Performance characterization of hadoop workloads on SR-IOV-enabled virtualized InfiniBand clusters

Cloud computing and big data: Technologies and applications

Analysis of SR-IOV in Docker containers using RTT measurements

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.

The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data problems using HPCC, an open-source computing platform. The book comprises 15 chapters broken into three parts. The first part, Big Data Technologies, includes introductions to big data concepts and techniques; big data analytics; and visualization and learning techniques. The second part, LexisNexis Risk Solution to Big Data, focuses on specific technologies and techniques developed at LexisNexis to solve critical problems that use big data analytics. It covers the open source High Performance Computing Cluster (HPCC Systems) platform and its architecture, as well as parallel data languages ECL and KEL, developed to effectively solve big data problems. The third part, Big Data Applications, describes various data intensive applications solved on HPCC Systems. It includes applications such as cyber security, social network analytics including fraud, Ebola spread modeling using big data analytics, unsupervised learning, and image classification. The book is intended for a wide variety of people including researchers, scientists, programmers, engineers, designers, developers, educators, and students. This book can also be beneficial for business managers, entrepreneurs, and investors.

Big data has evolved as a most challenging area in scientific study and research. It has drawn much attention during the last few years. It influences our modern society, business, government, healthcare, research and almost every discipline. In this data-driven era, where data is continuously acquired from a verity of sources for different purposes, the ability to make timely decisions based on available data is a very critical task. The massive data size, variety, velocity, accuracy and high dimensionality presents new challenges to big data. This paper attempts to present some challenges of big data. In addition, a study on the conceptual design of big data architecture presented on specific big data applications. In this paper, we presented research work on big data analytics techniques in the areas of text, sentiment, video, social media and predictive analytics. A comparative analysis on selected big data applications has been presented in detail.

Single Root I/O Virtualization (SR-IOV) is a promising I/O virtualization approach for achieving high performance in the virtualization over InfiniBand (IB) network. One challenge is related to the hardware address assignment for each virtual IB device. There are two schemes for the hardware address assignment, static assignment and dynamic assignment. Static assignment always preserves the hardware address of a virtual IB device that is attached to a VM, but the dynamic assignment does not. A drawback, however, using static assignment is that its communication will be disconnected after VM migration. In this paper, we point out the problem related to SR-IOV over IB that breaks the network connections after VM migration when the static assignment is deployed. Then, we propose a signalling mechanism that can maintain the network connectivity after VM migration. The performance evaluation using an experimental test bed shows that the proposed signalling mechanism does not increase the service downtime during hot migration. We also optimize the signalling method, where the same event can only be forwarded to a physical server once regardless of the hosted VMs, to reduce the management message overhead from $O(n*m)$ to $O(n)$.

With the development of strategic goals of “The Belt and Road”, cross-border e-commerce has developed prosperously in China. The application of big data has been a vital strategic measure for cross-border e-commerce in “Internet+” era. Based on the motivation of many cross-border e-commerce enterprises to apply big data in marketing, this paper researches the procedures and fruits of applying big data into the accurate marketing of cross-border e-commerce combining the qualitative methods and literature review. It concluded that applying big data to cross-border marketing not only provides consumers' convenience but also enterprises' benefits of various aspects. Therefore, the research on the application of big data in accurate marketing of cross-border e-commerce bears practical significance.

Big data management and application specialty is an emerging major in our school of information and business management. Based on the Internet plus and big data era, we mainly study the application of big data analysis theory and methods in economic management and the management and management of big data. This paper aims to explore how to improve the innovation and practical ability of students in this major through practical teaching. This paper mainly elaborates from four aspects, namely, the basic principles of practical teaching, the path of students' ability improvement, the construction of the completed practical teaching system and the application of practical teaching in this major.

The chapter showcases the awareness of big data usage among librarians in Zimbabwe. The concept of big data is new, and librarians are building capacity to move with the current trends in librarianship. This chapter assists in pointing areas where big data can be applied in libraries. It also documents the challenges that are faced when using big data applications and proffer solutions that can be applied to deal with those challenges. It answers the question of whether it is practical to utilise big data in any type of library. A qualitative study was done where an online questionnaire was administered to twenty librarians in research institutions in Zimbabwe. The findings revealed that librarians are aware of the big data concept but are not utilising the tools and techniques in data mining and analysis. The authors recommend that capacity building should be done to equip librarians with the requisite skills.

