Dissertations / Theses on the topic 'Load prediction and scheduling'
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Andrews, Matthew. "Scheduling techniques for packet routing, load, balancing and disk scheduling." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/42692.
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Radi, Kamal Mohammed A. R. "Scheduling algorithm including load curtailment costs." Thesis, Queen's University Belfast, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.335465.
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Dacre, Marcus James. "Stochastic scheduling in networks." Thesis, University of Newcastle Upon Tyne, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285800.
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Antoniadis, Antonios. "Scheduling algorithms for saving energy and balancing load." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2012. http://dx.doi.org/10.18452/16566.
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Diese Arbeit beschäftigt sich mit Scheduling von Tasks in Computersystemen. Wir untersuchen sowohl die in neueren Arbeiten betrachtete Zielfunktion zur Energieminimierung als auch die klassische Zielfunktion zur Lastbalancierung auf mehreren Prozessoren. Beim Speed-Scaling mit Sleep-State darf ein Prozessor, der zu jedem Zeitpunkt seine Geschwindigkeit anpassen kann, auch in einen Schlafmodus übergehen. Unser Ziel ist es, den Energieverbrauch zu minimieren. Wir zeigen die NP-Härte des Problems und klären somit den Komplexitätsstatus. Wir beweisen eine untere Schranke für die Approximationsgüte für eine spezielle natürliche Klasse von Schedules. Ferner entwickeln wir eine Familie von Algorithmen, die gute Approximationsfaktoren liefert, und zeigen, dass diese sogar Lösungen liefert, die optimal für die zuvor erwähnte Klasse von Schedules sind. Anschließend widmen wir unsere Aufmerksamkeit dem folgenden Termin-basierten Scheduling-Problem. Es seien mehrere Prozessoren gegeben, wobei jeder einzelne Prozessor zu jedem Zeitpunkt seine Geschwindigkeit anpassen kann. Ziel ist es wie zuvor, den Energieverbrauch des erzeugten Schedules zu minimieren. Für den Offline-Fall entwickeln wir einen optimalen Polynomialzeit-Algorithmus. Für das Online-Problem erweitern wir die zwei bekannten Ein-Prozessor-Algorithmen Optimal Available und Average Rate. Wir zeigen, dass diese den gleichen bzw. einen um die additive Konstante von eins vergrößerten kompetiven Faktor haben. Bei der Lastbalancierung auf mehreren Prozessoren betrachten wir Offline-Load-Balancing auf identischen Maschinen. Unser Ziel ist es, die Current-Load für temporäre Tasks mit identischem Gewicht zu minimieren. Wir zeigen, dass eine Lösung mit maximaler Imbalance von eins immer existiert und entwickeln einen effizienten Algorithmus, der solche Lösungen liefert. Zum Schluss beweisen wir die NP-Härte von zwei Verallgemeinerungen des Problems.This thesis studies problems of scheduling tasks in computing environments. We consider both the modern objective function of minimizing energy consumption, and the classical objective of balancing load across machines. We first investigate offline deadline-based scheduling in the setting of a single variable-speed processor that is equipped with a sleep state. The objective is that of minimizing the total energy consumption. Apart from settling the complexity of the problem by showing its NP-hardness, we provide a lower bound of 2 for general convex power functions, and a particular natural class of schedules. We also present an algorithmic framework for designing good approximation algorithms. Furthermore, we give tight bounds for the aforementioned particular class of schedules. We then focus on the multiprocessor setting where each processor has the ability to vary its speed. We first study the offline problem and show that optimal schedules can be computed efficiently in polynomial time. Regarding the online problem and a natural class of power functions, we extend the two well-known single-processor algorithms Optimal Available and Average Rate. We prove that Optimal Available has the same competitive ratio as in the single-processor case. For Average Rate we show a competitive factor that increases by an additive constant of one compared to the single-processor result. With respect to load balancing, we consider offline load balancing on identical machines, with the objective of minimizing the current load, for temporary unit-weight jobs. The problem can be seen as coloring n intervals with k colors, such that for each point on the line, the maximal difference between the number of intervals of any two colors is minimal. We prove that a coloring with maximal difference at most one is always possible, and develop a fast polynomial-time algorithm for generating such a coloring. Lastly, we prove that two generalizations of the problem are NP-hard.
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Piccinini, Federico. "Dynamic load balancing based on latency prediction." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143333.
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Spotify is a music streaming service that offers access to a vast music catalogue; it counts more than 24 million active users in 28 different countries. Spotify's backend is made up by a constellation of independent loosely-coupled services; each service consists of a set of replicas, running on a set of servers in multiple data centers: each request to a service needs to be routed to an appropriate replica. Balancing the load across replicas is crucial to exploit available resources in the best possible way, and to provide optimal performances to clients. The main aim of this project is exploring the possibility of developing a load balancing algorithm that exploits request-reply latencies as its only load index. There are two reasons why latency is an appealing load index: in the first place it has a significant impact on the experience of Spotify users; in the second place, identifying a good load index in a distributed system presents significant challenges due to phenomena that might arise from the interaction of the different system components such as multi-bottlenecks. The use of latency as load index is even more attractive under this light, because it allows for a simple black box model where it is not necessary to model resource usage patterns and bottlenecks of every single service individually: modeling each system would be an impractical task, due both to the number of services and to the speed at which these services evolve. In this work, we justify the choice of request-reply latency as a load indicator, by presenting empirical evidence that it correlates well with known reliable load index obtained through a white box approach. In order to assess the correlation between latency and a known load index obtained through a white box approach, we present measurements from the production environment and from an ad-hoc test environment. We present the design of a novel load balancing algorithm based on a modified ' accrual failure detector that exploits request-reply latency as an indirect measure of the load on individual backends; we analyze the algorithm in detail, providing an overview of potential pitfalls and caveats; we also provide an empirical evaluation of our algorithm, compare its performances to a pure round-robin scheduling discipline and discuss which parameters can be tuned and how they affect the overall behavior of the load balancer.
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Murphy, Patrick F. R. "Load-haul-dump vehicle component life prediction using experimentally acquired load histories." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2002. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ65641.pdf.
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Dogan, Osman Tufan. "A Stochastic Approach For Load Scheduling Of Cogeneration Plants." Phd thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/2/12611698/index.pdf.
