Cloud

It is now more than ever that enterprises must use cutting-edge tools to remain competitive and adaptable to evolving markets. This article discusses the business process optimization transformative impact of data analytics and its focus on Dynamic Process Adaptation Using Real-Time Multi-Source Analytics (DPA-RMA). The architecture connects many sources of data ranging from IoT devices to CRM and social media channels, to dynamically modify processes in real time instead of static optimization. The report lists several advantages of real-time analytics such as: Better decisions made with proofs, Higher efficiency through workflow optimization and redundancies, highly personalized customer experiences. These benefits enable organizations to achieve operational excellence and stay agile in changing markets. But data analytics isn't easy to apply. The paper pinpoints the top challengesorganization resistance to change, data security, infrastructural needs to implement real-time analytics solutions, etc. For all these, the research suggests concrete interventions such as change management procedures, strong encryption and scalable cloud platforms. Furthermore, the study also shows how it is crucial to follow data protection laws like GDPR and HIPAA to avoid risk and protect trust. Predictive and prescriptive analytics can be enabled using the new technology such as artificial intelligence (AI) and machine learning (ML), with a vision forward-looking approach, the paper proposes. In offering an overview of analytics-based optimization as compared to the traditional approaches and a roadmap for overcoming implementation challenges, this research offers a practical guide to any organization looking to make the most of data analytics. These results validate not only the strategic importance of moving to data-driven operations but also the importance of creating an innovation and adaptability organizational culture.

In this digital era, artificial intelligence (AI) is revolutionizing all the fields known to man. Data governance and data lineage practices are no exception. AI is making its mark in the area of Data governance and Lineage, making them more efficient and reliable. This paper explores the integration of AI into data governance frameworks, emphasizing its role in automating data quality checks, enhancing regulatory compliance, and ensuring data security. Furthermore, the study examines how AI driven data lineage provides comprehensive visibility into data movements, improving transparency and accountability. By detailing the synergy between AI, data governance, and data lineage, the paper highlights how these advancements enable organizations to manage data more effectively and gain actionable insights.

In recent times, the advent of cloud based AI chatbots has transformed real time customer support by infusing the newer technologies to deliver better user experience and operational efficiency. The next steps have seen large language models (LLMs) including GPT 4 added into it to allow understanding and generation of human speech, enhancing quality of interaction. Also, voice recognition and natural language processing (NLP) integration provides a smooth voice based interaction experience for different customers' choices. Chatbots are capable of sentiment analysis making them able to detect customer emotions and respond according to the generated sentiment, or even escalate the case to human agents if required. Also, the multilingual support has been enhanced to enable chatbots to interact in multiple languages hence they become more accessible and effective. Also, the combination of AI systems with human intelligence in hybrid models allows for more accurate and context aware answers that can handle very wide range of customer queries. Together, these technologies help the optimization of the real time customer support process, which improves the satisfaction and the operational excellence.

The phenomenon of climate change emerges as a significant concern of the 21st century, wielding substantial implications for ecosystems, human populations, and economic stability. Effective monitoring plays an indispensable role in addressing these challenges by facilitating the identification of trends and providing the necessary data to inform mitigation and adaptation strategies. Traditional climate monitoring methodologies often exhibit limitations, including high costs, slow data processing, and insufficient resolution, which hinder timely responses to rapid environmental changes. In this context, Artificial Intelligence (AI) has assumed a crucial role in overcoming these challenges. The capabilities of AI in processing extensive datasets, recognizing patterns, and making predictions enhance the accuracy and efficiency of climate change surveillance at unprecedented rates. This study evaluates various AI technologies that support climate change monitoring, including machine learning, deep learning, natural language processing, and big data analytics. The analysis encompasses applications in climate forecasting, satellite observation, wildfire detection, energy management, and carbon emission tracking. The study further discusses the benefits of AI, such as improved data processing, real-time monitoring, and predictive abilities, while acknowledging the challenges of data integrity, integration, scalability, and ethical considerations. The paper concludes by speculating on future advancements in AI, including ecosystem modelling, AI-enhanced climate policy formulation, and collaborative AI networks, which hold the potential to significantly bolster global initiatives aimed at promoting climate resilience and sustainability.

The demand on online courses to achieve higher education skills and knowledge has been rising during the last years. More and more people study academic subjects in parallel of having part-time or even full-time jobs. One of the most important challenges of e-learning solutions is to reproduce the features of a higher education institution's campus as completely as possible to students who attend online courses. While there is a large market of e-learning tools, the majority of them come with a lecturer-oriented and material-based view only. When using single tools, a lot of aspects are not covered, for example the interaction between learners is often not paid enough attention to. In literature it has been shown that the social interaction of students on a classical university or college campus has a very positive effect on learning. To enable social interaction for online students, typically a conglomerate of software tools has to be provided: Classical e-learning tools to distribute content, tools for video conferencing and tools for social networking. While offering just a selection of tools in parallel might be able to cover the requirements formally, it often introduces unnecessary complexity. Several login procedures, different user interfaces and time-consuming ways of organizing data storage can render tools very unattractive. Especially when your typical addressees are roaming students who spend time for learning in alternating environments, there is a high demand for cloud-based virtual desktop solutions, which provide a fast and simple way to start learning or interacting with fellow students, wherever a student stays at the moment. In this work-in-progress paper we present our idea of a cloud-based virtual desktop environment approach while focusing especially on engineering courses and their requirements.

