Design and Implementation of Power Dispatching Management System Based on Cloud Platform

In this paper, we put forward a basic taxonomy of cloud computing architectures. By this taxonomy, cloud computing architectures are essentially subdivided into Cloud Platform Architecture (CAA) and Cloud Application Architecture (CAA) which are linked via the cloud services available in the marketplace of Information Technology (IT) capabilities. We elaborate the constructs of CPA and CAA, respectively. Such a division between CPA and CAA is fundamental for cloud computing to serve as a potential foundation for delivering IT services as utilities over the Internet.

The paper deals with the problem of decision-making support for digital production planning. The analysis of the stages of organization and production planning currently is poorly represented in existing software solutions. Main stages of digital production planning are identified such as optimization of manufacturing placement; optimization of suppliers selection of resources and its delivery to the place of manufacturing. Taking into account the conditions of the modern stage of development of technologies for the digital production, we can identify the key requirements for the functionality and use of the software platform. The formalization (ontology) based on the approach of production and operational management and the architecture of a digital cloud platform and its functional modules has been developed for the digital production planning process. It is a decentralized platform as a public network consisting of independent software modules (cloud services), connected into a tool with expandable number of modules. The software tool consists of production-planning unit and production-management support unit. Currently, a number of modules of the system have been developed and experimentally tested; now we are developing the full-featured tool.

Based on the flight big data environment, the existing problems of current flight training management are analyzed. This paper designs the cloud platform architecture based on flight big data, and points out the key technology research that should be carried out under this architecture. With the help of this cloud platform, flight big data can be effectively analyzed and processed to provide decision support for flight training management, flight safety assessment and risk early warning.

With the rapid growth of biological technology, large amount of biological data can be produced in few days or months. Many of common-used tools become computation-consuming in analyzing such big biological data. Cloud computing has emerged to provide the huge amount of computing power and play important role in development of bioinformatics tools. We propose a cloud computing framework that is able to easily deploy the bioinformatics tools on cloud virtualization platform based on Hadoop. This framework can work on the public cloud platform vendor such as Amazon EC2 and also private cloud platform. All the tools performed by cloud computing framework as Bioinformatics as a Services are available at http://bioinfo.cs.pu.edu.tw/CBBTS. The tools deployed on cloud platform by the proposed framework are tested in Providence University cloud platform and provided good proportional acceleration when scaled out onto many computational units. In the big biological data era, cloud computing based solutions are important role to develop bioinformatics services over internet. In the work, the proposed framework is able to simply deploy several well-known bioinformatics tools on cloud virtualization platform and as web services. This framework can work on the public cloud platform vendor such as Amazon EC2 and also private cloud platform.

The term ‘cloud computing’ was initially coined to refer to on-demand computing services that were offered by commercial service providers such as Amazon, Google, and Microsoft. Cloud computing is an emerging computation model that shares resources over the Internet and provides on-demand facilities to its customers. In other words, it can be seen as a computation platform combined with shared computer processing resources over the Internet. Computing resources can be shared from anywhere using Internet connectivity. Thus, cloud computing enables ubiquitous and on-demand access to a shared pool of resources to meet users’ requirements [1]. Figure 2.1 depicts an overview of the cloud computing technology. As depicted in Figure 2.1, cloud computing consists of several shared computer resources, such as computing devices, software, and applications, which are accessed in an on-demand basis. Additionally, different consumers, who access the cloud platform through mobile phones, laptops, desktops, and other peripheral devices, are also present in the cloud computing technological framework. All the resources and consumers are linked through Internet connectivity. The entire framework is part of a cloud platform. Therefore, a cloud platform helps its users to store and process their data in data centers. The important feature of cloud computing is that a central management entity controls all operations associated with different users situated in different geographical regions. Thus, multiple agents can participate in a single platform to share/procure resources in an on-demand basis. Figure 2.2 shows an abstracted view of the cloud computing technology. The cloud manager maps the incoming requests with the available resources, which are shared with the cloud manager. So, it is evident that the cloud manager only manages the available resources and incoming requests in an optimum manner, so that the users of the cloud-services can get seamless and ubiquitous services. However, the cloud-service subscribers do not know from which hardware/software resources, the requested services are provided. Similarly, the owners of the shared resources also do not know to whom their resources are mapped. Therefore, although a common platform is used for multiple parties, an abstraction level is always used for security and privacy concerns. As evident from Figure 2.2, there are multiple parties that participate in the resource sharing/procuring/managing process – publisher, subscriber, and manager. The publishers publish their shared resources to the cloud-service providers.