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In this thesis, load scheduling problem for cogeneration plants is interpreted in the context of stochastic programming. Cogeneration (CHP) is an important technology in energy supply of many countries. Cogeneration plants are designed and operated to cover the requested time varying demands in heat and power. Load scheduling of cogeneration plants represents a multidimensional optimization problem, where heat and electricity demands, operational parameters and associated costs exhibit uncertain behavior. Cogeneration plants are characterized by their &lsquoheat to power ratio&rsquo
. This ratio determines the operating conditions of the plant. However, this ratio may vary in order to adapt to the physical and economical changes in power and to the meteorological conditions. Employing reliable optimization models to enhance short term scheduling capabilities for cogeneration systems is an important research area. The optimal load plan is targeted by achieving maximum revenue for cogeneration plants. Revenue is defined for the purpose of the study as the sales revenues minus total cost associated with the plant operation. The optimization problem, which aims to maximize the revenue, is modeled by thermodynamic analyses. In this context, the study introduces two objective functions: energy based optimization, exergy-costing based optimization. A new method of stochastic programming is developed. This method combines dynamic programming and genetic algorithm techniques in order to improve computational efficiency. Probability density function estimation method is introduced to determine probability density functions of heat demand and electricity price for each time interval in the planning horizon. A neural network model is developed for this purpose to obtain the probabilistic data for effective representation of the random variables. In this study, thermal design optimization for cogeneration plants is also investigated with particular focus on the heat storage volume.
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Rosas, Ham Demian. "Dynamic scheduling in multicore processors." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/dynamic-scheduling-in-multicore-processors(c96d6641-ba16-44d2-9da5-34c41fd15956).html.
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The advent of multi-core processors, particularly with projections that numbers of cores will continue to increase, has focused attention on parallel programming. It is widely recognized that current programming techniques, including those that are used for scientific parallel programming, will not allow the easy formulation of general purpose applications. An area which is receiving interest is the use of programming styles which do not have side-effects. Previous work on parallel functional programming demonstrated the potential of this to permit the easy exploitation of parallelism. This thesis investigates a dynamic load balancing system for shared memory Chip Multiprocessors. This system is based on a parallel computing model called SLAM (Spreading Load with Active Messages), which makes use of functional language evaluation techniques. A novel hardware/software mechanism for exploiting fine grain parallelism is presented. This mechanism comprises a runtime system which performs dynamic scheduling and synchronization automatically when executing parallel applications. Additionally the interface for using this mechanism is provided in the form of an API. The proposed system is evaluated using cycle-level models and multithreaded applications running in a full system simulation environment.
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Bahadornejad, Momen. "On-line local load measurement based voltage instability prediction." Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/16187/1/Momen_Bahadornejad_Thesis.pdf.
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Voltage instability is a major concern in operation of power systems and it is well known that voltage instability and collapse have led to blackout or abnormally low voltages in a significant part of the power system. Consequently, tracking the proximity of the power system to an insecure voltage condition has become an important element of any protection and control scheme.
The expected time until instability is a critical aspect. There are a few energy management systems including voltage stability analysis function in the real-time environment of control centres, these are based on assumptions (such as off-line models of the system loads) that may lead the system to an insecure operation and/or poor utilization of the resources.
Voltage instability is driven by the load dynamics, and investigations have shown that load restoration due to the on-load tap changer (OLTC) action is the main cause of the voltage instability. However, the aggregate loads seen from bulk power delivery transformers are still the most uncertain power system components, due to the uncertainty of the participation of individual loads and shortcomings of the present approaches in the load modeling.
In order to develop and implement a true on-line voltage stability analysis method, the on-line accurate modeling of the higher voltage (supply system) and the lower voltage level (aggregate load) based on the local measurements is required.
In this research, using the changes in the load bus measured voltage and current, novel methods are developed to estimate the supply system equivalent and to identify load parameters. Random changes in the load voltage and current are processed to estimate the supply system Thevenin impedance and the composite load components are identified in a peeling process using the load bus data changes during a large disturbance in the system. The results are then used to anticipate a possible long-term voltage instability caused by the on-load tap changer operation following the disturbance. Work on the standard test system is provided to validate the proposed methods.
The findings in this research are expected to provide a better understanding of the load dynamics role in the voltage stability, and improve the reliability and economy of the system operation by making it possible to decrease uncertainty in security margins and determine accurately the transfer limits.
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Bahadornejad, Momen. "On-line local load measurement based voltage instability prediction." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16187/.
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Voltage instability is a major concern in operation of power systems and it is well known that voltage instability and collapse have led to blackout or abnormally low voltages in a significant part of the power system. Consequently, tracking the proximity of the power system to an insecure voltage condition has become an important element of any protection and control scheme. The expected time until instability is a critical aspect. There are a few energy management systems including voltage stability analysis function in the real-time environment of control centres, these are based on assumptions (such as off-line models of the system loads) that may lead the system to an insecure operation and/or poor utilization of the resources. Voltage instability is driven by the load dynamics, and investigations have shown that load restoration due to the on-load tap changer (OLTC) action is the main cause of the voltage instability. However, the aggregate loads seen from bulk power delivery transformers are still the most uncertain power system components, due to the uncertainty of the participation of individual loads and shortcomings of the present approaches in the load modeling. In order to develop and implement a true on-line voltage stability analysis method, the on-line accurate modeling of the higher voltage (supply system) and the lower voltage level (aggregate load) based on the local measurements is required. In this research, using the changes in the load bus measured voltage and current, novel methods are developed to estimate the supply system equivalent and to identify load parameters. Random changes in the load voltage and current are processed to estimate the supply system Thevenin impedance and the composite load components are identified in a peeling process using the load bus data changes during a large disturbance in the system. The results are then used to anticipate a possible long-term voltage instability caused by the on-load tap changer operation following the disturbance. Work on the standard test system is provided to validate the proposed methods. The findings in this research are expected to provide a better understanding of the load dynamics role in the voltage stability, and improve the reliability and economy of the system operation by making it possible to decrease uncertainty in security margins and determine accurately the transfer limits.
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Graue, Ryan M. (Ryan Matthew). "Prediction and optimization techniques to streamline surgical scheduling." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82482.
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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics; in conjunction with the Leaders for Global Operations Program at MIT, 2013.This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from department-submitted PDF version of thesis
Includes bibliographical references (p. 72-76).
Abstract We have created a set of decision support tools to streamline the surgical case scheduling process by allowing surgical wait list cases (elective cases that cannot be assigned a slot on the operating room schedule at the time of booking) to be confirmed onto the operating room schedule up to three weeks in advance of the day of surgery. Prior to our research, wait list cases could not be confirmed more than a few days prior to the desired day of surgery due to uncertainty about available time prior to the release of dedicated OR capacity. Earlier confirmation of wait list cases serves three purposes: (1) to improve patients' ability to plan logistics to prepare for their visits, (2) to reduce wait list case backlogs for surgeons' offices, and (3) to reduce variability in the total daily caseload through proactive decision making. Our contributions assist scheduling personnel in confirming wait list case dates sooner to help medical institutions achieve these benefits. We have developed two Excel-based pieces of software: a prediction tool and a schedule optimization tool. The prediction tool predicts time that is available each day between one and three weeks in advance to accommodate wait list cases, and the schedule optimization tool automates the consolidation process for all cases that are currently booked on a future date so that rooms and equipment are used as efficiently as possible. Our platform lets users interact with simple GUIs in which they make selections to generate prediction results and optimized daily case schedules. Specifically, our prediction algorithm employs a multiple linear regression model over historical data to forecast unused time, and the optimization tool uses a mixed integer linear program to optimize the daily schedule by consolidating cases into a minimum number of rooms and closing any gaps between cases, subject to constraints that are specific to the facility and the date in question. We have achieved our desired outcome of maximizing operating room resource utilization by giving human schedulers a set of tools to use on a daily basis that simplifies the scheduling process and confirms wait list cases with more advance notice. This system is generalizable to other areas within healthcare delivery environments and any other industry where tasks are scheduled in advance into a fixed set of resources with a record of historical demand over time.
by Ryan M. Graue.