Cloud computing offers unparalleled scalability and agility, but it also introduces complexities in managing and controlling costs. This paper explores the critical role of cost tagging strategies in enhancing cloud visibility and accountability. By implementing a robust tagging framework, organizations can gain granular insights into their cloud spending, identify cost inefficiencies, and foster a culture of cost responsibility across teams. Cost tagging is not merely a technical exercise; it's a strategic imperative for organizations embracing cloud computing. By implementing a welldefined tagging strategy, businesses can gain greater control over their cloud costs, improve operational efficiency, and unlock the full potential of their cloud investments.Improving cloud visibility and accountability through cost tagging strategies involves the systematic use of metadata tags to categorize and track cloud resources. By attaching key-value pairs to cloud assets, organizations gain detailed insights into their cloud usage and expenses. This approach facilitates accurate cost allocation, enhances financial reporting, and promotes accountability by attributing costs to specific projects, departments, or business units. Consistent and standardized tagging, coupled with automation, ensures that all resources are accurately labeled, reducing human error and improving overall cloud financial management. This strategy empowers organizations to optimize their cloud spending and align it with business objectives.

Digitalisation is a set of tools that are essential for businesses to grasp and create value from. This set of slides is a presentation I have been working on to provide an introduction to digital and inform the end user about the impact and potential.

The Internet of things (IoT) has been widely used in life support facilities, especially in the healthcare sector. The management of health and safety (H&S) devices is an indispensable factor in guaranteeing human life. Thus, many companies and organisations have ensured the safety of their workplace from any overwhelming risks, such as fire, damage and accidents. The problem is that companies that have thousands of employees also have hundreds of health and safety systems in continuous use and Building Service Managers do not know which devices need maintenance or replenishment and require a significant amount of time to continuously check that their health and safety systems are maintained according to legislation requirements. The consequences of companies not meeting their health and safety obligations are serious and expensive if any of their employees are seriously injured as a consequence of the health and safety products not being in their original, immaculate order for use. Furthermore, insurance companies will refuse to pay compensation for injury to employees if it has been shown that the health and safety products are not in their original, immaculate order for use. Therefore, it makes economic sense to design and use an Internet of Things based monitoring system to record the state of health and safety products within companies. This thesis presents the design and prototype of an IoT-based health and safety (H&S) products monitoring system using artificial intelligence (AI) and image processing. The thesis aims to present a real-time control and monitoring system to determine the status of consumable hospital devices, such as first aid boxes, earplug dispensers, life jackets and fire extinguishers by using Zigbee sensors to read the measurement of the weight, and image-processing (sensor in a micro digital camera with affixed laser light) to determine the level. The proposed design is a dynamic (real-time) system that can monitor and predict the status of any equipment in hospitals or workplaces and notify the building service managers when any facilities need to be updated. Therefore, the proposed system can reduce hospitals' and companies' time, money and workforce requirements compared with previous manual maintenance approaches that are time-consuming and require more efforts and higher costs. Artificial intelligence infrastructure based on Ant Colony, Traveling Salesman Problem TSP and Genetic Algorithm GA is proposed to be implemented in optimisation algorithm to support salesmen in selecting the shortest path and latest time in maintaining and replenishing critically low devices at different locations in an appropriate time. Artificial neural networks are used to predict the optimal performance and the correlation between effective input factors and ii performance output. The achieved AI accuracy results is 96% of the proposed system which are proven to be reliable and can be used to manage many consumable H&S products in different district hospitals and companies as compared with the other approaches i.e. (Genetic, Ant-colony and TSP) that have been used at the same application whilst meeting regulatory requirements. The efficiency of the proposed system is validated because it reduces time, costs,

As organizations increasingly transition their software development, deployment, and operations to the cloud, the need for robust security practices is paramount. By combining the DevOps and Security together, we can call it DevSecOps, which provides a better approach for building and maintaining the secure cloud-based systems. With the increasing of technology day by day, threats and security issues also increases day by day. In this paper, we discuss the threats and counter them by various steps. The paper underscores the importance of fostering a security-first culture, implementing a zero-trust model, and adhering to compliance and auditing requirements.

The study aimed to determine what domains of social networking usage significantly influence on engagement
of senior high school students. A non-experimental quantitative research design and the descriptive correlation
technique was used. Universal sampling was used in the conduct of the study. The collected data using adapted
survey questionnaires were analyzed using mean, Pearson product-moment correlation coefficient, and linear
regression. The findings of the study revealed that the level of social networking usage and engagement of the
senior high school students were described as high. Also, there was a strong significant correlation between
social networking usage and student engagement; Further, the result shown a significant influence of social
networking usage towards student engagement, with two domains namely socialization and informativeness
significantly influence student engagement. The socialization got the highest data value, therefore, considered as
best influence.

Ethical questions arise at every stage of the research process from initial design through fieldwork and analysis of research findings. This paper consider principles and level of research ethics and how these apply in qualitative research and data analysis stages. Researchers should refer to some ethical guidelines to ensure they have adhered to the principles of good research practices. This paper focus on the ethical perspective in a given situation that we generally need to follow in qualitative research when collecting and analyzing data. These include maintaining scientific integrity, upholding human rights and dignity, ensuring social responsibility, building trust in researcher and the participants and protecting their safety.

