança na forma como estas infraestruturas estão sendo montadas, configuradas e gerenciadas. Nesta pesquisa consideramos o problema de gerenciar uma infraestrutura híbrida, cujo poder computacional é formado por máquinas locais dedicadas, máquinas virtuais obtidas de provedores de computação na nuvem e máquinas virtuais remotas disponíveis a partir de uma grade peer-to-peer (P2P) best-effort. As aplicações executadas nesta infraestrutura são caracterizadas por uma função de utilidade no tempo, ou seja, a utilidade produzida pela execução completa da aplicação depende do tempo total necessário para sua finalização. Tomamos uma abordagem dirigida a negócios para gerenciar esta infraestrutura, buscando maximizar o lucro total obtido. Aplicações são executadas utilizando poder computacional local e da grade best-effort, quando possível. Qualquer capacidade extra requerida no intuito de melhorar a lucratividade da infraestrutura é adquirida no mercado de computação na nuvem. Também assumimos que esta capacidade extra pode ser reservada para uso futuro através de contratos de curta ou longa duração, negociados sem intervenção humana. Para contratos de curto prazo, o custo por unidade de recurso computacional pode variar significativamente entre contratos, com contratos mais urgentes apresentando, geralmente, custos mais caros. Além disso, devido à incerteza inerente à grade best-effort, podemos não saber exatamente quantos recursos serão necessários do mercado de computação na nuvem com certa antecedência. Superestimar a quantidade de recursos necessários leva a uma reserva maior do que necessária; enquanto subestimar leva à necessidade de negociar contratos adicionais posteriormente. Neste contexto, propomos heurísticas que podem ser usadas por agentes planejadores de contratos no intuito de balancear o custo e a utilidade obtida na execução das aplicações, com o objetivo de alcançar um alto lucro global. Demonstramos que a habilidade de estimar o comportamento da grade é uma importante condição para estabelecer contratos que produzem alta eficiência no uso da infraestrutura híbrida de TI. / With the emergence of the cloud computing paradigm and the continuous search to reduce the cost of running Information Technology (IT) infrastructures, we are currently experiencing an importam change in the way these infrastructures are assembled, configured and managed. In this research we consider the problem of managing a hybrid high-performance computing infrastructure whose processing elements are comprised of in-house dedicated machines, virtual machines acquired from cloud computing providers, and remote virtual machines made available by a best-effort peer-to-peer (P2P) grid. The applications that run in this hybrid infrastructure are characterised by a utility function: the utility yielded by the completion of an application depends on the time taken to execute it. We take a business-driven approach to manage this infrastructure, aiming at maximising the total profit achieved. Applications are run using computing power from both in-house resources and the best-effort grid. whenever possible. Any extra capacity required to improve the profitability of the infrastructure is purchased from the cloud computing market. We also assume that this extra capacity is reserved for future use through either short or long term contracts, which are negotiated without human intervention. For short term contracts. the cost per unit of computing resource may vary significantly between contracts, with more urgent contracts normally being more expensive. Furthermore, due to the uncertainty inherent in the besteffort grid, it may not be possible to know in advance exactly how much computing resource will be needed from the cloud computing market. Overestimation of the amount of resources required leads to the reservation of more than is necessary; while underestimation leads to the necessity of negotiating additional contracts later on to acquire the remaining required capacity. In this context, we propose heuristics to be used by a contract planning agent in order to balance the cost of running the applications and the utility that is achieved with their execution. with the aim of producing a high overall profit. We demonstrate that the ability to estimate the grid behaviour is an important condition for making contracts that produce high efficiency in the use of the hybrid IT infrastructure.