S.M.
M.B.A.
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Senniappan, Karthikeyan Yildirim Mehmet Bayram. "Parallel-machine scheduling with load-balancing and sequence-dependent setups." Diss., A link to full text of this thesis in SOAR, 2006. http://soar.wichita.edu/dspace/handle/10057/679.
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Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering."December 2006." Title from PDF title page (viewed on Sept. 18, 2007). Thesis adviser: Mehmet Bayram Yildirim. Includes bibliographic references (leaves 53-57).
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Saranyan, N. "Prediction based load balancing heuristic for a heterogeneous cluster." Thesis, Indian Institute of Science, 2003. http://hdl.handle.net/2005/95.
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Load balancing has been a topic of interest in both academia and industry, mainly
because of the scope for performance enhancement that is available to be exploited in
many parallel and distributed processing environments. Among the many approaches
that have been used to solve the load balancing problem, we find that only very few
use prediction of code execution times. Our reasoning for this is that the field of code prediction
is in its infancy. As of this writing, we are not aware of any prediction-based
load balancing approach that uses prediction8 of code-execution times, and uses neither
the information provided by the user, nor an off-line step that does the prediction, the
results of which are then used at run-time. In this context, it is important to note that
prior studies have indicated the feasibility of predicting the CPU requirements of general
application programs.
Our motivation in using prediction-based load balancing is to determine the feasibility
of the approach. The reasoning behind that is the following: if prediction-based load
balancing does yield good performance, then it may be worthwhile to develop a predictor
that can give a rough estimate of the length of the next CPU burst of each process. While
high accuracy of the predictor is not essential, the computation overhead of the predictor
must be sufficiently' small, so as not to offset the gain of load balancing.
As for the system, we assume a set of autonomous computers, that are connected by
a fast, shared medium. The individual nodes can vary in the additional hardware and
software that may be available in them. Further, we assume that the processes in the
workload are sequential.
The first step is to fix the parameters for our assumed predictor. Then, an algorithm
that takes into account the characteristics of the predictor is proposed. There are many
trade-off decisions in the design of the algorithm, including certain steps in which we
have relied on trial and error method to find suitable values. The next logical step is
to verify the efficiency of the algorithm. To assess its performance, we carry out event
driven simulation. We also evaluate the robustness of the algorithm with respect to the
characteristics of the predictor.
The contribution of the thesis is as follows: It proposes a load-balancing algorithm
for a heterogeneous cluster of workstations connected by a fast network. The simulation
assumes that the heterogeneity is limited to variability in processor clock rates; but
the algorithm can be applied when the nodes have other types of heterogeneity as well.
The algorithm uses prediction of CPU burst lengths as its basic input unit. The performance
of the algorithm is evaluated through event driven simulation using assumed
workload distributions. The results of the simulation show that the algorithm yields a
good improvement in response times over the scenario in which no load redistribution is
done.
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Molloy, Susan. "Ship powering prediction using load varying self-propulsion tests." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ62403.pdf.
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Leen, Sean B. "Elastic-plastic generalised load-displacement prediction for tubular joints." Thesis, University of Nottingham, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287266.
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Dunn, Michael S. "Asymmetric Non-Uniform Proportional Share Scheduling." Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1282313314.
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Thiam, Cheikhou. "Anti load-balancing for energy-aware distributed scheduling of virtual machines." Toulouse 3, 2014. http://thesesups.ups-tlse.fr/2441/.
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La multiplication de l'informatique en nuage (Cloud) a abouti à la création de centres de données dans le monde entier. Le Cloud contient des milliers de nœuds de calcul. Cependant, les centres de données consomment d'énorme quantités d'énergie à travers le monde estimées à plus de 1,5 % de la consommation mondiale d'électricité et devrait continuer à croître. Une problématique habituellement étudiée dans les systèmes distribués est de répartir équitablement la charge. Mais lorsque l'objectif est de réduire la consommation électrique, ce type d'algorithmes peut mener à avoir des serveurs fortement sous chargés et donc à consommer de l'énergie inutilement. Cette thèse présente de nouvelles techniques, des algorithmes et des logiciels pour la consolidation dynamique et distribuée de machines virtuelles (VM) dans le Cloud. L'objectif principal de cette thèse est de proposer des stratégies d'ordonnancement tenant compte de l'énergie dans le Cloud pour les économies d'énergie. Pour atteindre cet objectif, nous utilisons des approches centralisées et décentralisées. Les contributions à ce niveau méthodologique sont présentées sur ces deux axes. L'objectif de notre démarche est de réduire la consommation de l'énergie totale du centre de données en contrôlant la consommation globale d'énergie des applications tout en assurant les contrats de service pour l'exécution des applications. La consommation d'énergie est réduite en désactivant et réactivant dynamiquement les nœuds physiques pour répondre à la demande des ressources. Les principales contributions sont les suivantes: - Ici on s'intéressera à la problématique contraire de l'équilibrage de charge. Il s'agit d'une technique appelée Anti Load-Balancing pour concentrer la charge sur un nombre minimal de nœuds. Le but est de pouvoir éteindre les nœuds libérés et donc de minimiser la consommation énergétique du système. - Ensuite une approche centralisée a été proposée et fonctionne en associant une valeur de crédit à chaque nœud. Le crédit d'un nœud dépend de son affinité pour ses tâches, sa charge de travail actuelle et sa façon d'effectuer ses communications. Les économies d'énergie sont atteintes par la consolidation continue des machines virtuelles en fonction de l'utilisation actuelle des ressources, les topologies de réseaux virtuels établis entre les machines virtuelles et l'état thermique de nœuds de calcul. Les résultats de l'expérience sur une extension de CloudSim (EnerSim) montrent que l'énergie consommée par les applications du Cloud et l'efficacité énergétique ont été améliorées. - Le troisième axe est consacré à l'examen d'une approche appelée "Cooperative scheduling Anti load-balancing Algorithm for cloud". Il s'agit d'une approche décentralisée permettant la coopération entre les différents sites. Pour valider cet algorithme, nous avons étendu le simulateur MaGateSim. Avec une large évaluation expérimentale d'un ensemble de données réelles, nous sommes arrivés à la conclusion que l'approche à la fois en utilisant des algorithmes centralisés et décentralisés peut réduire l'énergie consommée des centres de donnéesThe multiplication of Cloud computing has resulted in the establishment of largescale data centers around the world containing thousands of compute nodes. However, Cloud consume huge amounts of energy. Energy consumption of data centers worldwide is estimated at more than 1. 