The concept of graph labeling was introduced by Rosa in 1967 [7]. In 2019 Esakkiammal et.al., introduced the
concept of Proper d-lucky labeling [5], Proper Lucky labeling was introduced by Kins Yenoke et.al.,(2016) [1],
In 2004 Sundaram et.al., introduced the concept of Product Cordial Labeling (2022) [9], Tribonacci cordial
labeling[ was introduced by Sarbari Mitra et.al., 2022 [8],In 2024 Bala et.al., introduced the concept of Tribonacci
Product cordial labeling[2]. Gaussian anti magic labeling was introduced by Thirusangu et.al., (2019) [10].
Motivated by the above work, in this paper we have investigate and the existence of d-lucky Labeling, Luck
Labeling, Tribonacci Product cordial labeling for Twig(𝑃𝑚⨀𝐾1), 𝑚 ≥ 2.

The number of the Internet of Things (IoT) devices is growing, leading to increased generation of data at a level that has never been witnessed from the network edge. These pervading are through different industries: in the likes of healthcare, transportation, manufacturing, and smart cities, explicit needs call for effective data processing and analysis at the edge. This paper discusses the idea of edge computing in an IoT context, focusing on how this can best be optimized in terms of minimizing the two aforementioned areas to improve overall system performance. The traditional approach to data processing in IoT systems, which is cloud-centric,json, often experiences latency problems because data has to travel over distances to servers that are centralized for analysis. More so, the continuous transmission of voluminous raw data into the cloud is very consuming of the energy and weighs down the network bandwidth. That is where the edge computing comes into place, filling this gap through the relocation of computational tasks near the source, thereby reducing latency that translates to relieving the network from congestion. This paper tries to reflect key principles and benefits of edge computing in an IoT environment based on a comprehensive review of existing literature and relevant case studies. Near IoT devices, edge nodes enable computational capabilities.

As organizations increasingly adopt DevOps to accelerate software development and deployment, ensuring robust security within this fast-paced environment has become a critical challenge. This research investigates the integration of Artificial Intelligence (AI) into DevOps security workflows, focusing on vulnerability detection, threat prevention, and automated compliance. Through a comprehensive exploration of AI techniques, including machine learning and deep learning, this study examines how AI-driven tools enhance security operations, mitigate risks, and improve compliance in DevOps pipelines. The findings underscore the transformative potential of AI in strengthening DevOps security frameworks.

This article introduces a new theory – cloud theory – using Druze society in the Middle East as a test case.
The theory provides a comprehensive framework for understanding unique dynamics regarding the resilience, adaptability, and influence of Druze society – a minority group in the Middle East. As such, the article aids understanding of the importance of social capital and the factors supporting its development and maintenance, with potential to improve support for other minority cultures worldwide.
— Cloud theory can be implemented in other fields, such as international relations and organisational behaviour, to elucidate the dynamics of power and influence in various contexts.

The research paper focuses on the effectiveness of Kubernetes in container orchestration. In this concern, it investigates the sports industry to assess the way Kubernetes can support this industry for its operational management. This system can be combined with Azure cloud and ML algorithms for better resource allocation and schedule management. This study found that Kubernetes can provide beneficial insight into resource operations by building data engineering.

Cloud computing adoption speed
brought fundamental changes to IT infrastructure by
enabling organizations to create adaptable systems
that reduced operational costs. The digitization of
modern businesses creates essential problems with
resource handling and security difficulties because
their digital platforms are built from complicated
program systems requiring reliable operational
frameworks. The current operational issues need
solutions that AI-powered and ML-based intelligent
cloud management systems can deliver. When AI
tools merge with resource deployment, the
performance of predictive automated cloud systems
is enhanced. This output enables the system to
perform operational security at a better level. ML's
operation stability types emerge from its ability to
detect workload and preserve self while identifying
abnormal activities. Business success through
operational excellence requires companies to
establish individual cloud management platforms
with AI capabilities and ML processing functions.
The main operational benefits of AI and ML-based
management systems create three fundamental
features that benefit automated workload
optimization while enabling instantaneous anomaly
detection and continuous security system
adjustments. AI automation generates its highest
operational effectiveness through prediction-based
analysis when work tasks are distributed among
available resources across the workload distribution
system. Through ML technology, organizations use
threat detection models to identify security threats in
advance, thus preventing unauthorized data
breaches. System operators who conduct reliability
tests on behavior frontiers gain two advantages
through improved operational capability and
reliability performance.
The upcoming years will require additional
operational intelligence monitoring capabilities and
automated cloud ecosystem management for cloud
management systems since this domain has started to
use AI and ML elements. The adaptive management
of wise clouds is the leading method to solve current
IT difficulty issues in hybrid and multi-cloud
implementations. Using precise AI and ML systems
predictions allows autonomous maintenance
operations to reach maximum cloud operational
success. The present limitations within data
protection and integration exist while companies
require AI solutions to improve their enterprise cloud
deployment capabilities. AI and ML systems at
company organizations allow them to select cloud
solutions that fulfill security needs by adjusting
budget allocation processes due to business growth
needs.

Today nearly every big company operates several data centers spread across the world. Each of these data centers hosts a variety of infrastructure services like MySQL, Redis, Hive, Message Queue etc. Now for any company it's very important to write the same code for all regions for scalability and following the DRY (Don't repeat yourself) aspect of system design. But the people who are designing and writing these codes are spread across multiple regions and due to that sometimes they are not aligned on every system configs. Sometimes even within the same region software engineers are not aligned properly on these system configs as to what system configs to keep for that region. Because of that, data center system configs start differing across multiple regions. Now it is very important to make sure these configurations are the same across all regions because it can lead to major critical issues in your team. In this paper, we will first introduce what the problem is, then we will talk about existing solutions and then we will talk about proposed solutions and how it tackles all the problems listed in the introduction section.

