Customer call centers with hundreds of agents working in parallel are ubiquitous in many industries. These systems have a large amount of daily traffic that is stochastic in nature. It becomes more and more difficult to manage a call center because of its increasingly large scale and the stochastic variability in arrival and service processes. In call center operations, customer abandonment is a key factor and may significantly impact the system performance. It must be modeled explicitly in order for an operational model to be relevant for decision making. In this thesis, a large-scale call center is modeled as a queue with many parallel servers. To model the customer abandonment, each customer is assigned a patience time. When his waiting time for service exceeds his patience time, a customer abandons the system without service. We develop analytical and numerical tools for analyzing such a queue. We first study a sequence of G/G/n+GI queues, where the customer patience times are independent and identically distributed (iid) following a general distribution. The focus is the abandonment and the queue length processes. We prove that under certain conditions, a deterministic relationship holds asymptotically in diffusion scaling between these two stochastic processes, as the number of servers goes to infinity. Next, we restrict the service time distribution to be a phase-type distribution with d phases. Using the aforementioned asymptotic relationship, we prove limit theorems for G/Ph/n+GI queues in the quality- and efficiency-driven (QED) regime. In particular, the limit process for the customer number in each phase is a d-dimensional piecewise Ornstein-Uhlenbeck (OU) process. Motivated by the diffusion limit process, we propose two approximate models for a GI/Ph/n+GI queue. In each model, a d-dimensional diffusion process is used to approximate the dynamics of the queue. These two models differ in how the patience time distribution is built into them. The first diffusion model uses the patience time density at zero and the second one uses the entire patience time distribution. We also develop a numerical algorithm to analyze these diffusion models. The algorithm solves the stationary distribution of each model. The computed stationary distribution is used to estimate the queue's performance. A crucial part of this algorithm is to choose an appropriate reference density that controls the convergence of the algorithm. We develop a systematic approach to constructing a reference density. With the proposed reference density, the algorithm is shown to converge quickly in numerical experiments. These experiments also show that the diffusion models are good approximations of queues with a moderate to large number of servers.

Made available in DSpace on 2014-07-29T15:08:15Z (GMT). No. of bitstreams: 1 LeonardoAntonio alves.pdf: 913407 bytes, checksum: 5f344f91c6101d278dcd0a211a753de0 (MD5) Previous issue date: 2010-08-19 / In this work, we present an analytical model to predict the performance of the client-server system regarding an E-Learning platform. This approach is based on the queuing theory and can adequately describe the performance of an E-Learning platform and its parameters, such as the server response time. It is used a Poisson model to describe the traffic processes in the EaD system, comparing the output of the model to an E-Learning platform working at the MSD Education company. In this study, it was developed a precise tool for designing E-Learning platforms based on client-server model according to the increasing access demand. / Neste trabalho, propõe-se um modelo de análise para prever o desempenho de um sistema cliente-servidor referente a uma plataforma de Educação a Distância. Esta abordagem é baseada na teoria de filas e pode descrever adequadamente o desempenho de uma plataforma de Educação a Distância e seus parâmetros, como por exemplo, o tempo médio de resposta do servidor. Utiliza-se o modelo de Poisson para descrever o processo de requisições no sistema EaD, comparando os resultados fornecidos pelo modelo com os de uma plataforma de Educação a Distância mantida pela empresa MSD Educação. Neste estudo, desenvolveu-se uma ferramenta precisa para a concepção e dimensionamento de plataformas de Educação a Distância baseada no modelo cliente-servidor de acordo com a demanda crescente de acesso.

