A New Approach Based on a Multi–Agent System for IDS in Cloud Computing

Cloud computing and big data: Technologies and applications

Big data as a Service (BDaaS) provides a viable strategy for organizations to implement scalable, tailorable big data infrastructure and applications built on this infrastructure. New trends in the BDaaS market are moving toward an open world model -- what we call the Neo-Metropolis model -- for developing BDaaS platforms. The key to the success of such large-scale technology-agnostic platforms, we posit, is an architectural strategy revolving around microservices and DevOps. This article presents the results of an action research with a Neo-Metropolis BDaaS vendor and illustrates how architectural support for DevOps is critical in achieving desired system qualities and enabling platform success. This research contributes to illuminate best practices of DevOps, and to validate and augment a set of DevOps tactics previously developed, while adding and recategorizing new instances of well-established architectural tactics.

The purpose of this research articles is to analyze the characteristics of big data as a service (BDaaS) markets which is currently in its initial stages from adoption driving and inhibiting perspectives. This study theoretically classifies the BDaaS market into drivers and inhibitors through the PEST analysis based on previous literature. Analytic hierarchy process (AHP) matrix was used to analyze the segmentation, drivers and inhibitors, to understand the nature of the early BDaaS market. This study has proposed a new theoretical methodology for analyzing the early market of BDaaS and it is expected that this methodology may be used in the market analysis of other fields beyond the BDaaS market. Customer factors in the consumerization phenomenon and social, political, and technological factors in the PEST analysis were the important drivers of BDaaS adoption. Suppliers from the consumerization phenomenon and political factors in the PEST analysis were the most important inhibitors of BDaaS adoption. The results have important implications for policymaking and fostering other newly established BDaaS markets in emerging economies.

PurposeBig data has posed problems for businesses, the Information Technology (IT) sector and the science community. The problems posed by big data can be effectively addressed using cloud computing and associated distributed computing technology. Cloud computing and big data are two significant past-year problems that allow high-efficiency and competitive computing tools to be delivered as IT services. The paper aims to examine the role of the cloud as a tool for managing big data in various aspects to help businesses.Design/methodology/approachThis paper delivers solutions in the cloud for storing, compressing, analyzing and processing big data. Hence, articles were divided into four categories: articles on big data storage, articles on big data processing, articles on analyzing and finally, articles on data compression in cloud computing. This article is based on a systematic literature review. Also, it is based on a review of 19 published papers on big data.FindingsFrom the results, it can be inferred that cloud computing technology has features that can be useful for big data management. Challenging issues are raised in each section. For example, in storing big data, privacy and security issues are challenging.Research limitations/implicationsThere were limitations to this systematic review. The first limitation is that only English articles were reviewed. Also, articles that matched the keywords were used. Finally, in this review, authoritative articles were reviewed, and slides and tutorials were avoided.Practical implicationsThe research presents new insight into the business value of cloud computing in interfirm collaborations.Originality/valuePrevious research has often examined other aspects of big data in the cloud. This article takes a new approach to the subject. It allows big data researchers to comprehend the various aspects of big data management in the cloud. In addition, setting an agenda for future research saves time and effort for readers searching for topics within big data.

This study investigates the determinants of Big Data as a Service (BDaaS) adoption among organizations operating in data-intensive industries such as finance, healthcare, retail, and logistics in Europe. Guided by an integrated theoretical lens that combines the Technology-Organization-Environment (TOE) framework with Diffusion of Innovations (DOI), Socio-Technical Systems (STS), and Resource-Based View (RBV), the research employs a quantitative, cross-sectional design. Data were collected through structured questionnaires from 327 IT professionals and decision-makers and analysed using Structural Equation modelling (SEM) and logistic regression. The results indicate that technological readiness, organizational capacity, environmental pressure, and human technology fit, significantly influence BDaaS adoption intention and actual implementation. Moreover, organizational capacity mediates the relationship between technological readiness and adoption, while firm size moderates the effect of environmental pressure. These findings offer theoretical contributions to the literature on digital transformation and provide practical and policy insights for fostering BDaaS uptake across sectors.

In order to improve the ability of data retrieval in cloud computing environment, a reduced dimension classification algorithm of complex attribute big data in cloud computing based on deep neural network learning is proposed. The complex attribute big data under cloud computing is constructed by low dimensional feature set, and the complex attribute big data under cloud computing is analyzed by linear programming and fitting using grid clustering method. Big data samples of all complex attributes are sampled and trained to extract the associated features of big data, which is a complex attribute under cloud computing. The feature extraction results of complex attribute big data under cloud computing are inputted into the deep neural network learner for data classification, and the complex attribute big data dimensionality reduction classification under cloud computing is realized by combining big data fusion clustering method. The simulation results show that the accuracy of big data dimension reduction classification for complex attributes in cloud computing is high and the error rate is small.

