Highly scalable algorithms for scheduling tasks and provisioning machines on heterogeneous computing systems

Includes bibliographical references.2015 Summer.As high performance computing systems increase in size, new and more efficient algorithms are needed to schedule work on the machines, understand the performance trade-offs inherent in the system, and determine which machines to provision. The extreme scale of these newer systems requires unique task scheduling algorithms that are capable of handling millions of tasks and thousands of machines. A highly scalable scheduling algorithm is developed that computes high quality schedules, especially for large problem sizes. Large-scale computing systems also consume vast amounts of electricity, leading to high operating costs. Through the use of novel resource allocation techniques, system administrators can examine this trade-off space to quantify how much a given performance level will cost in electricity, or see what kind of performance can be expected when given an energy budget. Trading-off energy and makespan is often difficult for companies because it is unclear how each affects the profit. A monetary-based model of high performance computing is presented and a highly scalable algorithm is developed to quickly find the schedule that maximizes the profit per unit time. As more high performance computing needs are being met with cloud computing, algorithms are needed to determine the types of machines that are best suited to a particular workload. An algorithm is designed to find the best set of computing resources to allocate to the workload that takes into account the uncertainty in the task arrival rates, task execution times, and power consumption. Reward rate, cost, failure rate, and power consumption can be optimized, as desired, to optimally trade-off these conflicting objectives

Short-Term Resource Allocation and Management

Almost all sectors of the economy, such as, government, healthcare, education, ship repair, construction, and manufacturing require project management. A key component of project management deals with scheduling of tasks such that limited resources are utilized in an effective manner. Current research on resource constrained project-scheduling has been classified as: a) Single project with single mode for various tasks, b) Single project with multiple task modes, c) Multiple projects with single task mode, and d) Multiple projects with multiple task modes.;This work extends the current multi-project, multi-mode scheduling techniques. The resources can be renewable, and non-renewable. In addition, it focuses on short term scheduling, that is, scheduling on an hourly, daily, or weekly basis. Long term scheduling assumes a stable system, that is, resources, priorities, and other constraints do no change during the scheduling period. In this research, short term scheduling assumes a dynamic system, that is, resources, priorities, and other constraints change over time.;A hybrid approach is proposed to address the dynamic nature of the problem. It is based on discrete event simulation and a set of empirical rules provided by the project manager. The project manager is assumed to be highly knowledgeable about the project. He/she is regarded as an integral part of the system. Such an approach is better suited to deal with real world scheduling. The proposed approach does not seek to provide a single optimum solution, instead, it generates a series of feasible solutions, along with the impact of each solution on schedule and cost.;Two project case studies dealing with finding an optimum solution were selected from the literature. The proposed technique was applied to the data set in these studies. In both cases the proposed approach found the optimum solution. The model was then applied to two additional problems to test the features that could not be tested on the dataset from the literature.;As for practical implications, the proposed approach enhances the decision making process, by providing more resource allocation flexibility, and results in improved solutions in terms of total project duration and cost. From an academic viewpoint, this research enriches the existing literature, as it provides an extension of the resource constrained project scheduling problems, a discrete event simulation and four cases studies which highlights relevant issues to model properly the complexity of real-life projects

Scheduling of unloading, inventory and delivery operations at an LNG receiving terminal

Master'sMASTER OF ENGINEERIN

시장환경에서 마이크로그리드 계통연계 상태의 확률론적 분석에 기반한 최적 예비력 스케쥴링에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 윤용태.Microgrid can be a useful entity to support stable and efficient operation of power systems with large-scale penetration of distributed generators, such as wind generators, energy storage systems, and combined heat and power plant. One major characteristics of microgrid is that it could take an island operation and maintain its reliable power supply if an accident occurred in the main grid. However, microgrid operator (MGO) cannot help taking some special action like load shedding during the island operation, since its generation capability has a limit. Therefore, MGO has to take this island operation into account when it make a plan for its own energy resources. Actually many prior researches about microgrid operation include reserve power scheduling in preparation for the uncertain islanding event. This dissertation analyses the risk of microgrid island operation, and describes the method that enables MGO to reflect this probabilistically into its operating cost when it makes a plan for the energy resources. In order to this, an islanding event of the microgrid is interpreted as a transaction suspension, and microgrid islanding rule is defined in the form of market rule to clarify the responsibility distribution of a contract breach in a market. To quantitatively examine the influence of market rule, different two microgrid islanding rules are proposed based on the Power Exchange for Frequency Control (PXFC) market, which was devised by M. Ilic et al. Postulating these two rules, the risk of microgrid island operation is examined. In other words, the triggering condition of islanding event is mathematically formulated, and microgrid islanding probability (MIP), which represents the probability of being in the islanded state during a unit time, is proposed and calculated. Utilizing the proposed MIP index, an optimization problem is constructed. The objective function is expected value of daily operating cost of microgrid, which include the risk of microgrid island operation, and the decision variable is the purchase capacity of reserve band in PXFC market. The optimization problem is solved and simulated with the market information of the PJM electricity market. The effectiveness of the proposed reserve scheduling method in terms of operating cost is investigated using simulations, where the proposed method and two further methods are applied to microgrids with different generation capabilities. Also simulation results of MIP analysis show that microgrid island operation has some hysteresis characteristics. Utilizing the proposed method, MGO can schedule its reserve power corresponding the market and grid conditions.Chapter 1 Introduction 1 1.1 Background 1 1.2 Previous researches 3 1.3 Objectives of the dissertation 5 1.4 Overview of the dissertation 6 Chapter 2 Microgrid Operation and Market 8 2.1 Electricity Market in Microgrid Environment 8 2.2 Power Exchange for Frequency Control Market 10 2.3 Microgrid Islanding Rule A 13 2.4 Microgrid Islanding Rule B 16 Chapter 3 Optimal Operation Strategy by Microgrid Operator for Microgrid Islanding Rule A 20 3.1 Objective Function and Problem Formulation for Rule A 20 3.2 Defining the Cost Functions for Rule A 22 3.3 Microgrid Islanding Model for Rule A 24 3.4 Formulating Microgrid Islanding Probability for Rule A 27 Chapter 4 Numerical Simulation for Microgrid Islanding Rule A 31 4.1 Simulation Settings 31 4.2 Simulation Results 35 Chapter 5 Optimal Operation Strategy by Microgrid Operator for Microgrid Islanding Rule B 42 5.1 Objective Function and Problem Formulation for Rule B 42 5.2 Defining the Cost Functions for Rule B 43 5.3 Microgrid Islanding Model for Rule B 45 5.4 Formulating Microgrid Islanding Probability for Rule B 48 Chapter 6 Numerical Simulation for Microgrid Islanding Rule B 53 6.1 Simulation Settings 53 6.2 Simulation Results 56 Chapter 7 Conclusions and Future Extensions 65 7.1 Conclusions 65 7.2 Future Extensions 67 Bibliographies 69 Appendix 74 Appendix A Common Definition of Microgrid 74 Appendix B Pattern Search Optimization 76 Appendix C Damages for Breach of Contract 77 Appendix D Reserve Scheduling of a Microgrid Considering Market Participation and Energy Storage System 80 국문초록 85Docto

