A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

Intelligent integrated maintenance for wind power generation

A novel architecture and system for the provision of Reliability Centred Maintenance (RCM) for offshore wind power generation is presented. The architecture was developed by conducting a bottom-up analysis of the data required to support RCM within this specific industry, combined with a top-down analysis of the required maintenance functionality. The architecture and system consists of three integrated modules for Intelligent Condition Monitoring, Reliability and Maintenance Modelling, and Maintenance Scheduling that provide a scalable solution for performing dynamic, efficient and cost effective preventative maintenance management within this extremely demanding renewable energy generation sector. The system demonstrates for the first time, the integration of state-of-the-art advanced mathematical techniques: Random Forests, Dynamic Bayesian Networks, and Memetic Algorithms in the development of an intelligent autonomous solution. The results from the application of the intelligent integrated system illustrated the automated detection of faults within a wind farm consisting of over 100 turbines, the modelling and updating of the turbines’ survivability and creation of a hierarchy of maintenance actions, and the optimising of the maintenance schedule with a view to maximising the availability and revenue generation of the turbines

Joint Condition-based Maintenance and Condition-based Production Optimization

Developments in sensor equipment and the Internet of Things increasingly allow production facilities to be monitored and controlled remotely and in real-time. Organizations can exploit these opportunities to reduce costs by employing condition-based maintenance (CBM) policies. Another recently proposed option is to adopt condition-based production (CBP) policies that control the deterioration of equipment remotely and in real-time by dynamically adapting the production rate. This study compares their relative performance and introduces a fully dynamic condition-based maintenance and production (CBMP) policy that integrates both policies. Numerical results show that the cost-effectiveness of the policies strongly depends on system characteristics such as the planning time for maintenance, the cost of corrective maintenance, and the rate and volatility of the deterioration process. Integrating condition-based production decisions into a condition-based maintenance policy substantially reduces the failure risk, while fewer maintenance actions are performed. Interestingly, in some situations, the combination of condition-dependent production and maintenance even yields higher cost savings than the sum of their separate cost savings. Moreover, particularly condition-based production is able to cope with incorrect specifications of the deterioration process. Overall, there is much to be gained by making the production rate condition-dependent, also, and sometimes even more so, if maintenance is already condition-based. These insights provide managerial guidance in selecting CBM, CBP, or the fully flexible CBMP policy

A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance

Increasing energy price and requirements to reduce emission are new chal-lenges faced by manufacturing enterprises. A considerable amount of energy is wasted by machines due to their underutilisation. Consequently, energy saving can be achieved by turning off the machines when they lay idle for a comparatively long period. Otherwise, turning the machine off and back on will consume more energy than leave it stay idle. Thus, an effective way to reduce energy consumption at the system level is by employing intelligent scheduling techniques which are capable of integrating fragmented short idle periods on the machines into large ones. Such scheduling will create opportunities for switching off underutilised resources while at the same time maintaining the production performance. This paper introduces a model for the bi-objective optimisation problem that minimises the total non-processing electricity consumption and total weighted tardiness in a job shop. The Turn off/Turn on is applied as one of the electricity saving approaches. A novel multi-objective genetic algorithm based on NSGA-II is developed. Two new steps are introduced for the purpose of expanding the solution pool and then selecting the elite solutions. The research presented in this paper is focused on the classical job shop envi-ronment, which is widely used in the manufacturing industry and provides considerable opportunities for energy saving. The algorithm is validated on job shop problem instances to show its effectiveness. Keywords: Energy efficient production plannin

Study on bridge inspections, A: identifying barriers to new practices and providing strategies for change