To ensure the success of 6G networks, it’s crucial for leaders to foster collaboration between governments, businesses, and research institutions. Establishing robust frameworks for secure global connectivity requires a unified approach. This includes setting international standards for cybersecurity, establishing global regulatory bodies, and ensuring cross-border data privacy protections. Learn more in 6G Leadership for Secure Global Connectivity Training by Tonex.

The integration of EPIC, KRONOS, and Workday in healthcare organizations presents a transformative opportunity to enhance operational efficiency, streamline workforce management, and improve patient care. EPIC serves as a robust electronic health record (EHR) system, facilitating the comprehensive management of patient information and clinical workflows. KRONOS optimizes workforce management through effective scheduling and labor analytics, while Workday addresses human capital management and financial processes. This paper explores the synergies achieved through the integration of these platforms, highlighting benefits such as enhanced data sharing, improved reporting and analytics, and better compliance with regulatory standards. Additionally, the challenges of interoperability, data security, and change management are examined to provide a holistic understanding of the integration process. API based integrated approach aims to foster a more coordinated, efficient, and patient-centered healthcare environment. This paper delves into the synergies achieved through the integration of these platforms, highlighting the significant benefits, including enhanced data sharing, improved reporting and analytics, and increased compliance with regulatory standards.

Cloud computing is an emerging technology where IT resources are virtualized to users as a set of a unified computing resources on a pay per use basis. The resources are dynamically chosen to satisfy a user Service Level Agreement and a required level of performance. Divisible load applications occur in many scientific and engineering applications and can easily be mapped to a Cloud using a master-worker pattern. However, those applications pose challenges to obtain the required performance. We model divisible load applications tasks processing on a set of cloud resources. We derive a novel model and formulas for computing the blocking probability in the system. The formulas are useful to analyze and predict the behavior of a divisible load application on a chosen set of resources to satisfy a Service Level Agreement before the implementation phase, thus saving time and platform energy. They are also useful as a dynamic feedback to a cloud scheduler for optimal scheduling. We evaluate the model in a set of illustrative scenarios.

The advent of Cloud Computing opened new opportunities in several areas, namely in the application development processes. As consequence, nowadays, PaaS (Platform-as-a-Service) service model allows simpler and flexible deployment strategies of applications, avoiding the need for dedicated networks, servers, storage, and other services. Within this context, several PaaS providers exist in the market, but each one having specific characteristics, proprietary technologies and Application Programming Interfaces (APIs). Based on such assumptions, this work addresses the challenge of devising a PaaS aggregation solution with the objective of unifying the information and management processes of applications created in PaaS environments. The proposed solution, denominated as PaaS Manager, take the form of a PaaS API aggregator aiming to struggle the existing lock-in in the PaaS market. In this perspective, this paper describes the specification, development and test of the proposed PaaS Man...

The quality of data becomes an essential component for the success of an organisation in a world that is largely influenced by data, where data analytics is becoming increasingly popular in the process of informing strategic decisions. The failure to improve the quality of the data can lead to undesirable outcomes such as poor decisions, ineffective strategies, dysfunctional operations, lost commercial prospects, and abrasion of the consumer. In the process of organisations shifting their focus towards transformative methods such as generative artificial intelligence, several use cases may emerge that have the potential to aid the improvement of data quality. Streamlining procedures such as data classification, metadata management, and policy enforcement can be accomplished by the incorporation of generative artificial intelligence into data governance frameworks. This, in turn, reduces the workload of human data stewards and minimises the possibility of human error. In order to ensure compliance with legal standards such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), generative artificial intelligence may analyse enormous datasets by utilising machine learning algorithms to discover patterns, inconsistencies, and compliance issues.

The volume and complexity of data is always increasing, and organisations are becoming more adept at using data, which in turn is creating new needs for various methods of data aggregation, manipulation, storage, and presentation. The reality of running a business is becoming too much for data management systems to handle on its own, and progressive businesses have come to this realisation. On the other hand, they have started to see that robust data governance is crucial to solving the data problem. Past efforts at data governance were unsuccessful due to IT-driven, inflexible processes and fragmented, system-by-system approaches. Governance has traditionally been informal, happening in silos around specific enterprise repositories, and there has been a lack of organisational support and structure. With the rise of cloud computing and its widespread use, experts are paying more attention to the topic of data governance, which is surprisingly understudied. Initial studies aimed at creating efficient cloudbased data governance systems are detailed in this article. The significance of data governance in the cloud is discussed in this paper in a way that is both informative and accessible to cloud providers and consumers. Next, the research provides a theoretical basis for the system's development along with a five-stage procedure.

The volume and complexity of data is always increasing, and organisations are becoming more adept at using data, which in turn is creating new needs for various methods of data aggregation, manipulation, storage, and presentation. The reality of running a business is becoming too much for data management systems to handle on its own, and progressive businesses have come to this realisation. On the other hand, they have started to see that robust data governance is crucial to solving the data problem. Past efforts at data governance were unsuccessful due to IT-driven, inflexible processes and fragmented, system-by-system approaches. Governance has traditionally been informal, happening in silos around specific enterprise repositories, and there has been a lack of organisational support and structure. With the rise of cloud computing and its widespread use, experts are paying more attention to the topic of data governance, which is surprisingly understudied. Initial studies aimed at creating efficient cloudbased data governance systems are detailed in this article. The significance of data governance in the cloud is discussed in this paper in a way that is both informative and accessible to cloud providers and consumers. Next, the research provides a theoretical basis for the system's development along with a five-stage procedure.