Cloud Computing and Big Data continue to be disruptive forces in computing and have made inroads in the Computer Science curriculum, with courses in Cloud Computing and Big Data being routinely offered at the graduate and undergraduate level. One major challenge in offering courses in Big Data and Cloud Computing is resources. The question is how to provide students with authentic experiences making use of current Cloud and Big Data resources and tools and do so in a cost effective manner. Historically, three options, namely physical clusters, virtual clusters and cloud-based clusters, have been used to support Big Data and Cloud Computing courses. Virtual clusters and cloud-based options are those that institutions have typically adopted and many arguments in favor of these options exist in the literature, citing cost and performance. Here we argue that teaching Big Data and Cloud Computing courses can be done making use of a physical cluster and that many of the existing arguments fail to take into account many important factors in their calculations. These factors include the flexibility and control of a physical cluster in responding to changes in industry, the ability to work with much larger datasets, and the synergy and broad applicability of an appropriately equipped physical cluster for courses such as Cloud Computing, Big Data and Data Mining. We present three possible configurations of a physical cluster which span the spectrum in terms of cost and provide cost comparisons of these configurations against virtual and cloud-based options, taking into account the unique requirements of an academic setting. While limitations do exist with a physical cluster and it is not an option for all situations, our analysis and experience indicates that there is great value in using a physical cluster to support teaching Cloud Computing and Big Data courses and it should not be dismissed.

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.

In the quest for sustainable urban development, smart cities are increasingly harnessing cloud computing and big data analytics to optimize energy usage and drive policy initiatives. This review explores how leveraging these technologies can transform energy management within urban environments. Cloud computing provides the scalable infrastructure necessary for processing and storing vast amounts of data generated by smart sensors and IoT devices in real-time. By utilizing cloud-based platforms, cities can ensure the efficient aggregation, analysis, and dissemination of energy data, facilitating informed decision-making and policy formulation. Big data analytics plays a crucial role in this framework by enabling the analysis of complex and voluminous datasets to uncover patterns and insights that traditional methods may miss. Through advanced analytics, cities can gain a deeper understanding of energy consumption trends, predict peak demand periods, and identify opportunities for efficiency improvements. This data-driven approach allows policymakers to design and implement targeted energy policies, optimize resource allocation, and reduce operational costs. Key benefits of integrating cloud computing and big data analytics in energy optimization include enhanced real-time monitoring capabilities, improved accuracy in demand forecasting, and more effective management of energy resources. The ability to analyze and visualize large datasets supports the development of dynamic policies that adapt to changing conditions and promote energy efficiency across various sectors. Despite these advantages, challenges such as data privacy concerns, the need for robust cybersecurity measures, and the requirement for skilled personnel to interpret complex data must be addressed. Additionally, ensuring equitable access to these technologies across diverse urban areas is essential for maximizing their impact. In conclusion, the strategic application of cloud computing and big data analytics offers significant potential for optimizing energy use in smart cities. By embracing these technologies, urban centers can drive more effective and responsive energy policies, contributing to broader sustainability goals and enhancing overall quality of life.

Intelligent approaches for security technologies

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.

ENERGY EFFICIENT ROBUST BACTERIAL FORAGING ROUTING PROTOCOL (EE-RBFRP) FOR BIG DATA NETWORK

In this paper, wavelet analysis methods, mainly including wavelet basis function, continuous and discrete wavelet transform and multiresolution analysis were introduced first. Then, current applications of the wavelet analysis methods to various aspects of cloud computing and big data were summarized. The three aspects of wavelet-based analysis include: The application of wavelet denoising, the online monitoring for image and the image watermarking algorithm design in cloud computing. For big data, we discuss from two aspects: The function approximation ability and the image processing in big data, several suggestions and opinions on the future researches and applications of wavelet analysis methods in cloud computing and big data were discussed.

Security is a major challenge faced by cloud computing (CC) due to its open and distributed architecture. Hence, it is vulnerable and prone to intrusions that affect confidentiality, availability, and integrity of cloud resources and offered services. Intrusion detection system (IDS) has become the most commonly used component of computer system security and compliance practices that defends cloud environment from various kinds of threats and attacks. This chapter presents the cloud architecture, an overview of different intrusions in the cloud, the challenges and essential characteristics of cloud-based IDS (CIDS), and detection techniques used by CIDS and their types. Then, the authors analyze 24 pertinent CIDS with respect to their various types, positioning, detection time, and data source. The analysis also gives the strength of each system and limitations in order to evaluate whether they carry out the security requirements of CC environment or not.

Cloud computing has gained an extreme importance nowadays. Every organization is getting attracted toward the Cloud computing due to its attractive features like cost saving, adaptability, etc. Although it offers the attractive features but still Cloud threats need great consideration. The insider threat is critically challenging in the Cloud-based environments. In order to mitigate from insider attacks in Clouds, the use of Intrusion detection system (IDS) is quite challenging. Every type of IDS has different methods of attack detection. So, single IDS cannot guarantee the protection from all types of attacks. Thus, in this paper, we have studied the various types of IDS and their features which made them either suitable or unsuitable for cloud computing. Also on the basis of review, required features for the Cloud-based IDS are identified.