5% of the global electricity use and is expected to grow further. A problem usually studied in distributed systems is to evenly distribute the load. But when the goal is to reduce energy consumption, this type of algorithms can lead to have machines largely under-loaded and therefore consuming energy unnecessarily. This thesis presents novel techniques, algorithms, and software for distributed dynamic consolidation of Virtual Machines (VMs) in Cloud. The main objective of this thesis is to provide energy-aware scheduling strategies in cloud computing for energy saving. To achieve this goal, we use centralized and decentralized approaches. Contributions in this method are presented these two axes. The objective of our approach is to reduce data center's total energy consumed by controlling cloud applications' overall energy consumption while ensuring cloud applications' service level agreement. Energy consumption is reduced by dynamically deactivating and reactivating physical nodes to meet the current resource demand. The key contributions are: - First, we present an energy aware clouds scheduling using anti-load balancing algorithm : concentrate the load on a minimum number of severs. The goal is to turn off the machines released and therefore minimize the energy consumption of the system. - The second axis proposed an algorithm which works by associating a credit value with each node. The credit of a node depends on its affinity to its jobs, its current workload and its communication behavior. Energy savings are achieved by continuous consolidation of VMs according to current utilization of resources, virtual network topologies established between VMs, and thermal state of computing nodes. The experiment results, obtained with a simulator which extends CloudSim (EnerSim), show that the cloud application energy consumption and energy efficiency are being improved. - The third axis is dedicated to the consideration of a decentralized dynamic scheduling approach entitled Cooperative scheduling Anti-load balancing Algorithm for cloud. It is a decentralized approach that allows cooperation between different sites. To validate this algorithm, we have extended the simulator MaGateSim. With an extensive experimental evaluation with a real workload dataset, we got the conclusion that both the approach using centralized and decentralized algorithms can reduce energy consumed by data centers
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Joshi, Avinash. "Load Balancing, Queueing and Scheduling Mechanisms in Mobile Ad Hoc Networks." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1004447615.
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Yusof, Norzan Mohd. "Environmental load versus concrete quality : prediction of structure's design life." Thesis, University of Birmingham, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323997.
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Xie, Ningbo. "NDT-based performance prediction for wrinkled composites under compressive load." Thesis, University of Bristol, 2018. http://hdl.handle.net/1983/919cd8f3-463f-4618-b794-ede41a5b92ad.
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The work in this thesis aimed to develop a method to transfer the three-dimensional (3D) non-destructive characterised data into finite-element models containing as-manufactured structural details. Two types of fibre-misalignment defects: out-of-plane wrinkles and in-plane waviness, were investigated and a quasi-isotropic layup [45o/90 o /-45 o /0 o]ns was chosen as the representative industrial layup sequence for validation purposes. A pivotal part of the work was the development of the transfer process to automatically and faithfully convert the output of non-destructive characterisation inversion methods into finite-element models containing these two types of defect. For out-of-plane wrinkles, using this transfer method and previously validated modelling techniques, a series of models were exercised under compressive load to identify the hierarchy and interdependences of wrinkle parameters, covering the wrinkle severity, shape and extent. The outcome indicated that the maximum out-of-plane wrinkle angle from the load direction was the governing factor to influence the compressive strength and this should be the key parameter to be measured non-destructively when wrinkles are detected in the components. For in-plane waviness, it was studied both experimentally and numerically. First, the in-plane waviness was introduced into 0 plies by a specially designed rig and four waviness-severity coupons were manufactured to investigate the compressive damage process influenced by this in-plane waviness. Two High-speed cameras were used, focusing on the edge and the front face of the coupon waviness region to capture the failure modes and their sequences. X-ray CT techniques were also applied to identify the internal failure activities when the loading process was interrupted for some samples, before the ultimate damage was reached. Numerical models were then created in Abaqus/Explicit following a similar method to the out-of-plane case but requiring significant further development in order to faithfully model samples that could also be created experimentally. These were validated against the experimental recordings. Both experimental and numerical studies showed the dominant failure modes were delamination and splitting due to in-plane waviness incorporated in the samples.
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Chan, Sonya. "The use of EMG for load prediction during manual lifting." Thesis, Kingston, Ont. : [s.n.], 2007. http://hdl.handle.net/1974/871.
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Bai, Mo, and Mahabaleshwar Sagar Parampalli. "Accelerated Engine Suspension Load Prediction and Exhaust System Displacement Simulation." Thesis, KTH, Maskinkonstruktion (Inst.), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-218029.
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In today’s competitive automotive industry, most companies are trying to make their new designs and features implemented in their products to be ahead of their competitors. However, in the preliminary design stage of CEVT vehicles, dynamic simulation consumes excessive amount of time depending on the complexity of the dynamic model and simulation settings. It is beneficial and possible to shorten the simulation time. This thesis focuses on reducing the dynamic simulation time in ADAMS/Car in engine suspensions’ early development stage. Five simulation time reducing methods, i.e., reducing end time in driver control maneuver, stopping similar simulations, converting specific flexible parts to rigid parts, properly increasing the step size and performing simulation for engine suspension subsystem instead of the full vehicle system, were proposed and investigated separately to study their effects on the simulation time and the accuracy of the fatigue damage results of the engine suspension. With the proper combination of the five methods, total dynamic simulation time was effectively reduced to 61% and the variation of fatigue damage results of each engine suspension component was controlled within 30%. Dynamic modelling of an exhaust system is also included in this thesis and it provides referential data for the packaging design of exhaust system.I dagens konkurrensutsatta bilindustri försöker de flesta företag att göra sina nya mönster och funktioner implementerbara i sina produkter för att vara före sina konkurrenter. I det preliminära konstruktionsstadiet av CEVT-fordon förbrukar dynamisk simulering dock en stor tid beroende på komplexiteten hos den dynamiska modellen och simuleringsinställningarna. Det är fördelaktigt och möjligt att förkorta simuleringstiden. Denna avhandling fokuserar på att minska den dynamiska simuleringstiden i ADAMS / Car i motorupphängningens tidiga utvecklingsstadium. Med hjälp av bakgrundsstudier och erfarenheter från CEVT’s personal provades fem olika sätt att minska simuleringstiden, samtidigt som simuleringsresultatens noggrannhet kontrollerades. Varje metod användes separat i simuleringen för att studera effekten på resultatens noggrannhet. I slutet kombineras alla metoder i simuleringen för att få bästa möjliga simuleringstid utan att förlora noggrannhet. Genom att kombinera de fem metoderna reducerades den totala dynamiska simuleringstiden till 61% och variationen i utmattningsskadans resultat av varje motorupphängningskomponent kontrollerades inom 30%. Dynamisk modellering av ett avgassystem ingår också i denna avhandling, vilket ger referensdata för framtida förpackningsdesign av avgassysteme
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Jensen, Taylor (Taylor Moroni), and Qi Sun. "Absenteeism prediction and labor force optimization in rail dispatcher scheduling." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85457.