In ant societies, and, more in general, in insect societies, the activities of the individuals, as well as of the society as a whole, are not regulated by any explicit form of centralized control. On the other hand, adaptive and robust behaviors transcending the behavioral repertoire of the single individual can be easily observed at society level. These complex global behaviors are the result of self-organizing dynamics driven by local interactions and communications among a number of relatively simple individuals.<p><p>The simultaneous presence of these and other fascinating and unique characteristics have made ant societies an attractive and inspiring model for building new algorithms and new multi-agent systems. In the last decade, ant societies have been taken as a reference for an ever growing body of scientific work, mostly in the fields of robotics, operations research, and telecommunications.<p><p>Among the different works inspired by ant colonies, the Ant Colony Optimization metaheuristic (ACO) is probably the most successful and popular one. The ACO metaheuristic is a multi-agent framework for combinatorial optimization whose main components are: a set of ant-like agents, the use of memory and of stochastic decisions, and strategies of collective and distributed learning.<p><p>It finds its roots in the experimental observation of a specific foraging behavior of some ant colonies that, under appropriate conditions, are able to select the shortest path among few possible paths connecting their nest to a food site. The pheromone, a volatile chemical substance laid on the ground by the ants while walking and affecting in turn their moving decisions according to its local intensity, is the mediator of this behavior.<p><p>All the elements playing an essential role in the ant colony foraging behavior were understood, thoroughly reverse-engineered and put to work to solve problems of combinatorial optimization by Marco Dorigo and his co-workers at the beginning of the 1990's.<p><p>From that moment on it has been a flourishing of new combinatorial optimization algorithms designed after the first algorithms of Dorigo's et al. and of related scientific events.<p><p>In 1999 the ACO metaheuristic was defined by Dorigo, Di Caro and Gambardella with the purpose of providing a common framework for describing and analyzing all these algorithms inspired by the same ant colony behavior and by the same common process of reverse-engineering of this behavior. Therefore, the ACO metaheuristic was defined a posteriori, as the result of a synthesis effort effectuated on the study of the characteristics of all these ant-inspired algorithms and on the abstraction of their common traits.<p><p>The ACO's synthesis was also motivated by the usually good performance shown by the algorithms (e.g. for several important combinatorial problems like the quadratic assignment, vehicle routing and job shop scheduling, ACO implementations have outperformed state-of-the-art algorithms).<p><p>The definition and study of the ACO metaheuristic is one of the two fundamental goals of the thesis. The other one, strictly related to this former one, consists in the design, implementation, and testing of ACO instances for problems of adaptive routing in telecommunication networks.<p><p>This thesis is an in-depth journey through the ACO metaheuristic, during which we have (re)defined ACO and tried to get a clear understanding of its potentialities, limits, and relationships with other frameworks and with its biological background. The thesis takes into account all the developments that have followed the original 1999's definition, and provides a formal and comprehensive systematization of the subject, as well as an up-to-date and quite comprehensive review of current applications. We have also identified in dynamic problems in telecommunication networks the most appropriate domain of application for the ACO ideas. According to this understanding, in the most applicative part of the thesis we have focused on problems of adaptive routing in networks and we have developed and tested four new algorithms.<p><p>Adopting an original point of view with respect to the way ACO was firstly defined (but maintaining full conceptual and terminological consistency), ACO is here defined and mainly discussed in the terms of sequential decision processes and Monte Carlo sampling and learning.<p><p>More precisely, ACO is characterized as a policy search strategy aimed at learning the distributed parameters (called pheromone variables in accordance with the biological metaphor) of the stochastic decision policy which is used by so-called ant agents to generate solutions. Each ant represents in practice an independent sequential decision process aimed at constructing a possibly feasible solution for the optimization problem at hand by using only information local to the decision step.