The study of reducing the cost of investment in wind energy based on the cat swarm optimization with high reliability

Wind and solar are the most important source of renewable energy for power supply in remote locations involves serious consideration of the reliability of these unconventional energy sources. We apply the cat swarm meta-heuristic optimization method to solve the problem of wind power system design optimization. The electrical power components of the system are characterized by their cost, capacity and reliability. This study seeks to optimize the design of parallel power systems in which multiple choices of generators wind, transformers and lines. Our plan has the advantage of allowing electrical components with different parameters to be customized in electrical power systems. The UMGF method is applied to allow rapid reliability estimation. A computer program is developed for the UMGF application and CS algorithm. An example is provided to explain

HSO: A Hybrid Swarm Optimization Algorithm for Re-Ducing Energy Consumption in the Cloudlets

Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made

HSO: A hybrid swarm optimization algorithm for reducing energy consumption in the cloudlets

Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made

Resource Management In Cloud And Big Data Systems

Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field

Frontiers In Operations Research For Overcoming Barriers To Vehicle Electrification

Electric vehicles (EVs) hold many promises including diversification of the transportation energy feedstock and reduction of greenhouse gas and other emissions. However, achieving large-scale adoption of EVs presents a number of challenges resulting from a current lack of supporting infrastructure and difficulties in overcoming technological barriers. This dissertation addresses some of these challenges by contributing to the advancement of theories in the areas of network optimization and mechanism design. To increase the electric driving range of plug-in hybrid electric vehicles (PHEVs), we propose a powertrain energy management control system that exploits energy efficiency dif- ferences of the electric machine and the internal combustion engine during route planning. We introduce the Energy-Efficient Routing problem (EERP) for PHEVs, and formulate this problem as a new class of the shortest path problem. We prove that the EERP is NP-complete. We then propose two exact algorithms that find optimal solutions by exploiting the transitive structure inherent in the network. To tackle the intractability of the problem, we proposed a Fully Polynomial Time Approximation Scheme (FPTAS). From a theoretic perspective, the proposed two-phase approaches improve the state-of-the-art to optimally solving shortest path problems on general constrained multi-graph networks. These novel approaches are scalable and offer broad potential in many network optimization problems. In the context of vehicle routing, this is the first study to take into account energy efficiency difference of different operating modes of PHEVs during route planning, which is a high level powertrain energy management procedure. Another challenge for EV adoption is the inefficiency of current charging systems. In addition, high electricity consumption rates of EVs during charging make the load manage- ment of micro grids a challenge. We proposed an offline optimal mechanism for scheduling and pricing of electric vehicle charging considering incentives of both EV owners and utility companies. In the offline setting, information about future supply and demand is known to the scheduler. By considering uncertainty about future demand, we then designed a family of online mechanisms for real-time scheduling of EV charging. A fundamental problem with significant economic implications is how to price the charging units at different times under dynamic demand. We propose novel bidding based mechanisms for online scheduling and pricing of electric vehicle charging. The proposed preemption-aware charging mechanisms consider incentives of both EV drivers and grid operators. We also prove incentive-compatibility of the mechanisms, that is, truthful reporting is a dominant strategy for self-interested EV drivers. The proposed mechanisms demonstrate the benefits of electric grid load management, revenue maximization, and quick response, key attributes when providing online charging services

Resource Management In Cloud And Big Data Systems

Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field