Full textAbstract:
Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Engineering Systems Division, 2013.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 61-62).
Unplanned employee absences are estimated to account for a loss of 3% of scheduled labor hours. This can be costly in railroad dispatcher scheduling because every absence must be filled through overtime or a qualified extra dispatcher. One factor that complicates this problem is the uncertainty of unplanned employee absences. The ability to predict unplanned absences would facilitate effective scheduling of extra dispatchers and help reduce overtime costs. This thesis uses data from a railroad company over a four year period to examine company-wide factors thought to impact the number of unplanned absences among dispatchers. Using Poisson regression, we identify several factors that provide statistical evidence of influencing the number of unplanned absences. These factors are month, snowstorms, shift, and certain holidays. Despite these findings, the overall predictive capability of our regression model is very weak. Instead, we model the number of unplanned absences by shift as a Hadrom process with a Negative Binomial distribution and use Monte Carlo simulation to explore the impact on overtime costs of increasing the number of scheduled extra dispatchers and increasing the number of positions on which each employee is qualified to work. Our results show that increasing the number of extra dispatchers has a greater effect on reducing overtime, but the cost savings from reducing overtime expenses are not enough to offset the additional labor costs of having more employees on staff. Our results provide insight regarding the relationship among extra staff, higher levels of qualification among employees, and the willingness to use overtime in handling unplanned absences.
by Taylor Jensen and Qi Sun.
M. Eng. in Logistics
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Xu, Yizheng. "Probabilistic estimation and prediction of the dynamic response of the demand at bulk supply points." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/probabilistic-estimation-and-prediction-of-the-dynamic-response-of-the-demand-at-bulk-supply-points(b9e427ec-7e5e-49a5-aec4-0f34032d71a9).html.
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The dynamic response of the demand is defined as the time-domain real and reactive power response to a voltage disturbance, and it represents the dynamic load characteristics. This thesis develops a methodology for probabilistic estimation and prediction of dynamic responses of the demand at bulk supply points. The main outcome of the research is being able to predict the contribution of different categories of loads to the total demand mix and their controllability without conducting detailed customer surveys or collecting smart meter data, and to predict the dynamic response of the demand without performing field tests. The prediction of the contributions of different load categories and their controllability and load characteristics in the near future (e.g., day ahead) plays an important role in system analysis and planning, especially in the short-term dispatch and control. However, the research related to this topic is missing in the publically available literature, and an approach needs to be developed to enable the prediction of the participation of different loads in total load mix, their controllability and the dynamic response of the demand. This research contributes to a number of areas, such as load forecasting, load disaggregation and load modelling. First, two load forecasting methodologies which have not been compared before are compared; and based on the results of comparison and considering the actual requirements in this research, a methodology is selected and used to predict both the real and reactive power. Second, a unique methodology for load disaggregation is developed. This methodology enables the estimation of the contributions of different load categories to the total demand mix and their controllability based on RMS measured voltage and real and reactive power. The confidence level of the estimation is also assessed. The methodology for disaggregation is integrated with the load forecasting tool to enable prediction of load compositions and dynamic responses of the demand. The prediction is validated with data collected from real UK power network. Finally, based on the prediction, an example of load shifting is used to demonstrate that different dynamic responses can be obtained based on the availability and redistribution of controllable devices and that load shifting decisions, i.e., demand side management actions, should be made based not only on the amount of demand to be shifted, but also on predicted responses before and after load shifting.
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Bischof, Stefan. "Efficient algorithms for on-line scheduling and load distribution in parallel systems." [S.l. : s.n.], 1999. http://deposit.ddb.de/cgi-bin/dokserv?idn=959771409.
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Haukedal, Eirik Daleng. "Load-scheduling and Plug-in Hybrid Electric Vehicles in the Smart Grid." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18624.
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To avoid the problem that increasing PHEV demand will further aggravate peak demand hours in the power grid, several different multi-agent scheduling mechanisms have been investigated, including two centralized scheduling mechanisms and two decentralized scheduling mechanisms. For both of the decentralized mechanisms, the PHEV agents choose their own charging plans without relying upon a centralized scheduler, while in the centralized scheduling mechanisms, the PHEVs agents defer control to a central agent for creating their charging plans. From the results, we found that while both the centralized mechanisms and the decentralized mechanisms helped to reduce the average maximum peak, the performance of the centralized mechanisms proved to be highly dependent on how the day-ahead portfolio was calculated. Because of this, the overall best performer was decentralized mechanism, which gave the best results for reducing the average maximum peak without compromising the ability of the PHEVs to charge their batteries too much.
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ALWAN, HAYDER O. "Load Scheduling with Maximum Demand and Time of Use pricing for Microgrids." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5954.
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Several demand side management (DSM) techniques and algorithms have been used in the literature. These algorithms show that by adopting DSM and Time-of-Use (TOU) price tariffs; electricity cost significantly decreases, and optimal load scheduling is achieved. However, the purpose of the DSM is to not only lower the electricity cost, but also to avoid the peak load even if the electricity prices low. To address this concern, this dissertation starts with a brief literature review on the existing DSM algorithms and schemes. These algorithms can be suitable for Direct Load Control (DLC) schemes, Demand Response (DR), and load scheduling strategies. \end{abstract} Secondly, the dissertations compares two of DSM algorithms to show the performance based on cost minimization, voltage fluctuation, and system power loss [see in Chapter 5]. The results show the importance of balance between objectives such as electricity cost minimization, peak load occurrence, and voltage fluctuation evolution while simultaneously optimizing the cost.
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Alkharboush, Raed. "Improved Prediction-based Dynamic Load Balancing Systems for HLA-Based Distributed Simulations." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32162.
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Due to the dependency of High-Level Architecture (HLA)-Based simulations on the resources of distributed environments, simulations can face load imbalances and can suffer from low performance in terms of execution time. HLA is a framework that simplifies the implementation of distributed simulations; it also has been built with dedicated resources in mind. As technology is nowadays shifting towards shared environments, the following two weaknesses have become apparent in HLA: managing federates and reacting towards load imbalances on shared resources. Moreover, a number of dynamic load management systems have been designed in order to provide a solution to enable a balanced simulation environment on shared resources. These solutions use specific techniques depending on simulation characteristics or load aspects to perform the balancing task. Load prediction is one of such techniques that improves load redistribution heuristics by preventing load imbalances. In this thesis, a number of enhancements for a prediction technique are presented, and their efficiency are compared. The proposed enhancements solve the observed problems with Holt’s implementations on dynamic load balancing systems for HLA-Based distributed simulations and provide better forecasting. As a result, these enhancements provide better predictions for the load oscillations of the shared resources. Furthermore, a number of federate migration decision-making approaches are introduced to add more intelligence into the process of migrating federates. The approaches aim to solve a dependency problem in the prediction-based load balancing system on the prediction model, thus making similar systems adapt to any future system improvements.