Abstract Medium access control (MAC) in wireless ad hoc networks has received considerable attention for almost a couple of decades; however, there are still open problems which deserve thorough study in order to facilitate migration to the next generation broadband wireless communication systems. In ad hoc networks, a detected frame collision can be due to the so-called unreachability problem, where the destination station is situated either in the transmission or interference range of an emitting station and is unable to receive connection establishment frames from any of its neighboring stations. Unreachability might also be due to the inability of a radio station to respond to any connection establishment request, though when the unreachable station receives the connection establishment requests, however, it is prohibited from responding to the requests due to being situated in the interference range of the emitting neighbor. To investigate the impact of this problem, we have to be equipped with a proper analytical framework; therefore, as the first part of this thesis, a scalable framework called Parallel Space – Time Markov chain (PSTMC) is proposed, through which a finite load non-saturated ad hoc network can be easily modeled. At the first step, a single-hop ad hoc network is considered and the accuracy of the model is evaluated using extensive numerical results. Subsequently, the proposed framework is further extended to model multi-hop ad hoc networks. Several discussions are also given on how the framework can be deployed for an arbitrary network topology. One of the main key features of the PSTMC model is its remarkable scalability in modeling complex network configurations. In fact, it is shown that multi-hop ad hoc networks have bounded complexity in being modeled by the PSTMC framework due to its spectacular specifications. These features lead us to a powerful tool by which an arbitrary network topology can be studied. In addition, the proposed models clearly facilitate demonstrating the impact of the unreachability problem on the performance of multi-hop networks. The introduced framework shows how the unreachability problem degrades the achieved throughput and channel capacity by the contending radio stations depending on the deployed network topology. In the remainder of the thesis the unreachability problem in mobile ad hoc networks is tackled and a new MAC protocol to enhance the performance of the network is proposed. This MAC scheme is equipped with smart decision-making algorithms as well as adaptive management mechanisms to reduce the impact of the unreachability problem in single channel scenarios. Subsequently, the problem of concurrent radio resource management and contention resolution in multi-channel cognitive ad hoc networks is considered. In particular, a multi-channel technique for traffic distribution among a set of data channels without centralized control, which is enabled by a probabilistic channel selection algorithm as well as a multi-channel binary exponential backoff mechanism, is proposed. It is shown through simulations that the suggested scheme outperforms the existing MAC protocols in multi-channel environments as well as cognitive networks coexisting with primary users. A mathematical model is also introduced to study the performance of the multi-channel MAC protocol in a single-hop non-saturated wireless network.

Production parallel systems are space-shared and employ batch queues in which the jobs submitted to the systems are made to wait before execution. Thus, jobs submitted to parallel batch systems incur queue waiting times in addition to the execution times. Prediction of these queue waiting times is important to provide overall estimates to the users and can also help meta-schedulers make scheduling decisions. In the first part of our research, we have developed an integrated framework PQStar for identification and prediction of jobs with short queue waiting times. Analyses of the job traces of supercomputers reveal that about 56 to 99% of the jobs incur queue waiting times of less than an hour. Hence, identifying these quick starters or jobs with short queue waiting times is Essential for overall improvement on queue waiting time predictions. An important aspect of our prediction strategy for quick starters is that it considers the processor occupancy state and the queue state at the time of the job submission in addition to the job characteristics including the requested number of processors and the estimated runtime. Our experiments with different Production supercomputer job traces show that our prediction strategies can lead to correct identification of about 20% more quick starters on an average and provide tighter bounds for these jobs, and result in about 24% higher overall prediction accuracy on an average than the next best existing method. We have also developed a framework for predicting ranges of queue waiting times for other classes of jobs by employing multi-class classification on similar jobs in history. Our hierarchical prediction strategy first predicts the point wait time of a job using dynamic k- Nearest Neighbor (kNN) method. It then performs a multi-class classification using Support Vector Machines (SVMs) among all the classes of the jobs. The probabilities given by the SVM for the predicted class (obtained from the kNN), along with its neighboring classes, are used to provide a set of ranges of wait times with probabilities. Our experiments with different production supercomputer job traces show that our prediction strategies can lead to about 8% improved accuracy on an average in prediction of the non-quick starters, compared to the next best existing method. Finally, we have used these predictions and probabilities in a meta-scheduling strategy that distributes jobs to different queues/sites in a multi-queue/grid environment for minimizing wait times of the jobs. For a given target job, we first identify the queues/sites where the job can be a quick starter to get a set of candidate queues/sites for the scheduling of the job. We then compute the expected value of the predicted wait time in each of the candidate queues/sites, and schedule the job to the one with minimum expected value, for the execution of the job. We have performed experiments with different production supercomputer job traces and synthetic traces for various system sizes, partitioning schemes and different workloads. These experiments have shown that our scheduling strategy gives much improved performance when compared to the existing scheduling policies by reducing the overall average queue waiting times of the jobs by about 47% on an average.

In this thesis, we consider improving supply chain and service systems through demand-side management. In Chapters 1 and 2, we focus on a new notion of flexibility that has emerged in e-commerce called consumer flexibility. Motivated by the fact that some customers may willingly provide flexibility on which product or service they receive in exchange for a reward, firms can design flexible options to leverage this consumer flexibility for significant benefit in their operations. In Chapter 1, we consider the context of online retailing where consumer flexibility can be realized through opaque selling, where some specific attributes of the products are not revealed to the customer until after purchase. In Chapter 2, we focus on the context of online booking systems for scheduled services where consumer flexibility can be realized through large time windows. The main findings are on the power of limited flexibility using simple flexible options with just a small fraction of customers willing to be flexible. In Chapter 3, we study the issue of congested elevator queuing systems due to the requirement of social distancing during a pandemic. We propose simple interventions for safely managing the elevator queues, which require no programming of the elevator system and only manage passenger behaviors. The key idea is to explicitly or implicitly group passengers going to the same or nearby floor into the same elevator as much as possible. Simulations and stability analysis show that our proposed interventions significantly reduce queue length and wait time.