Operation management for a private cloud infrastructure faces many challenges including efficient resource allocation, load-balancing, and quick response to real-time work-load changes. Traditional manual IT operation management is inadequate for this highly dynamic and complex environment. This work presents a distributed service architecture that is designed to provide an automated, shared, off-site operation management service for private clouds. The service architecture incorporates important concepts such as: Metric Templates for minimizing the network overhead for transmission of cloud metrics; a Cloud Snapshot that provides a global view of the current status of the cloud, supporting optimal decision making; and a Calendar-based Data Storage Model to reduce the storage required for cloud metric data and increase analysis performance. A proactive response to cloud events is generated based on statistical analysis of historical metrics and predicted usage. The architecture, functional components and operation management strategies are described. A prototype implementation of the proposed architecture was deployed as a service on the OpenStack. The effectiveness and usability of the proposed proactive operation management solution has been comprehensively evaluated using a simulated private cloud with dynamic workloads.

In today's rapidly evolving digital landscape, organizations increasingly rely on Application Programming Interface known as API for seamless integration and data exchange. API providers comprising consumers, developers, and project teams face several challenges in discovering, developing and mapping APIs in compliance with organizational enterprise architecture principles. This paper aims to address these challenges by proposing an AI-Powered Agent (AIPA) Framework that leverages Generative AI and AI Code Assistant tools to enhance API governance and streamline the API development cycle (API Management, develop API, testing, security, deployment and monitoring). The proposed framework facilitates API discovery through an interactive chat interface that summarizes available APIs based on the use case. Additionally, it aims to automate key aspects of API development, including compliance checks, security protocols, and document generation thereby significantly reducing effort(s), human error(s), and improving productivity and efficiency. Establishing an API Center for Enablement (C4E) ensures consistent adoption of best practices across the organization. By integrating AI-driven solution with API governance, this paper outlines a pathway for organizations to improve API quality, security, and usability while empowering to adapt to digital disruptions. The findings suggest that a comprehensive, AI-enhanced governance framework enhances operational and development efficiency and fosters innovation.

Cloud computing has emerged as the backbone of digital transformation across industries, offering scalable, cost-efficient, and flexible computing resources. However, as enterprises increase their reliance on cloud-based infrastructure, the environmental and social impact of cloud adoption has come under scrutiny. Sustainable cloud computing integrates Environmental, Social, and Governance (ESG) principles to minimize carbon emissions, enhance energy efficiency, and ensure ethical data governance. This paper explores sustainable strategies for ESG-compliant cloud adoption, addressing key challenges and proposing frameworks for a responsible digital future.

In the fast-changing landscape of Banking and Financial services, effective Investment contract review is for maintaining regulatory compliance, mitigating risks, and ensuring transparency among partners / institutions / clients and their stakeholders. Traditional methods of manual contract review, which often involve labor-intensive and repetitive processes, particularly when handling larger volumes of complex agreements. This paper proposes the BaFiCoRe (Banking and Financial Contract Review) Framework, which harnesses the capabilities of Generative AI (GenAI) and Blockchain technologies to revolutionize the contract review processes. GenAI is employed to automate critical tasks such as contract analysis, adherence to accounting standards and conclusions (including U.S GAAP-U.S Generally Accepted Accounting Principles and IFRS-International Financial Reporting Standards) and identification of discrepancies, thereby significantly minimizing errors and improving efficiency. Concurrently, Blockchain technology is employed to address the challenges in contract verification, secure storage, and the transparent audit trails. By integrating GenAI and Blockchain technologies, the BaFiCoRe Framework provides transformative methodology for contract review, ensuring consistent governance, enhanced security, and operational effectiveness, ultimately fostering a more transparent and innovative banking and financial service industry.

The demand on online courses to achieve higher education skills and knowledge has been rising during the last years. More and more people study academic subjects in parallel of having part-time or even full-time jobs. One of the most important challenges of e-learning solutions is to reproduce the features of a higher education institution's campus as completely as possible to students who attend online courses. While there is a large market of e-learning tools, the majority of them come with a lecturer-oriented and material-based view only. When using single tools, a lot of aspects are not covered, for example the interaction between learners is often not paid enough attention to. In literature it has been shown that the social interaction of students on a classical university or college campus has a very positive effect on learning. To enable social interaction for online students, typically a conglomerate of software tools has to be provided: Classical e-learning tools to distribute content, tools for video conferencing and tools for social networking. While offering just a selection of tools in parallel might be able to cover the requirements formally, it often introduces unnecessary complexity. Several login procedures, different user interfaces and time-consuming ways of organizing data storage can render tools very unattractive. Especially when your typical addressees are roaming students who spend time for learning in alternating environments, there is a high demand for cloud-based virtual desktop solutions, which provide a fast and simple way to start learning or interacting with fellow students, wherever a student stays at the moment. In this work-in-progress paper we present our idea of a cloud-based virtual desktop environment approach while focusing especially on engineering courses and their requirements.