2021 Summer.Includes bibliographical references.Bridge inspections are one of the key elements required for a successful bridge management process to ensure adequate bridge performance. Inspections significantly inform maintenance decisions and can help in managing maintenance activities to achieve a reliable bridge network. In the United States (U.S.) routine visual inspections are required for most bridges at a maximum interval of 24-months regardless of the bridge condition. However, limitations of current bridge inspection practices impact the quality of information provided about bridge condition and the subsequent decisions made based on that information. Accordingly, the overarching goal of this research project is to support bridge inspection practices by providing a systematic and rational framework for bridge inspection planning and identifying the factors that can facilitate innovation and research transfer in the bridge inspection field. To do so, this dissertation includes three separate yet related studies; each focusing on essential aspects of bridge inspection planning. Much research in bridge inspection has been conducted to improve the inspection planning process. The first study provides an overview of current bridge inspection practices in the U.S. and conducts a systematic literature review on innovations in the field of bridge inspection planning to identify research gaps and future needs. This study provides a background on the history of bridge inspection in the U.S., including current bridge inspection practices and their limitations, and analyzes the connections between nondestructive evaluation techniques, deterioration models and bridge inspection management. The primary emphasis of the first study is a thorough analysis of research proposing and investigating different methodologies for inspection planning. Studies were analyzed and categorized into three main types of inspection planning approaches; methods that are based on: reliability, risk analysis, and optimization approaches. This study found that one of the main barriers that may be preventing the implementation of new inspection planning frameworks in practice is that the approaches presented focus on a single bridge element or deterioration mechanism in the decision-making process. Additionally, it was concluded that approaches in the literature are either complex to apply or depend solely on expert judgement. Limitations of the uniform calendar-based approach used to schedule routine inspections have been reported in the literature. Accordingly, the objective of the second study is to provide a new systematic approach for inspection planning that integrates information from bridge condition prediction models, inspection data, and expert opinion using Bayesian analysis to enhance inspection efficiency and maintenance activities. The proposed uncertainty-based inspection framework can help bridge owners avoid unnecessary or delayed inspections and repair actions, determine the inspection method, and consider more than one deterioration process or bridge component during the inspection planning process. The inspection time and method are determined based on the uncertainty and risks associated with the bridge condition. As uncertainty in the bridge condition reaches a defined threshold, an inspection is scheduled utilizing nondestructive techniques to reduce the uncertainty level. The framework was demonstrated on a new and on an existing reinforced concrete bridge deck impacted by corrosion deterioration. The results showed that the framework can reduce the number of inspections compared to conventional scheduling methods, while also reducing the uncertainty regarding the transition in the bridge deck condition and repair time. As identified through the first study, over the last two decades many researchers have focused on providing new ideas to improve conventional bridge inspection practices, however, little guidance is provided for implementing these new research products in practice. This, along with resistance to change and complexity of the proposed ideas, resulted in a lack of consistency and success in applying new technologies in bridge inspection programs across state departments of transportation (DOTs). Accordingly, the third paper presents a qualitative study set out to identify the factors that can help improve research products and accelerate change and research transfer in bridge inspection departments. This study used semi-structured interviews, written interviews, and questionnaires for data collection and engaged with twenty-six bridge staff members from different DOTs. The findings of this study are expected to be both specific to changes in bridge inspection practice and have some generalizability to other significant changes to engineering practice at DOTs. To improve research products, this study suggested that researchers need to collaborate more with DOT staff members and provide relevant research products that are not specific to certain bridge cases and can be applied on different bridges. Also, to facilitate change in transportation organizations, change leaders should focus on showing the need for change, gaining support from the FHWA, allocating the required resources, and enhancing the capacity of DOT staff members through training and effective communication. The investigation also presented participants' opinions on some of the aspects related to conventional inspection practices such as their support of a uniform inspection interval over a variable interval, and the main barriers limiting the use of NDE methods. This study contributes to the body of knowledge in the bridge inspection field by providing a new inspection planning approach that depends on the uncertainty and the risks associated with the bridge condition and uses both computational methods and expert judgment allowing bridge owners select inspection time and method while considering more than one deterioration process or bridge element. In addition, this study presents some of the factors that can help reduce the gap between research and practice and facilitate innovation and change in transportation organizations

Digital simulation of gas turbine performance

No abstract available

Wind Energy Management

The book "Wind Energy Management" is a required part of pursuing research work in the field of Renewable Energy at most universities. It provides in-depth knowledge to the subject for the beginners and stimulates further interest in the topic. The salient features of this book include: - Strong coverage of key topics - User friendly and accessible presentation to make learning interesting as much as possible - Its approach is explanatory and language is lucid and communicable - Recent research papers are incorporate