Supply Chain

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications

Effective Resource and Workload Management in Data Centers

The increasing demand for storage, computation, and business continuity has driven the growth of data centers. Managing data centers efficiently is a difficult task because of the wide variety of datacenter applications, their ever-changing intensities, and the fact that application performance targets may differ widely. Server virtualization has been a game-changing technology for IT, providing the possibility to support multiple virtual machines (VMs) simultaneously. This dissertation focuses on how virtualization technologies can be utilized to develop new tools for maintaining high resource utilization, for achieving high application performance, and for reducing the cost of data center management.;For multi-tiered applications, bursty workload traffic can significantly deteriorate performance. This dissertation proposes an admission control algorithm AWAIT, for handling overloading conditions in multi-tier web services. AWAIT places on hold requests of accepted sessions and refuses to admit new sessions when the system is in a sudden workload surge. to meet the service-level objective, AWAIT serves the requests in the blocking queue with high priority. The size of the queue is dynamically determined according to the workload burstiness.;Many admission control policies are triggered by instantaneous measurements of system resource usage, e.g., CPU utilization. This dissertation first demonstrates that directly measuring virtual machine resource utilizations with standard tools cannot always lead to accurate estimates. A directed factor graph (DFG) model is defined to model the dependencies among multiple types of resources across physical and virtual layers.;Virtualized data centers always enable sharing of resources among hosted applications for achieving high resource utilization. However, it is difficult to satisfy application SLOs on a shared infrastructure, as application workloads patterns change over time. AppRM, an automated management system not only allocates right amount of resources to applications for their performance target but also adjusts to dynamic workloads using an adaptive model.;Server consolidation is one of the key applications of server virtualization. This dissertation proposes a VM consolidation mechanism, first by extending the fair load balancing scheme for multi-dimensional vector scheduling, and then by using a queueing network model to capture the service contentions for a particular virtual machine placement

Investigations into Elasticity in Cloud Computing

The pay-as-you-go model supported by existing cloud infrastructure providers is appealing to most application service providers to deliver their applications in the cloud. Within this context, elasticity of applications has become one of the most important features in cloud computing. This elasticity enables real-time acquisition/release of compute resources to meet application performance demands. In this thesis we investigate the problem of delivering cost-effective elasticity services for cloud applications. Traditionally, the application level elasticity addresses the question of how to scale applications up and down to meet their performance requirements, but does not adequately address issues relating to minimising the costs of using the service. With this current limitation in mind, we propose a scaling approach that makes use of cost-aware criteria to detect the bottlenecks within multi-tier cloud applications, and scale these applications only at bottleneck tiers to reduce the costs incurred by consuming cloud infrastructure resources. Our approach is generic for a wide class of multi-tier applications, and we demonstrate its effectiveness by studying the behaviour of an example electronic commerce site application. Furthermore, we consider the characteristics of the algorithm for implementing the business logic of cloud applications, and investigate the elasticity at the algorithm level: when dealing with large-scale data under resource and time constraints, the algorithm's output should be elastic with respect to the resource consumed. We propose a novel framework to guide the development of elastic algorithms that adapt to the available budget while guaranteeing the quality of output result, e.g. prediction accuracy for classification tasks, improves monotonically with the used budget.Comment: 211 pages, 27 tables, 75 figure

A Review of the Current Level of Support to Aid Decisions for Migrating to Cloud Computing

© 2016 Copyright held by the owner/author(s). Cloud computing provides an innovative delivery model that enables enterprises to reduce operational costs and improve flexibility and scalability. Organisations wishing to migrate their legacy systems to the cloud often need to go through a difficult and complicated decision-making process. This can be due to multiple factors including restructuring IT resources, the still evolving nature of the cloud environment, and the continuous expansion of the services offered. These have increased the requirement for tools and techniques to help the decision-making process for migration. Although significant contributions have been made in this area, there are still many aspects which require further support. This paper evaluates the existing level of support to aid the decision-making process. It examines the complexity of decisions, evaluates the current state of Decision Support Systems in respect of migrating to the cloud, and analyses three models that proposed support for the migration processes. This paper identifies the need for a coherent approach for supporting the whole decision-making process. Further, it explores possible new approaches for addressing the complex issues involved in decision-making for migrating to the cloud

Managing multi-tiered suppliers in the high-tech industry

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2009.Includes bibliographical references (leaves 131-135).This thesis presents a roadmap for companies to follow as they manage multi-tiered suppliers in the high-tech industry. Our research covered a host of sources including interviews and publications from various companies, consulting companies, software companies, the computer industry, trade associations, and analyst firms among others. While our review found that many companies begin supplier relationship management after sourcing events, we show that managing suppliers should start as companies form their competitive strategy. Our five step roadmap provides a deliberate approach for companies as they build the foundation for effective and successful multi-tiered supplier relationship management.by Charles E. Frantz and Jimin Lee.M.Eng.in Logistic