Kubernetes has become the cornerstone for deploying and managing containerized applications. Its dynamic and distributed nature, while offering scalability and resilience, introduces significant complexities in observability, monitoring, and impact analysis. Traditional monitoring solutions often fall short in providing visibility into the functionalities affected by interruptions in the Kubernetes platform, leading to prolonged downtime and impact analysis. This paper investigates the challenges associated with achieving effective observability for Kubernetes environments due to their distributed architecture. We highlight the limitations of relying solely on conventional metrics, logs, and events to understand the impact of system interruptions. To address these challenges, we propose an integrated approach that combines synthetic testing with existing observability tools. By executing synthetic transactions and simulating user interactions with the Kubernetes platform across various use cases, this method proactively evaluates the system's performance and detects issues that passive monitoring might overlook. This comprehensive view enables faster identification of impacted functionalities. Through practical experiments, we demonstrate how incorporating synthetic testing improves the detection of service degradations and supports more effective troubleshooting strategies.

Kubernetes has emerged as the leading platform for container orchestration, offering a robust framework for automating the deployment, scaling, and management of containerized applications. As its adoption increases, managing diverse workloads across various environments becomes increasingly complex, necessitating mechanisms for policy enforcement, compliance assurance, and dynamic configuration management. In Kubernetes, admission webhooks provide a means to intercept requests, enabling the mutation and validation of these requests to enforce custom policies. This paper presents comprehensive guidelines for configuring and operating admission webhooks to enhance security, consistency, and operational efficiency in Kubernetes environments. Through this paper, readers will gain hands-on experience in implementing best practices, thereby optimizing the use of admission webhooks to effectively maintain organizational policies and standards.

In today's digital age, the utilization of cloud technology has become increasingly prevalent in organizations of all sizes. A Cloud Center of Excellence (CCoE) has emerged as a strategic approach to drive cloud adoption and ensure governance, security, and compliance in the cloud environment. The success of a CCoE, however, heavily relies on effective leadership that can set the vision, build the team, and drive the transformation towards cloud maturity. Establishing a Cloud Center of Excellence (CCoE) is a strategic initiative undertaken by organizations to manage cloud adoption effectively, ensure governance, and optimize cloud resources. This research paper explores the pivotal role of leadership in establishing a CCoE, analyzing the various leadership styles, strategies, and challenges faced in the process. By examining case studies and best practices, this paper aims to provide a comprehensive understanding of how effective leadership can drive the successful implementation and operation of a CCoE, ultimately contributing to organizational growth and innovation.

The explosion of Cloud computing propaganda has forced many companies to quickly move towards this new technology. Particularly given the current economic climate it seems like a prudent way to dynamically increase and decrease infrastructure at low cost. However, past experience with SOA has taught us that lack of commercial adaption and a proliferation of unusable standards may hinder this technology. Support from IBM and Microsoft for cloud is promising and leads to the need for strong design of cloud based systems to ensure quality and productivity. Issues already identified in Grid Computing and SOA will certainly prove important in the design of cloud based systems Due to the speed of network development due to cloud architectures, an increasing level of importance must be placed on the design to regulate issues such as: instance access control, regulatory issues, development practices, security and practical operational issues. Capturing and discussing best practices on these subjects will contribute to a healthy movement in the right direction for those who will develop the Service Cloud.

Ce cours vous introduit au Cloud Computing, en explorant ses concepts fondamentaux, son évolution et son impact sur l'innovation technologique ainsi que sur les modèles d'affaires modernes. Vous découvrirez les différents modèles de déploiement du Cloud, notamment public, privé et hybride, ainsi que les services associés tels que l'IaaS, le PaaS et le SaaS. Nous aborderons ensuite les technologies sous-jacentes du Cloud, notamment la virtualisation, le stockage distribué et les réseaux, qui permettent une gestion flexible et scalable des ressources. Le cours traite également des enjeux de sécurité et de gouvernance dans le Cloud, en mettant l'accent sur les meilleures pratiques pour protéger les données et gérer les risques de manière conforme. Nous discuterons des stratégies pour gérer les incidents et garantir la résilience des services Cloud, y compris les techniques de reprise après sinistre. Enfin, vous découvrirez les principaux avantages du Cloud Computing (flexibilité, réduction des coûts, scalabilité), ainsi que les défis associés, tels que la gestion de la performance, la sécurité et la dépendance aux fournisseurs.

The paper presents and analyzes the results of unstructured open interviews with seven professors of basic art subjects and with two groups of twenty-five students of all levels of study at the Applied Arts and Design Program of the Academy of Arts, University of Novi Sad. The interviews were first conducted immediately after the state of emergency was lifted in May and June 2020, and then in October 2023, with the aim of finding out which channels of communication for online teaching these professors had chosen, which channels they would choose today in a similar situation, and what the opinion of the art students about their choices is. Based on the results of the study, it was determined that the professors initially used less suitable channels of communication for online teaching of art subjects, because they never had training for online teaching, so most of them used e-mails. Over time, other possibilities were explored, so both the professors and the students chose Google Meet and Discord as the most desirable means. However, everyone agreed that online teaching cannot fully satisfy the needs of teaching art subjects in higher education, although some platforms turned out to be very useful, so professors still use them today as an additional form of communication with their students.