This paper is based on the review of cloud computing in educational field. The technology provides IT-level facilities with services-on-demand-based feature, i.e., cloud computing, one of the growing technologies in today’s era. The reason why all IT and educational societies are moving toward cloud computing is it offers such kinds of services which help to reduce the initial investment and take back the maximum output in terms of processing power, storage, and networking. But, for using cloud computing in educational society, there are two ways: First is taking services from third parties like Amazon, IBM, and so on. Second is developing our own cloud platform within the educational society by using commodity hardware or by current working systems in that society. Many educational hubs invested already on their own infrastructure instead of buying from third parties and using same services and same features without paying too much money. This paper also focuses on the how some educational societies are currently working on the cloud platform and why other schools, colleges, and universities must move toward cloud platform with proper data security assurance. To use this cloud services freely, there are some open-source platforms currently present in the market. This research is based on finding better private cloud platform for educational societies based on their performance.

Recent years have seen a significant increase in cloud computing simulation tools. They are essential to evaluate cloud computing services and applications. Indeed, they can help to test, evaluate, and replicate the assumptions cost-effectively before a cloud platform is deployed in a real environment. It also provides an option to change and control the bottlenecks of the performance prior to implementing on an actual cloud platform. The simulation tools for cloud computing are also important to help users choose which cloud computing service is suitable for their requirements as deploying and operating a cloud platform in a real environment are often expensive, time consuming, or even impractical due to multiple test runs in different conditions and requirements. This paper surveys the various simulation platforms and tools of cloud computing. We identify the characteristics of the tools highlighting their strengths and weaknesses. Finally, the paper notes the challenges and the future research directions.

Software testing has become an essential indicator of students' professional level. To improve students' software testing ability, the construction of software testing laboratory has become an urgent need for many institutions. With the development and popularization of cloud computing, more and more software industries rely on cloud computing, which also brings new ideas for the construction and development of software testing laboratories in colleges and universities. This paper designs and implements a software testing laboratory based on a cloud platform, which can reuse the traditional hardware devices such as the servers in the laboratory. It is an intelligent and convenient laboratory environment with rich functions and powerful management functions, such as antagonistic experiments, programming communities, developer testing, and so on. Students can also connect to the cloud platform through the campus network to carry out various experiments. The experimental results of students will be stored on the cloud platform to facilitate the management of teachers, which can effectively improve the level of resource utilization of university laboratories. Through the cloud platform, we can arouse the enthusiasm of students and improve their professional skills, and improve the quality of experimental teaching in colleges and universities as well.

Cloud computing is emerging as an alternative computing platform to bridge the gap between scientists' growing computational demands and their computing capabilities. A scientist who wants to run HPC applications can obtain massive computing resources 'in the cloud' quickly (in minutes), as opposed to days or weeks it normally takes under traditional business processes. Due to the popularity of Amazon EC2, most HPC-in-the-cloud research has been conducted using EC2 as a target platform. Previous work has not investigated how results might depend upon the cloud platform used. In this paper, we extend previous research to three public cloud computing platforms. In addition to running classical benchmarks, we also port a 'full-size' NASA climate prediction application into the cloud, and compare our results with that from dedicated HPC systems. Our results show that 1) virtualization technology, which is widely used by cloud computing, adds little performance overhead; 2) most current public clouds are not designed for running scientific applications primarily due to their poor networking capabilities. However, a cloud with moderately better network (vs. EC2) will deliver a significant performance improvement. Our observations will help to quantify the improvement of using fast networks for running HPC-in-the-cloud, and indicate a promising trend of HPC capability in future private science clouds. We also discuss techniques that will help scientists to best utilize public cloud platforms despite current deficiencies.

The ever-changing nature of IT demands a changing role for both system administrators and database administrators. Being system admin personnel will require more knowledge, broader skillset, and more tools on hand to be able to adapt to the cloud computing culture. The cloud computing has changed the way for IT Operations. The future system administrator will thus have to help configure, implement and manage the applications that run on cloud platforms. The same thing happens to database administrators as well. The role of DBAs will shift to cloud DBAs, and adding to their responsibilities will manage the cloud based database and NoSQL databases in serverless settings. Cloud DBAs will have to deal with data protection and play a key role in providing data compatibility across the cloud platforms and maybe migrating data storage from one cloud service provider to another. In short DBAs role moved from tactical infrastructure caretakers towards a more strategic role of being an advisor for other developers. In this poster, we describe changes of roles of system administrators and database administrators in the era of cloud computing.