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference

An Adaptable Optimal Network Topology Model for Efficient Data Centre Design in Storage Area Networks

In this research, we look at how different network topologies affect the energy consumption of modular data centre (DC) setups. We use a combined-input directed approach to assess the benefits of rack-scale and pod-scale fragmentation across a variety of electrical, optoelectronic, and composite network architectures in comparison to a conventional DC. When the optical transport architecture is implemented and the appropriate resource components are distributed, the findings reveal fragmentation at the layer level is adequate, even compared to a pod-scale DC. Composable DCs can operate at peak efficiency because of the optical network topology. Logical separation of conventional DC servers across an optical network architecture is also investigated in this article. When compared to physical decentralisation at the rack size, logical decomposition of data centers inside each rack offers a small decrease in the overall DC energy usage thanks to better resource needs allocation. This allows for a flexible, composable architecture that can accommodate performance based in-memory applications. Moreover, we look at the state of fundamentalmodel and its use in both static and dynamic data centres. According to our findings, typical DCs become more energy efficient when workload modularity increases, although excessive resource use still exists. By enabling optimal resource use and energy savings, disaggregation and micro-services were able to reduce the typical DC's up to 30%. Furthermore, we offer a heuristic to duplicate the Mixed integer model's output trends for energy-efficient allocation of caseloads in modularized DCs

Bot recognition in a Web store: An approach based on unsupervised learning

Abstract Web traffic on e-business sites is increasingly dominated by artificial agents (Web bots) which pose a threat to the website security, privacy, and performance. To develop efficient bot detection methods and discover reliable e-customer behavioural patterns, the accurate separation of traffic generated by legitimate users and Web bots is necessary. This paper proposes a machine learning solution to the problem of bot and human session classification, with a specific application to e-commerce. The approach studied in this work explores the use of unsupervised learning (k-means and Graded Possibilistic c-Means), followed by supervised labelling of clusters, a generative learning strategy that decouples modelling the data from labelling them. Its efficiency is evaluated through experiments on real e-commerce data, in realistic conditions, and compared to that of supervised learning classifiers (a multi-layer perceptron neural network and a support vector machine). Results demonstrate that the classification based on unsupervised learning is very efficient, achieving a similar performance level as the fully supervised classification. This is an experimental indication that the bot recognition problem can be successfully dealt with using methods that are less sensitive to mislabelled data or missing labels. A very small fraction of sessions remain misclassified in both cases, so an in-depth analysis of misclassified samples was also performed. This analysis exposed the superiority of the proposed approach which was able to correctly recognize more bots, in fact, and identified more camouflaged agents, that had been erroneously labelled as humans

The Organization and Evolution of the Hohokam Economy Agent-Based Modeling of Exchange in the Phoenix Basin, Arizona, AD 200-1450

abstract: The Hohokam of central Arizona left behind evidence of a culture markedly different from and more complex than the small communities of O'odham farmers first encountered by Europeans in the sixteenth and seventeenth centuries A.D. Archaeologists have worked for well over a century to document Hohokam culture history, but much about Pre-Columbian life in the Sonoran Desert remains poorly understood. In particular, the organization of the Hohokam economy in the Phoenix Basin has been an elusive and complicated subject, despite having been the focus of much previous research. This dissertation provides an assessment of several working hypotheses regarding the organization and evolution of the pottery distribution sector of the Hohokam economy. This was accomplished using an agent-based modeling methodology known as pattern-oriented modeling. The objective of the research was to first identify a variety of economic models that may explain patterns of artifact distribution in the archaeological record. Those models were abstract representations of the real-world system theoretically drawn from different sources, including microeconomics, mathematics (network/graph theory), and economic anthropology. Next, the effort was turned toward implementing those hypotheses as agent-based models, and finally assessing whether or not any of the models were consistent with Hohokam ceramic datasets. The project's pattern-oriented modeling methodology led to the discard of several hypotheses, narrowing the range of plausible models of the organization of the Hohokam economy. The results suggest that for much of the Hohokam sequence a market-based system, perhaps structured around workshop procurement and shopkeeper merchandise, provided the means of distributing pottery from specialist producers to widely distributed consumers. Perhaps unsurprisingly, the results of this project are broadly consistent with earlier researchers' interpretations that the structure of the Hohokam economy evolved through time, growing more complex throughout the Preclassic, and undergoing a major reorganization resulting in a less complicated system at the transition to the Classic Period.Dissertation/ThesisNetLogo code, software, and model initialization data.Ph.D. Anthropology 201