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Blagojevic, Filip. "Scheduling on Asymmetric Architectures." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/27952.
Full textAbstract:
We explore runtime mechanisms and policies for scheduling dynamic multi-grain parallelism on heterogeneous multi-core processors. Heterogeneous multi-core processors integrate conventional cores that run legacy codes with specialized cores that serve as computational accelerators. The term multi-grain parallelism refers to the exposure of multiple dimensions of parallelism from within the runtime system, so as to best exploit a parallel architecture with heterogeneous computational capabilities
between its cores and execution units. To maximize performance on
heterogeneous multi-core processors, programs need to expose multiple dimensions of parallelism simultaneously. Unfortunately, programming with multiple dimensions of parallelism is to date an ad hoc process, relying heavily on the intuition and skill of programmers. Formal techniques are needed to optimize multi-dimensional parallel program designs. We investigate user- and kernel-level schedulers that dynamically "rightsize" the dimensions and degrees of parallelism on the asymmetric parallel platforms. The schedulers address the problem of mapping application-specific concurrency to an architecture with multiple hardware layers of parallelism, without requiring programmer intervention or sophisticated compiler support. Our runtime environment outperforms the native Linux and MPI scheduling environment by up to a factor of 2.7. We also present a model of multi-dimensional parallel computation for steering the parallelization process on heterogeneous multi-core processors. The model predicts with high accuracy the execution time and
scalability of a program using conventional processors and
accelerators simultaneously. More specifically, the model reveals optimal degrees of multi-dimensional, task-level and data-level concurrency, to maximize performance across cores. We evaluate our runtime policies as well as the performance model we developed, on an IBM Cell BladeCenter, as well as on a cluster composed of Playstation3 nodes, using two realistic bioinformatics applications.Ph. D.
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Dhandayuthapani, Sumithra. "Automatic selection of dynamic loop scheduling algorithms for load balancing using reinforcement learning." Master's thesis, Mississippi State : Mississippi State University, 2004. http://library.msstate.edu/etd/show.asp?etd=etd-06292004-144402.
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Ehlers, George E. "Hi-fidelity simulation and prediction of helicopter single point external load stabilization." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA397012.
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Thesis (M.S. in Aeronautical Engineering)--Naval Postgraduate School, Sept. 2001.Thesis advisor: Woods, E. Robert. "September 2001." Includes bibliographical references (p. 43-45). Also Available online.
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Aliamiri, Sara. "Prediction of premature failure load in FRP or steel plated RC beams." Thesis, Loughborough University, 2013. https://dspace.lboro.ac.uk/2134/13631.
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Bozokalfa, Gökhan Akkurt Sedat. ""Ann" artifical neural networks and fuzzy logic models for cooling load prediction/." [s.l.]: [s.n.], 2005. http://library.iyte.edu.tr/tezlerengelli/master/makinamuh/T000372.pdf.
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Thesis (Master)--İzmir Institute Of Technology, İzmir, 2005.Keywords: Artificial neural networks, fuzzy logic, modeling, cooling load, prediction. Includes bibliographical references (leaves. 44-45).
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Bean, Brennan L. "Interval-Valued Kriging Models with Applications in Design Ground Snow Load Prediction." DigitalCommons@USU, 2019. https://digitalcommons.usu.edu/etd/7579.
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One critical consideration in the design of buildings constructed in the western United States is the weight of settled snow on the roof of the structure. Engineers are tasked with selecting a design snow load that ensures that the building is safe and reliable, without making the construction overly expensive. Western states use historical snow records at weather stations scattered throughout the region to estimate appropriate design snow loads. Various mapping techniques are then used to predict design snow loads between the weather stations. Each state uses different mapping techniques to create their snow load requirements, yet these different techniques have never been compared. In addition, none of the current mapping techniques can account for the uncertainty in the design snow load estimates. We address both issues by formally comparing the existing mapping techniques, as well as creating a new mapping technique that allows the estimated design snow loads to be represented as an interval of values, rather than a single value. In the process, we have improved upon existing methods for creating design snow load requirements and have produced a new tool capable of handling uncertain climate data.
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Bo-TingChen and 陳柏廷. "Prediction and Optimal Scheduling of Shedding Air Conditioning Load for Demand Response." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/93pf9z.
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碩士國立成功大學
電機工程學系
106
In order to relieve urgent need of new power plants as well as the pressure of power shortage, Taipower has promoted demand response (DR) program and biding options to consolidate demand side load management. This study evaluates the benefit for NCKU EE to partici-pate in the DR program. The proposed methodology uses historical power consumption, temperature, and humidity statistics as training data to develop a general regression neural network (GRNN) model, so that it can predict the power consumption of the NCKU EE building after shedding its indi-vidual air conditioning (A/C) loads. The predicted power consumption after shedding air conditioning load is further used as input for the genetic algorithm to come up with optimal commitment of A/C load shedding. The proposed strategy was applied in June 2018 to check the benefit of joining the DR program.
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Badenhorst, Werner. "Optimal control on rock winder hoist scheduling." Diss., 2010. http://hdl.handle.net/2263/28001.
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This dissertation addresses the problem of optimally scheduling the hoists of a twin rock winder system in a demand side management context. The objective is to schedule the hoists at minimum energy cost taking into account various physical and operational constraints and production requirements as well as unplanned system delays. The problem is solved by first developing a static linear programming model of the rock winder system. The model is built on a discrete dynamic winder model and consists of physical and operational winder system constraints and an energy cost based objective function. Secondly a model predictive control based scheduling algorithm is applied to the model to provide closed-loop feedback control. The scheduling algorithm first solves the linear programming problem before applying an adapted branch and bound integer solution methodology to obtain a near optimal integer schedule solution. The scheduling algorithm also compensates for situations resulting in infeasible linear programming solutions. The simulation results show the model predictive control based scheduling algorithm to be able to successfully generate hoist schedules that result in steady state solutions in all scenarios studied, including where delays are enforced. The energy cost objective function is proven to be very effective in ensuring minimal hoisting during expensive peak periods and maximum hoisting during low energy cost off-peak periods. The algorithm also ensures that the hoist target is achieved while controlling all system states within or around their boundaries for a sustainable and continuous hoist schedule. CopyrightDissertation (MEng)--University of Pretoria, 2010.
Electrical, Electronic and Computer Engineering
Unrestricted
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Chen, Niangjun. "Model Predictive Control for Deferrable Loads Scheduling." Thesis, 2014. https://thesis.library.caltech.edu/8468/1/master_thesis.pdf.