<p>Ants are repeatedly and concurrently generated in order to sample the solution set according to the current policy. The outcomes of the generated solutions are used to partially evaluate the current policy, spot the most promising search areas, and update the policy parameters in order to possibly focus the search in those promising areas while keeping a satisfactory level of overall exploration.<p><p>This way of looking at ACO has facilitated to disclose the strict relationships between ACO and other well-known frameworks, like dynamic programming, Markov and non-Markov decision processes, and reinforcement learning. In turn, this has favored reasoning on the general properties of ACO in terms of amount of complete state information which is used by the ACO's ants to take optimized decisions and to encode in pheromone variables memory of both the decisions that belonged to the sampled solutions and their quality.<p><p>The ACO's biological context of inspiration is fully acknowledged in the thesis. We report with extensive discussions on the shortest path behaviors of ant colonies and on the identification and analysis of the few nonlinear dynamics that are at the very core of self-organized behaviors in both the ants and other societal organizations. We discuss these dynamics in the general framework of stigmergic modeling, based on asynchronous environment-mediated communication protocols, and (pheromone) variables priming coordinated responses of a number of ``cheap' and concurrent agents.<p><p>The second half of the thesis is devoted to the study of the application of ACO to problems of online routing in telecommunication networks. This class of problems has been identified in the thesis as the most appropriate for the application of the multi-agent, distributed, and adaptive nature of the ACO architecture.<p><p>Four novel ACO algorithms for problems of adaptive routing in telecommunication networks are throughly described. The four algorithms cover a wide spectrum of possible types of network: two of them deliver best-effort traffic in wired IP networks, one is intended for quality-of-service (QoS) traffic in ATM networks, and the fourth is for best-effort traffic in mobile ad hoc networks.<p><p>The two algorithms for wired IP networks have been extensively tested by simulation studies and compared to state-of-the-art algorithms for a wide set of reference scenarios. The algorithm for mobile ad hoc networks is still under development, but quite extensive results and comparisons with a popular state-of-the-art algorithm are reported. No results are reported for the algorithm for QoS, which has not been fully tested. The observed experimental performance is excellent, especially for the case of wired IP networks: our algorithms always perform comparably or much better than the state-of-the-art competitors.<p><p>In the thesis we try to understand the rationale behind the brilliant performance obtained and the good level of popularity reached by our algorithms. More in general, we discuss the reasons of the general efficacy of the ACO approach for network routing problems compared to the characteristics of more classical approaches. Moving further, we also informally define Ant Colony Routing (ACR), a multi-agent framework explicitly integrating learning components into the ACO's design in order to define a general and in a sense futuristic architecture for autonomic network control.<p><p>Most of the material of the thesis comes from a re-elaboration of material co-authored and published in a number of books, journal papers, conference proceedings, and technical reports. The detailed list of references is provided in the Introduction.<p><p><p> / Doctorat en sciences appliquées / info:eu-repo/semantics/nonPublished

This term project deals with the problem of scheduling real-time tasks in overload conditions and techniques for lowering power consumption. Each of these parts features mechanisms and reasons for their using. There are also described specific algorithms, that are implemented, in operating system uC/OS-II, and compared in next phase of master's thesis.

政府在1990年開放新銀行申請設立、1991年共核准通過15家新銀行籌設，國內銀行自此即進入百家爭鳴的時代，各家銀行惡性競爭的結果，除了利差不斷降低外，授信品質亦不斷惡化，各銀行資本報酬率直直落，為求生存，遂絞盡腦汁不斷推出各種新型商品以促進客戶擴大信用；聯合貸款業務亦不例外，各銀行有鑑於傳統企業授信漸不能達到應有績效之際，紛紛著重於聯貸業務，於是業務競爭日趨白熱化，如何設計符合貸款企業期望的授信方案，是各銀行贏得聯合授信主辦權的重要課題。 因此，企業如何選擇聯合貸款主辦行之影響因素，是各銀行放款經理人亟盼了解的經營知識，而根據這些關鍵影響因素，各銀行為爭取主辦權所擬定的各項經營模式與策略，即是足以決定聯合貸款業務在各銀行未來之發展情形與定位，甚而決定其在聯貸市場所扮演之角色與市場佔有率。 有鑑於此，本研究主要以銀行端的角色來探討各銀行針對聯合貸款業務所為之相關措施及其後續之影響結果，並針對國內主要聯貸銀行做個案分析，以了解各銀行所採取的策略及經營方針。