Clouds allow enterprises to increase or decrease their resource allocation on demand in response to changes in workload intensity. Virtualization is one of the building blocks for cloud computing and provides the mechanisms to implement the dynamic allocation of resources. These dynamic reconfiguration actions lead to performance impact during the reconfiguration duration. In this paper, we model the cost of reconfiguring a cloud-based IT infrastructure in response to workload variations. We show that maintaining a cloud requires frequent reconfigurations necessitating both VM resizing and VM live migration, with live migration dominating reconfiguration costs. We design the CosM ig model to predict the duration of live migration and its impact on application performance. Our model is based on parameters that are typically monitored in enterprise data centers. Further, the model faithfully captures the impact of shared resources in a virtualized environment. We experimentally validate the accuracy and effectiveness of CosM ig using microbenchmarks and representative applications.

Cloud Computing is one of the most pervasive ICT changes of the last few years. Usually, Clouds offer a variety of Services, which are accessible over the Internet. These Services are regulated by some contracts called Service Level Agreements between Service providers and customers. The SLAs have already been introduced in Service Oriented Architectures in situations where some computing services need to be structured and regulated. In an SLA, the constraints of use, the duties and responsibilities of the parties involved, the charges and the service levels to guarantee, etc., are clearly stated by dedicated clauses. Despite the efforts made in systems security and the standardisation of SLAs, Cloud Services continues to suffer from various cybercriminal attacks, and unfortunately this phenomenon is likely to escalate within the next few years. It becomes urgent to take some countermeasures against these illegal practices to increase both the customer trust and quality of services of such new technologies. One of the alternatives for this phenomenon is to provide an efficient cloud Forensic Readiness System (FRS) to prevent and alert the provider and/or customer of any suspect attacks or strange behaviour. Much attention has been given to FRSs and they have certainly moved from simple log files and monitoring to very sophisticated components involving both human experts and computer analysis tools. In this paper we study the effect of SLAs on FRSs. As SLAs may be different from one jurisdiction to another we believe that FRSs should also comply with jurisdiction for more efficiency and speed of isolating and resolving forensic cases. Therefore, we propose an FRS that takes into account automatically SLAs and issue warnings and alerts to its users (providers and consumers) based on the jurisdiction and the nature of security breach and attacks. These SLAs are presented to the system as a set of rules (clauses). This will also prevent illegal data transfers and communications among different jurisdictions. Part of this paper will be dedicated to the formalisation of these SLAs and study its consequences on the FRS architecture and functioning. The rest of the paper will be dedicated to the design and development of the FRS reference architecture integrating the proposed SLA formal model.

As organizations increasingly migrate to cloud-based infrastructures, managing identity and access to sensitive data has become a critical concern. Identity and Access Management (IAM) systems play a pivotal role in ensuring that only authorized users and devices can access specific resources, helping to mitigate security risks and comply with stringent regulations. However, implementing IAM at scale in cloud environments presents several challenges, ranging from scalability and performance issues to security risks and compliance requirements. This paper explores effective project management strategies for large-scale IAM implementations in cloud-based environments. We begin by outlining the challenges organizations face when deploying IAM solutions in complex cloud ecosystems, including multi-cloud integration, vendor lock-in, and the evolving regulatory landscape. We examine the security risks associated with IAM in the cloud and discuss the implementation of advanced security models such as Privileged Access Management (PAM) and Zero Trust Security to mitigate these risks. In addition, we explore how agile project management methodologies can be leveraged to ensure flexible, iterative, and successful IAM deployments. The paper also presents real-world case studies from diverse industries, showcasing how organizations have overcome common IAM implementation challenges and achieved effective identity management at scale. Furthermore, we provide an in-depth analysis of emerging technologies such as Artificial Intelligence (AI), Machine Learning (ML), and blockchain, and how they are transforming IAM strategies in cloud environments. Finally, the paper offers a future outlook on IAM, predicting how evolving technologies and methodologies will shape the next generation of IAM solutions, including the integration of quantum computing and decentralized identity management. This paper serves as a comprehensive guide for IT professionals, security architects, and project managers tasked with implementing IAM solutions in cloud-based infrastructures, offering both practical insights and strategic recommendations to navigate the complexities of large-scale IAM projects.

In today's digital age, businesses heavily rely on information technology (IT) infrastructure for their operations. Disruptions to this infrastructure, whether caused by natural disasters, cyberattacks, or human error, can have severe consequences, including financial losses, reputational damage, and business interruption. Disaster Recovery (DR) plans are crucial for mitigating these risks by enabling organizations to quickly recover from such events and resume normal operations. However, traditional DR processes often involve manual steps, which can be time-consuming, error-prone, and slow, potentially exacerbating the impact of a disaster. This research paper explores the critical role of automation in modernizing DR strategies. We delve into the key aspects of DR automation, including its benefits, challenges, best practices, and emerging technologies. We also present a comprehensive framework for implementing an automated DR solution, considering factors such as risk assessment, technology selection, testing and validation, and ongoing maintenance.