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Real-time demand response is essential for handling the uncertainties of renewable generation. Traditionally, demand response has been focused on large industrial and commercial loads, however it is expected that a large number of small residential loads such as air conditioners, dish washers, and electric vehicles will also participate in the coming years. The electricity consumption of these smaller loads, which we call deferrable loads, can be shifted over time, and thus be used (in aggregate) to compensate for the random fluctuations in renewable generation.
In this thesis, we propose a real-time distributed deferrable load control algorithm to reduce the variance of aggregate load (load minus renewable generation) by shifting the power consumption of deferrable loads to periods with high renewable generation. The algorithm is model predictive in nature, i.e., at every time step, the algorithm minimizes the expected variance to go with updated predictions. We prove that suboptimality of this model predictive algorithm vanishes as time horizon expands in the average case analysis. Further, we prove strong concentration results on the distribution of the load variance obtained by model predictive deferrable load control. These concentration results highlight that the typical performance of model predictive deferrable load control is tightly concentrated around the average-case performance. Finally, we evaluate the algorithm via trace-based simulations.
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Sou-Jen, Lai, and 賴守仁. "Speculative Load/Store Scheduling for X86." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/10264470881287369734.
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碩士國立交通大學
資訊工程系
87
The design of Load/Store Units in existed superscalar microarchitectures is always restricted on the conservative scheduling of load/store operations. True data dependency of a load operation is checked before issuing to the data cache. Only load bypassing or forwarding is provided in the scheduling policy of the load/store units. However, the instruction level parallelism is still limited by true data dependencies seriously. Thus, various speculation methods have been proposed to increase the execution performance of load/store operations. The speculative scheduling proposed in this thesis not only predicts data dependencies of load operations, but also predicts the possibilities of data forwarding and data addresses of load operations. Thus, a load can speculatively bypass stores or be speculatively forwarded data directly from the store that might be depended upon before the address generation and true data dependency checking. Our model speculatively executes load operations much earlier than the proposed methods of data dependency speculation under precise prediction. Finally, the selective design is proposed to reduce mis-speculations.
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FU, CHUN-KUEI, and 傅俊貴. "Investigation of direct load control scheduling." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/93253919498978836970.
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Hwang, Hui-yue, and 黃慧瑜. "An X86 Load/store Unit with Aggressive Scheduling of Load/store Operations." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/53375158433004818687.
Full textAbstract:
碩士國立交通大學
資訊工程學系
86
Superscalar microarchitecture is broadly used in the modern microprocessors to execute multiple instructions in parallel. Because of limited register set and register-memory instruction set architecture, there are significant amounts of memory access instructions in x86 microprocessors. To explore more ILP of memory access operations becomes an important issue for the design of x86 microprocessors. The design of load/store units in the existed superscalar microprocessors is always restricted on the conservative scheduling of load/store operations. True data dependency is checked before issuing to the data cache. Only load bypassing or forwarding is provided in the scheduling policy of the load/store units. As higher degrees of instruction issuing are provided, more aggressive scheduling policies for load/store operations are required. In this thesis, the proposed speculation model, which allows load operations to precede the previous unsolved pending store operations, has the best performance gain in comparison with in-order, load bypassing and forwarding models. And, the additional traffic to the datacache caused by the true data dependency is always below 5.5%. The speculation model enlarges the execution window of load/store operations and gains the profit as the window size grows. In addition, the pipeline stages of the speculation model can be further reduced to gain more performance improvement.
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Lin, Chiao-Win, and 林巧雲. "Dynamic Load Route Scheduling Problem in Tandem AGVS." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/15065740760101931382.
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Simon, Michał. "Fault tolerant data acquisition through dynamic load scheduling." Rozprawa doktorska, 2013. https://repolis.bg.polsl.pl/dlibra/docmetadata?showContent=true&id=21532.
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Simon, Michał. "Fault tolerant data acquisition through dynamic load scheduling." Rozprawa doktorska, 2013. https://delibra.bg.polsl.pl/dlibra/docmetadata?showContent=true&id=21532.
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Erasmus, Dawid J. "Load scheduling with cogeneration and real time pricing." Diss., 2000. http://hdl.handle.net/2263/30443.
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The increase in energy costs, restructuring of electricity supply industry, quality of supply standards and environmental responsibility, initiated an investigation to utilize available carbon monoxide for cogeneration at an industrial site. A literature study indicated that most of the elements involved in establishing a cogeneration plant, were investigated individually, but had not been evaluated as a system with an indication of their relationships. The main objective of this study was to create a methodology to evaluate the impact of load scheduling, cogeneration and electrical tariff structures on the energy cost of an industrial operation. A modelling methodology was developed to evaluate the requirements for each of the elements, which were identified as the following processes: Plant and stockpiles Gas and fuel Power generation technology Electricity tariffs Financial evaluation Each of the processes' input requirements were evaluated in terms of being sufficient in providing either useful information or a model from which information could be manipulated. This methodology was then applied to a titanium slag producer with electric arc furnaces and excess carbon monoxide, which was burnt and treated as waste.Dissertation (M Eng (Electrical Engineering))--University of Pretoria, 2006.
Electrical, Electronic and Computer Engineering
unrestricted
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Maharaj, Yajna. "Modelling electricity load scheduling and retailer decision-making." Diss., 2016. http://hdl.handle.net/2263/57491.
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Electricity is critical to the economic and social development of humanity. Significant effort has been spent on the effective management thereof and with the growth of the renewable energy sector, traditionally regulated markets are no longer sufficient. This has resulted in privatization of the sector over the last three decades, and has largely been met with success internationally. South Africa however, continues to suffer rolling black-outs and rising energy costs. Many attribute this to the closed system under which the country operates.
In order for there to be sufficient buy-in for policy-change, key stakeholders such as the consumer and retailer must be made aware of the new reality under which they would operate, the factors that would affect their interests, and the extent to which they would be affected. Furthermore, the conflicting objectives of these parties must also be addressed through their simultaneous achievement, taken to be social welfare.
This dissertation satisfied these aims by creating an accurate depiction of the locally unique consumer and retailer?s realities through the development of operations research models. The resident?s problem was modelled as a load scheduling one which considered the vastly divergent socio-economic status of South Africans and how this affects their energy consumption patterns. The spot market dynamics that a retailer is confronted with was modelled as a three-regime Markov switching model. Because social welfare was the overwhelming interest of the study, a novel problem formulation was proposed to combine the resident?s interests in reducing bill payments and inconvenience levels, and the utility?s interest in increasing revenues.
The developed models and problem formulation were applied to South African scheduling data for residents operating under a fixed rate tariff. It was found that, under the guidelines of Eskom?s pricing boundaries, the relationship of the consumer?s price elasticity relative to the retailer was not a linear one. Social welfare was found to be a function of this relationship, and static tariffs that achieved optimal social welfare at varying degrees of relative price elasticity were identified. It was noted that insufficient
research has been conducted on validating the effect of the retail tariff on the resident and utility. Furthermore, this effect varies from one society to the next and is dependent on factors such as consumer attitudes and electricity profit margins.