The mining and application of big data in academic journals can accelerate the construction of data journals, enhance journal’s influence, and promote the sharing and dissemination of scientific data in academic journals worldwide. This paper uses bibliometric method to retrieve published articles with the theme of big data and journal in CNKI database, analyzes the academic achievements of the development of academic journals with the application of big data in the recent five years using quantitative visualization analysis, expounds the research progress of academic journals in big data field, and discusses the advantages of big data application in periodical industry. The results show that: study on the application of big data in academic journals are gradually deepening and scientific, and the relevant research still needs more financial fund from the state and social units, big data has prominent advantages such as accuracy, scientificity and value maximization in the workflows of academic journals. The mining and application of massive data is very important for promoting the development of high-quality academic journals and optimizing the supply-demand relationship of knowledge services of academic journals.

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things. With the development of climate observing systems, particularly satellite meteorological observation and high-resolution climate models, and the rapid growth in the volume of climate data, climate prediction is now entering the era of big data. The application of big data will provide new ideas and methods for the continuous development of climate prediction. The rapid integration, cloud storage, cloud computing, and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively, thus predicting the future climate more accurately. This paper describes the application status of big data in operational climate prediction in China; it analyzes the key big data technologies, discusses the future development of climate prediction operations from the perspective of big data, speculates on the prospects for applying climatic big data in cloud computing and data assimilation, and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.

Big data applications are becoming more popular across many different fields and civil engineering is no different. The benefits of big data lie in its potential to provide valuable insights into various large datasets. Big data applications can identify patterns and trends that were previously unknown, which can help them make informed decisions and develop effective strategies. In the case of civil engineering, this could be taking large datasets that have been produced in relation to pieces of infrastructure and using them to create more efficient management strategies. One of the issues with using big data is that if the input dataset is flawed then the output resulting from any big data analysis will be compromised. Therefore, data validation is used, which is the process of ensuring that data is accurate, complete, and consistent. The consequence of not undertaking data validation may be Inaccurate or inconsistent data which can lead to incorrect insights and decisions in big data applications. This paper explores the necessity of validating data before it is used in a big data application. It outlines some of the different methods used for validating data and provides an overview of the potential issues that may arise from data validation errors. A case study is then presented showing the process of validation on data collected from four bridges and provides recommendations for implementing data validation as part of a larger big data workflow. The results of the case study show that validation of data is an important step in the big data process both for confidence in the outputs and to make big data applications more useful and more common in the civil engineering field. The discussion and presented case study in this paper highlight the necessity of validating data. It has shown some of the potential issues that may arise from not undertaking data validation.

Over time, various novel methods and techniques have been proposed. Over the years, three technologies have emerged most frequently, namely big data, cloud computing, and artificial intelligence. Big data and cloud computing are actually extensions and paving the way for artificial intelligence technology. Especially in today's society, with the increase of energy resources, the application of big data is more and more extensive, and in education big data is also used in it. Therefore, it is now more and more convenient for us to know the excellence and gap between universities in various countries and regions. Therefore, the purpose of this paper is to study the comparative in the context of large data, Chinese and Japanese universities have the advantages and disadvantages of it education. Therefore, after consulting the relevant parties about it essence and it education, as well as the teaching background and teaching attitude of Chinese and Japanese universities, this paper integrates the information found by using big data-related algorithms, then analyzes and processes the relevant data using hierarchical bat algorithms, and then produces the experimental results we need. Experimental results show that the use of big data and hierarchical bat algorithms can better study the characteristics of it education in Chinese and Japanese universities.

The application of big data in the field of ecological environment has achieved remarkable results, but there are still problems such as insufficient innovation and lack of shared service ability. This research takes the big data platform as the technical support and makes full use of advanced technologies such as internet of things, cloud computing, and big data to realize the integration of ecological environment big data resources and real-time data sharing. Through network technology, the ecological environment metadata can be automatically obtained from the internet and stored. When sharing ecological environment metadata, realize the data response and declassification of geospatial data for different user access levels, and finally safely feedback the ecological environment spatial big data integrated with the target ecological environment metadata and declassified geospatial data to the access initiator, so as to realize the centralized sharing of decentralized environmental information resources and give full play to the supporting role of big data, so that big data can be effectively applied in environmental monitoring. Find out the changes of pollution sources and pollutants through massive data, so as to provide necessary data support for the whole environmental monitoring.

This study will investigate the security problems that are linked with the Hadoop ecosystem, Big Data, Map Reduce, and cloud computing. The objective of this research is to analyze these concerns. Specifically, the primary emphasis is placed on the security concerns that are linked with big data in cloud computing. Big data applications have a lot to offer organizations, companies, and a broad range of industries, no matter how big or little. There have been issues with Hadoop and cloud computing security, therefore we also look at several possible solutions to these difficulties. The field of cloud computing security is experiencing fast growth in several areas, including computer security, network security, information security, and data privacy. Data, apps, and the infrastructure related to them are greatly helped by cloud computing's use of rules, technologies, controls, and big data tools to keep everything safe. Researchers also anticipate that cloud computing, big data, and their associated benefits will represent the most exciting new areas of study.