National Center for Supercomputing Applications University of Illinois Champaign-Urbana, Illinois Center for Analysis and Prediction of Storms University of Oklahoma Norman, Oklahoma Rice University Houston, Texas University of Indiana Bloomington, Indiana University of Alabama in Huntsville Huntsville, Alabama Rutgers University New Brunswick, New Jersey Lawrence Berkeley National Laboratory Berkeley, California Georgia Institute of Technology Atlanta, Georgie University of Minnesota Minneapolis, Minnesota Portland State University Portland, Oregon University of Illinois Champaign-Urbana, Illinois Pacific Marine Environmental Laboratory Seattle, Washington University of Miami Miami, Florida

Cloud computing offers unparalleled scalability and agility, but it also introduces complexities in managing and controlling costs. This paper explores the critical role of cost tagging strategies in enhancing cloud visibility and accountability. By implementing a robust tagging framework, organizations can gain granular insights into their cloud spending, identify cost inefficiencies, and foster a culture of cost responsibility across teams. Cost tagging is not merely a technical exercise; it's a strategic imperative for organizations embracing cloud computing. By implementing a welldefined tagging strategy, businesses can gain greater control over their cloud costs, improve operational efficiency, and unlock the full potential of their cloud investments.Improving cloud visibility and accountability through cost tagging strategies involves the systematic use of metadata tags to categorize and track cloud resources. By attaching key-value pairs to cloud assets, organizations gain detailed insights into their cloud usage and expenses. This approach facilitates accurate cost allocation, enhances financial reporting, and promotes accountability by attributing costs to specific projects, departments, or business units. Consistent and standardized tagging, coupled with automation, ensures that all resources are accurately labeled, reducing human error and improving overall cloud financial management. This strategy empowers organizations to optimize their cloud spending and align it with business objectives.

The rapid adoption of cloud computing has brought about numerous advantages, including scalability, flexibility, and cost-efficiency. However, it has also raised concerns regarding the environmental impact of extensive data center operations and energy consumption. This research paper aims to explore the design of sustainable cloud solutions using the Well-Architected Framework, with a focus on minimizing environmental footprint while maintaining operational excellence.The primary purpose of this study is to identify best practices for designing sustainable cloud solutions and to demonstrate how the Well-Architected Framework can be effectively applied to achieve sustainability goals. By examining key principles and case studies, the paper seeks to provide a comprehensive understanding of sustainable cloud architecture.

Fault-tolerant systems are crucial in environments where uninterrupted service is mandatory and also in environments where continuous service is critical, such as in cloud computing and high-availability systems. The main goal of this research is to introduce a fault-tolerant framework that can effectively manage faults without leading to system failures, ensuring seamless service delivery. The proposed system follows a two-phase approach, incorporating fault detection and correction mechanisms that are designed to maintain system integrity and minimize performance degradation. Upon identifying a fault, the system deploys a correction technique aimed at preventing Service Level Violations (SLV), which could otherwise disrupt the service and breach Service Level Agreements (SLA). This fault-handling process is designed to mitigate interruptions while enhancing overall system performance. The proposed solution demonstrates a significant improvement in reducing SLA violations, achieving a reduction to 98.76%. Additionally, the efficiency of the system is enhanced through an increase in the task success rate, further ensuring the reliability of service. To validate its effectiveness, the performance of this fault-tolerant system has been benchmarked against other contemporary algorithms such as First Come First Serve (FCFS), Priority-based, and CD algorithms. The results reveal that the proposed approach offers notable improvements in response times, reducing them by 23%, 16%, and 33.1% respectively, in comparison to the FCFS, Prioritybased, and CD algorithms. These results demonstrate the system's superior capability in handling faults and maintaining service quality, making it an ideal choice for environments where uninterrupted service is mandatory.

In this study, we investigate the characteristics of global horizontal irradiance enhancement events induced by clouds over Thessaloniki for the period 1994-2023 using data recorded every one minute. We identified the cloud enhancement (CE) events by creating an appropriate cloud-free irradiance reference using a radiative transfer model and aerosol optical depth data from a collocated Cimel sun photometer and a Brewer spectrophotometer. We found a trend in CE events of + 112 ± 35 cases/year, and a trend in the corresponding irradiation of + 329.9 ± 112.0 kJ/year. To our knowledge, such long-term changes in CE events have not been presented in the past. The peak of the CE events was observed during May and June. CE events with duration longer than 10 min are very rare (< 8%), with exceptions lasting over an hour and up to 140 min. Finally, we have detected enhancements above the total solar irradiance at the top of the atmosphere for the same solar zenith angle of up to 204 W/m 2 , with the 75 % of the cases below 40 W/m 2. Most of these extreme events occur in springearly summer, with a secondary peak in autumn.

Changing trends in technologies, notably cheaper and faster memory hierarchies, have made it worthwhile to revisit many hardware-oriented design decisions made in previous decades. Hardware-oriented designs, in which one uses special- purpose hardware to perform some dedicated function, are a response to a high cost of executing instructions out of memory; when caches are expensive, slowor in scarce supply, it is a perfectly reasonable to use hardware that do not compete with user applications for cache space and do not rely on the performance of the caches. In contrast, when the caches are large enough to withstand competition between the application and operating system, the cost of executing operating system functions out of the memory subsystem decreases significantly and software-oriented designs become manageable.

The emergence of various technologies has since pushed researchers to develop new protocols that support high density data transmissions in Wide Area Networks. Many of these protocols are TCP protocol variants, which have demonstrated better performance in simulation and several limited network experiments but have limited practical applications because of implementation and installation difficulties. On the other hand, users who need to transfer bulk data (e.g., in grid/cloud computing) usually turn to application level solutions where these variants do not fair well. Among protocols considered in the application level solutions are UDP-based protocols, such as UDT (UDP-based Data Transport Protocol) for cloud /grid computing. Despite the promising development of protocols like UDT, what remains to be a major challenge that current and future network designers face is to achieve survivability and security of data and networks. Our previous research surveyed various security methodologies which led to the development of a framework for UDT. In this paper we present lowerlevel security by introducing an Identity Packet (IP) and Authentication Option (AO) for UDT.