Time-varying tariffs increased model complexity but are capable of achieving demand response which are believed to broaden the interests of the retailer and consumer. A trail-and-error algorithm was proposed as an appropriate tool for demonstrating the effects of demand responsiveness under a time-of-use (TOU) tariff. This was applied to the South African context with the inclusion of the novel problem formulation.
The novelty of this thesis is four-fold: firstly, a problem formulation that captures social welfare, which has previously not been considered in literature, is proposed. Secondly, the assumption of most works in this field that the effect of retail tariff changes on the consumer and retailer are the same, is disproved. In fact, this relative sensitivity is shown to be far greater for the resident than for the utility. Thirdly, a three-regime Markov switching model is successfully applied to the Australian market with no restrictions on the transition probability matrix. Finally, initial computations for this unique perspective on the problem are conducted with a trial-and-error algorithm and findings will certainly assist in guiding future research.Dissertation (MEng)--University of Pretoria, 2016.
tm2016
Industrial and Systems Engineering
MEng
Unrestricted
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Wang, Ming-jun, and 王銘君. "Parallel Turning Machine Scheduling with Limited Power Load." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/02975429293028069508.
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碩士逢甲大學
工業工程與系統管理學系
101
Power has become indispensable part of modern life in 21st century, and overuse of energy often results in severe climate change and environmental pollution. In classical production scheduling problems for parallel machines, jobs are known and unchanged. However, energy issues are seldom considered for such the problems in most of the literatures. In this study, selection of proper machining parameters under an electrical load constrained in a shop floor is addressed for the parallel machine scheduling problem. The objective of the scheduling problem is to minimize makespan. A simple heuristic is proposed to determine the proper machining parameter for each turning operation. After setting the machining parameters for all the jobs, the processing times are then constant, a genetic algorithm (GA)-based approach is developed for solving this parallel machine scheduling problem.
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Arif, Hamdani, and Hamdani Arif. "Load balanced Energy Efficient Scheduling in Cloud Computing." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/7tp9t3.
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碩士長庚大學
資訊工程學系
104
Acquiring fast processing time for some specific tasks will most likely satisfy the users. In response, cloud providers need to accommodate it by provisioning efficient tasks allocation policy. On the other hand, cloud computing platform allows the providers to manage their physical resources smartly in order to save the power consumption for their own merit. This paper addresses a trade-off mechanism between the cloud providers to achieve their own objectives and the users to obtain the quality of service evenly. Hence, a virtual and physical machine load balancing scheme is proposed to satisfy both user and service provider sides.
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Chen, Yueh Ren, and 陳躍仁. "Prediction-Based Load Balancing in Heterogeneous Environment." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/13541281458387784872.
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碩士國立中山大學
資訊工程研究所
83
In heterogeneous distributed environment, load balancing is very important for overall system performance improvement. But load balancing is not a easy work. So in this thesis, we will use benchmark to normalize the difference between the systems and establish a prediction system to forecast the future load variation by some techniques like linear regression and pattern composition. This system will help users to decide the best host to execute their programs for the maximum utilization of all system resources. And it is hard to write distributed applications in heterogeneous environment, too. For this reason, we provide a transparency tool --- modified PVM, which scheduler has been improved by our prediction system to automatically balance the system load for more efficient system performance.
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Hung, Yu-Hsin, and 洪鈺欣. "Load Prediction and Contract Capacity Optimization Research." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/84796303164980464046.
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碩士國立臺灣大學
工程科學及海洋工程學研究所
99
In Taiwan, most industrial and commercial enterprises sign power contracts with Taiwan Power Company. Problems occur when deciding the capacity in contracts: the high power capacity leads to increase of total electronic consuming cost. However, if the power capacity is set low, consumers run the risk of high penalty when the actual consumption exceed. The aim of this thesis is to optimize power demand for Taiwanese industries through the model of forecast and optimization. This thesis presents a new combination method by using particle swarm optimization (PSO) to forecast the load capacity, and control uncertainty of forecasting with stochastic simulation. Then optimize the capacity of contract with the improved particle swarm optimization by query based learning (QBLPSO) [1] algorithm. There are two main purposes in this thesis. First, the proposed method will be compared with the other methods, and we analyze separately the forecast and optimization. Second, we make decision analysis framework for determining the optimal power contract capacity and an empirical study in real cases, which included the industry, the commerce. The load forecast has about 90% of accuracy in government units, 92% of accuracy in commerce, 90% of accuracy in manufacturing industry, and 85% of accuracy in service industry. And optimization model help user to save about $195,374 in government units, $4,031 in commerce, $30,978 in manufacturing industry, and $39,905 in service industry. Therefore, the result of experiment explain that this proposed method can help user efficiently to make appropriate contract capacity.
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Black, Jonathan D. "Load Hindcasting: A Retrospective Regional Load Prediction Method Using Reanalysis Weather Data." 2011. https://scholarworks.umass.edu/theses/666.
Full textAbstract:
The capacity value (CV) of a power generation unit indicates the extent to which it contributes to the generation system adequacy of a region’s bulk power system. Given the capricious nature of the wind resource, determining wind generation’s CV is nontrivial, but can be understood simply as how well its power output temporally correlates with a region’s electricity load during times of system need. Both wind generation and load are governed by weather phenomena that exhibit variability across all timescales, including low frequency weather cycles that span decades. Thus, a data-driven determination of wind’s CV should involve the use of long-term (i.e., multiple decades) coincident load and wind data. In addition to the challenge of finding high-quality, long-term wind data, existing load data more than several years old is of limited utility due to shifting end usage patterns that alter a region’s electricity load profile. Due to a lack of long-term data, current industry practice does not adequately account for the effects of weather variability in CV calculations. To that end, the objective of this thesis is to develop a model to “hindcast” what the historic regional load in New England would have been if governed by the conjoined influence of historic weather and a more current load profile. Modeling focuses exclusively on summer weekdays since this period is typically the most influential on CV.
The summer weekday model is developed using multiple linear regression (MLR), and features a separate hour-based model for eight sub-regions within New England. A total of eighty-four candidate weather predictors are made available to the model, including lagged temperature, humidity, and solar insolation variables. A reanalysis weather dataset produced by the National Aeronautics and Space Administration (NASA) – the Modern Era Retrospective-Analysis for Research and Applications (MERRA) dataset – is used since it offers data homogeneity throughout New England over multiple decades, and includes atmospheric fields that may be used for long-term wind resource characterization. Weather regressors are selected using both stepwise regression and a genetic algorithm(GA) based method, and the resulting models and their performance are compared. To avoid a tendency for overfitting, the GA-based method employs triple cross-validation as a fitness function. Results indicate a regional mean absolute percent error (MAPE) of less than 3% over all hours of the summer weekday period, suggesting that the modeling approach developed as part of this research has merit and that further development of the hindcasting model is warranted.