No description available.

No / Cognitive Radio (CR) is an emerging technology used to significantly improve the efficiency of spectrum utilization. Although some spectrum bands in the primary user's licensed spectrum are intensively used, most of the spectrum bands remain underutilized. The introduction of open spectrum and dynamic spectrum access lets the secondary (unlicensed) users, supported by cognitive radios; opportunistically utilize the unused spectrum bands. However, if a primary user returns to a band occupied by a secondary user, the occupied spectrum band is vacated immediately by handing off the secondary user's call to another idle spectrum band. Multiple spectrum handoffs can severely degrade quality of service (QoS) for the interrupted users. To avoid multiple handoffs, when a licensed primary user appears at the engaged licensed band utilized by a secondary user, an effective spectrum handoff procedure should be initiated to maintain a required level of QoS for secondary users. In other words, it enables the channel clearing while searching for target vacant channel(s) for completing unfinished transmission. This paper proposes prioritized proactive spectrum handoff decision schemes to reduce the handoff delay and the total service time. The proposed schemes have been modeled using a preemptive resume priority (PRP) M/G/1 queue to give a high priority to interrupted users to resume their transmission ahead of any other uninterrupted secondary user. The performance of proposed handoff schemes has been evaluated and compared against the existing spectrum handoff schemes. Experimental results show that the schemes developed here outperform the existing schemes in terms of average handoff delay and total service time under various traffic arrival rates as well as service rates.

Ce manuscrit est centré sur la conception, l'amélioration et l'évaluation des protocoles des couches RESEAU, MAC et PHY. En particulier, nous nous focalisons sur la conception de nouveaux protocoles distribués pour une utilisation optimale/améliorée des ressources radio disponibles. Par ailleurs, nous caractérisons les performances des réseaux ad hoc à accès aléatoire au canal en utilisant des paramètres de plusieurs couches avec aptitude de transfert d'information (data forwarding). La majeure partie de nos analyses se base sur le concept d'interaction entre les couches OSI (cross-layer). En effet, cette nouvelle et attractive approche est devenue en peu de temps omniprésente dans le domaine de recherche et développement et dans le domaine industriel. Les métriques de performances qui nous intéressent sont la stabilité des files d'attentes de transfert, le débit, le délai et la consommation d'énergie. Principalement, la compréhension de l'interaction entre les couches MAC/PHY et routage du standard IEEE 802.11e DCF/EDCF, d'une part, et l'interaction entre noeuds en terme d'interférences, d'autre part, constituent le coeur central de notre travail / The present dissertation deals with the problem of under-utilization of collision channels and other related problems in wireless networks. It deals with the design of random access protocols for wireless systems and provides a mathematical framework for performance evaluation of multihop based heterogeneous wireless networks. This thesis is divided into three parts. In the first part, we propose new versions of slotted aloha incorporating power control, priority and hierarchy. Our simulations were important to understand the behaviour of such a system and the real impact of involved parameters (transmit power, transmit rate, arrival rate, hierarchy order). Both team problem (common objective function is maximized) and game problem (each user maximizes its own objective) were discussed. Introducing hierarchy seems to provide many promising improvement without/or with a low amount of external information. We also proposed two distributed algorithms to learn the desired throughput. Next, we developed in the second part an analytical Framework to evaluate performances of multihop based heterogeneous Wireless networks. We built a cross-layer model and derived expression of stability, end-to-end throughput and end-to-end delay. Furthermore, we provided an accurate approximation for the distribution of end-to-end delay in multihop ad hoc networks (operating with slotted aloha protocol). As a direct application, we highlighted how streaming and conversational flows could be supported in this class of ubiquitous networks. The third part of this thesis is devoted to understanding and modelling of IEEE 802.11e DCF/EDCF-operated multihop ad hoc networks. We indeed built a complete and simple APPLICATION/NETWORK/MAC/PHY cross-layered model with finite retries per packet per flow. We analyzed the stability of forwarding queues and derived expression of end-to-end throughput. We finally proposed a Fountain code-based MAC layer to improve the throughput/fairness over the network