Cloud computing has gradually evolved into an infrastructural tool for a variety of scientific research and computing. It has become a trend that lots of products have been migrated from local servers to cloud by many institutions and organizations. One of the challenges in cloud computing now is how to run software efficiently on cloud platforms since lots of original codes are not capable of being executed in parallel on cloud contexts, resulting in that the power of clouds cannot be exerted well. It is costly to redesign and convert current sequential codes into cloud platform. Thus, automatic translation from sequential code to cloud code is one of the directions that could be taken to resolve the problem of code migration in cloud infrastructure. In this paper, a new Java to MapReduce (J2M) translator is developed to achieve the automatic translation from sequential Java to cloud for specific data-parallel code with large loops. This paper will provide details about the design of our translator and evaluate our performance through experiments. The experimental results not only indicate that the translator can precisely translate the sequential Java into cloud codes, but also show that it can achieve very good speedup in performance, and we expect that an almost linear speedup is possible if larger enough data is processed. It is believed that the J2M translator is an ideal stereotype for code migration and will play an important role in the transition era of cloud computing.

Smart grids have various capabilities to meet the electricity demands of modern society in production and life, and real-time monitoring of smart grids is critical to enhance the reliability and operational efficiency of power utilities. With the development of artificial intelligence technology and cloud computing, several studies have proposed using the powerful computing ability of clouds to design fault detection systems based on deep learning. However, due to the transmission delay in the Internet backbone and the large amount of data uploaded to the system, various problems arise such as a large bandwidth load and poor real-time feedback from the cloud platform. To solve these problems, it is necessary to embed the artificial intelligence technology at the edge of a network to realize a de-centralized system. In this paper, we propose an edge computing assisted smart grid fault detection system that uses an embedded lightweight neural network device, which is placed near the edge of the monitored equipment to realize real-time monitoring. In addition, considering the limited communication resources, relatively low computation capabilities, and different monitoring accuracies of edge devices, we design an optimal allocation method for communication and computation resources, which can maximize the throughput of the system, and improve the resource utilization of the system while meeting the requirements of data transmission and delay processing. Finally, simulation experiments are carried out to show that compared with other structures of smart grid fault detection systems, our proposed system can transmit more data while meeting the requirements of the delay bound, reduce the time required for transmission, and enhance the real-time performance of smart grid fault detection systems.

Nowadays, as a result of the adoption of new Internet technologies like cloud computing and emerging business models, organizations are able to establish collaborative networks for executing Collaborative Business Processes (CBPs) in a flexible way. Current approaches of Process-Aware Information Systems (PAISs) for implementing and executing CBPs have shortcomings, not only related to the services offered, but also about issues typical of the technological solution chosen, such as portability, elasticity, and privacy when they are cloud-based. Portability refers to the dependency that is created due to the heterogeneity of the services offered by different cloud providers, elasticity defines the degree to which a system is able to adapt to workload changes by provisioning and de-provisioning resources in an autonomic manner, and privacy refers to sensitive information that is expected to be hidden from others. Then, the purpose of this work is to define an adequate cloud architecture for a CBPs management platform which faces these issues. To do so, starting from a definition of a cloud platform architecture that solves (almost all) the shortcomings for the CBPs services offered, an enhancement making use of the microservices paradigm is proposed, overcoming the identified difficulties.

With the rapid development of the Internet and cloud computing applications, the construction of a digital library in the cloud computing environment has been put on the agenda. Construction and management of digital libraries using cloud computing technology, can effectively utilize resources, reduce costs, achieve better information sharing, and improve the service function of the library. In this paper, studies digital library based on cloud computing. Firstly, introduces the characteristics and establish principles of digital library based on cloud computing; secondly, elaborated the concept and work principle of cloud computing; then, describes the key technology of cloud computing, and a detailed introduction of virtual storage technology in digital library; and finally introduced cloud computing digital library service model, including cloud software as a service layer (SaaS), cloud platform as a service layer (PaaS), cloud infrastructure as a service layer (IaaS).

Cloud computing has different meaning to different people, the privacy and security issues also differ between a consumer using a public cloud application, a medium-sized Company using a customized Design of business on a cloud platform, and Some Companies are using Platform on Public level which are Public to Public Network The security requirements in cloud computing environment is to find the Security threats in the Structure of clouds To find the security solutions, and finding basis so that Pre Security Step Should be taken in concerned with security future model. In this paper is to build a trusted computing environment for cloud computing system by Combining the trusted computing platform into cloud computing system Which is free from attacks and threats and system is designed with a model system in which cloud computing system is combined with trusted computing platform and trusted platform models. Keywords: cloud computing, data breaches, Api Attack Deniel Attack, Account high Jacking, Deniel of Services