Escalonamento ótimo baseado na teoria de controle supervisório aplicado a um estaleiro de reparo naval

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-graduação em Engenharia de Automação e Sistemas, Florianópolis, 2010A Teoria de Controle Supervisório (TCS) permite a síntese automática de supervisores não bloqueantes que habilitem todas e apenas as sequências que satisfaçam especificações de segurança para um sistema a eventos discretos temporizado. O supervisor ótimo que satisfaz as especificações de recursos, roteiros e prazos para problema do tipo jobshop contém todas as soluções de escalonamento possíveis. No entanto, o crescimento do número de estados dos modelos pode inviabilizar a solução para problemas reais. Nessa pesquisa, uma nova proposta de modelagem dos autômatos temporizados é desenvolvida com o objetivo de reduzir o tamanho dos modelos. Propõe-se também um algoritmo eficiente para síntese de escalonamento baseada na composição incremental dos roteiros de produção e prazos das tarefas e um método de bissecção para minimização do tempo de produção global e também dos tempos de produção de cada tarefa. Este método é aplicado a um estaleiro de reparo naval para o escalonamento das atividades nos cinco recursos principais para execução de dez obras distintas. Também foi desenvolvido um sistema que integra o planejamento da produção com uma ferramenta de síntese automática de supervisores para que o usuário não precise estar familiarizado com a TCS.The Supervisory Control Theory (SCT) allows automatic synthesis of nonblocking supervisors that ensures safety specifications to a timed discrete event system. The optimal supervisory that ensures the resources specifications, production routers, and due dates to the problem of jobshops provides all the possible solutions of scheduling. However, the size of the state space of the models can make impracticable the solution of such a problem. In this dissertation, a new modeling approach is proposed for the timed automata models in order to expressively reduce the size of the models. Also, it is proposed an efficient algorithm for the optimal schedules based on an incremental synthesis of the production routers and due dates. A method of bisection was developed to minimize of total production time and the lead times of jobs as well. This method is applied to a repair shipyard to schedule its activities in the five main resources and ten orders. From the research was developed a system that integrates the production planning with a tool of automatic synthesis of supervisors in order to make the interface an easier place for those users who are not used to SCT

Scheduling transshipment operations in maritime chemical transportation

Master'sMASTER OF ENGINEERIN

Maximum Profit Scheduling

The classical scheduling literature considers many problems where a given set of jobs must be processed at minimum cost, subject to various resource constraints. The literature only considers the issue of revenue generation in a very limited way, by allowing a job to remain unprocessed and its revenue contribution to be lost. By contrast, we consider three diverse practical situations where efficient scheduling affects revenue in much more general and realistic ways. First, we study two make-to-order environments where efficient scheduling increases customer goodwill, thus stimulating demand in different ways. Second, we study two make-to-stock environments where efficient scheduling creates inventory, thus also stimulating demand in different ways. Third, we study new product markets where efficient scheduling leads to a company becoming the first mover, and thus acquiring a larger market share. In each case, we provide both a computationally efficient algorithm for scheduling and a proof that a much more efficient algorithm is unlikely to exist. For both the make-to-stock and make-to-order problems, we also describe heuristic approaches that are easy to implement, and we study their average performance. The results show that substantial benefits arise from considering the implications of efficient scheduling for revenue and net profit. The practical impact of our work is to demonstrate the importance of efficient scheduling, not only in controlling cost, but also in increasing revenue and net profit.manufacturing, scheduling, profit maximization, make-to-stock, make-to-order, algorithms, heuristics