The pervasive availability of streaming data from various sources is driving todays’ enterprises to acquire low-latency big data streaming applications (BDSAs) for extracting useful information. In parallel, recent advances in technology have made it easier to collect, process and store these data streams in the cloud. For most enterprises, gaining insights from big data is immensely important for maintaining competitive advantage. However, majority of enterprises have difficulty managing the multitude of BDSAs and the complex issues cloud technologies present, giving rise to the incorporation of cloud service brokers (CSBs). Generally, the main objective of the CSB is to maintain the heterogeneous quality of service (QoS) of BDSAs while minimizing costs. To achieve this goal, the cloud, although with many desirable features, exhibits major challenges — resource prediction and resource allocation — for CSBs. First, most stream processing systems allocate a fixed amount of resources at runtime, which can lead to under- or over-provisioning as BDSA demands vary over time. Thus, obtaining optimal trade-off between QoS violation and cost requires accurate demand prediction methodology to prevent waste, degradation or shutdown of processing. Second, coordinating resource allocation and pricing decisions for self-interested BDSAs to achieve fairness and efficiency can be complex. This complexity is exacerbated with the recent introduction of containers. This dissertation addresses the cloud resource elasticity management issues for CSBs as follows: First, we provide two contributions to the resource prediction challenge; we propose a novel layered multi-dimensional hidden Markov model (LMD-HMM) framework for managing time-bounded BDSAs and a layered multi-dimensional hidden semi-Markov model (LMD-HSMM) to address unbounded BDSAs. Second, we present a container resource allocation mechanism (CRAM) for optimal workload distribution to meet the real-time demands of competing containerized BDSAs. We formulate the problem as an n-player non-cooperative game among a set of heterogeneous containerized BDSAs. Finally, we incorporate a dynamic incentive-compatible pricing scheme that coordinates the decisions of self-interested BDSAs to maximize the CSB’s surplus. Experimental results demonstrate the effectiveness of our approaches.

Made available in DSpace on 2016-04-04T18:31:22Z (GMT). No. of bitstreams: 1 Andrey Guedes Oliveira.pdf: 4354812 bytes, checksum: 17825c1f558c7ff18c82b21e9935d9fb (MD5) Previous issue date: 2008-02-14 / This work aims to describe the measurement the operational indicators in services attending and support problems realized by Network Operation Center, as well as, to establish attendance metrics directed towards contract maintenance, as well as, recommending the use of the simulations in the Operational Plans of the Service Information Technology Providers. The use of the Theory of Lines, eTOM (enhanced Telecom Operations Map) and ITIL (Information Technology Infrastructure Library) assisted in the analysis of the behavior of Network Operation Center. Had been used a real data from a Network Operation Center of the Datacenter, that it pertaining to a Services Telecommunications Provider. However, it is observed that the Theory of Lines and the simulations need a maximum value of attendance to be applied to the historical data of an operational team. The simulation by this attendance parameter allowed us to map the capacity of the Datacenter team with eight analysts, within acceptable values to fulfill Service Level Agreements up to 92% precision to support problems, 85% to analyze alarm tickets and 89% of utilization in a services attendance. The others simulations had projected the behavior of the team with two new scenes: ten and twelve analysts. The use of the computational simulations can be analyzed and be compared the simulated real data and with attendance projections, making possible an operational planning adjusted the new based business-oriented modalities in convergent networks. The computer simulations using maximum parameter, allowed a analyzing of services types behavior and establishing an operational planning. / Este trabalho tem por objetivo dimensionar recursos humanos nos Centros de Opera??es de Redes e estabelecer indicadores de atendimento voltados para a manuten??o dos contratos e a melhoria dos servi?os prestados, bem como utilizar simula??es computacionais no planejamento operacional para o Centro de Opera??es de Redes. As an?lises de atendimentos deste Centro de Opera??es foram realizadas por meio das melhores pr?ticas em gest?o de Tecnologia da Informa??o - ITIL (Information Technology Infrastructure Library), pelo mapa de processos de Telecomunica??es - eTOM (enhanced Telecom Operations Map) e pela Teoria das Filas. Para isto, foram utilizados dados hist?ricos e reais de um Centro de Opera??es de Rede de um Datacenter, pertencente a um Provedor de Servi?os de Tecnologia da Informa??o e Comunica??o. Foram analisados os dados de atendimentos via Teoria das Filas e comparado com os dados simulados. Notouse a necessidade de um valor m?ximo de atendimento para o dimensionamento da equipe, e esse par?metro serviu de base em novas simula??es que possibilitaram uma an?lise comparativa com proje??es de atendimento. A capacidade do grupo com os patamares aceit?veis, ao cumprimento dos Acordos de N?veis de Servi?os com oito analistas, ocorreu dentro de uma precis?o m?dia de 92% para solu??es a problemas, 85% para suporte a alarmes nos equipamentos e 89% de utiliza??o nas presta??es de servi?os. As demais simula??es projetaram o comportamento da equipe com dois novos cen?rios: dez e doze analistas. Os resultados propiciaram o estabelecimento de um planejamento operacional com os principais indicadores de atendimento e informa??es relevantes para a tomada de decis?es empresariais.