Daily Archives: December 1, 2025

Authors: Mohamad Siraj

Abstract: This study examines the impact of artificial intelligence (AI) on human resource efficiency among secondary teachers in international schools. While AI is increasingly promoted as a means to reduce teacher workload and enhance productivity, empirical evidence from school settings—particularly international schools—remains limited. The research focuses on how AI is used in teachers’ work, how it affects perceived workload and efficiency, and how organisational conditions shape these effects. A quantitative, cross-sectional survey design was employed. Data were collected from 150 secondary teachers working in 18 international schools, using a structured online questionnaire. The instrument captured AI usage patterns, perceptions of AI (perceived usefulness, perceived ease of use, AI anxiety, autonomy), HR-efficiency outcomes (perceived administrative workload, instructional efficiency, overall efficiency, job satisfaction) and organisational factors (leadership support, training and infrastructure). Descriptive statistics, reliability and factor analyses, correlations and multiple regression models were used to analyse the data. Findings indicate that AI is widely used for lesson planning, resource creation and assessment, but less so for administrative work and rarely for pastoral care or live classroom interaction. Teachers generally perceive AI as useful and moderately easy to use, yet administrative workload remains high. Perceived usefulness and actual AI usage are strong positive predictors of instructional and overall efficiency, and are associated with somewhat lower perceived administrative workload. AI anxiety is linked to higher workload and lower efficiency. Organisational support—through leadership, training and clear policies—consistently amplifies positive outcomes and reduces anxiety. The study concludes that AI currently offers incremental rather than transformative efficiency gains. Its contribution to human resource efficiency and teacher well-being depends on strategic, task-focused implementation and supportive organisational conditions, rather than on technology alone. Recommendations are offered for school leaders, HR practitioners and teachers, alongside directions for future research on AI, workload and sustainability in international education.

DOI: http://doi.org/10.5281/zenodo.17785987

Published by: vikaspatanker

Authors: Ashok Kumar

Abstract: Artificial Intelligence (AI) has revolutionized predictive performance tuning in cloud computing environments, offering significant advancements in resource allocation, fault detection, and autonomic optimization. In an era marked by increasing computational complexity, unpredictable traffic patterns, and heightened demands for availability, integrating AI into cloud operations enables proactive identification and mitigation of latency, bottlenecks, and system inefficiencies. This abstract provides a concise overview of how AI-driven techniques—such as machine learning models, deep neural networks, and reinforcement learning algorithms—have become indispensable for predictive analytics, facilitating dynamic resource scaling, workload balancing, and anomaly detection. AI systems leverage vast datasets generated by cloud infrastructures to uncover hidden patterns, optimize service level agreements (SLAs), and deliver high-performance computing with reduced costs and improved reliability. Challenges remain, especially regarding model interpretability, real-time adaptability, and ethical deployment. Nevertheless, the synergistic evolution of AI and cloud computing stands poised to redefine best practices in predictive performance tuning, fostering new paradigms of automation, resilience, and intelligence in the digital ecosystem.

DOI: http://doi.org/10.5281/zenodo.17777827

Published by: vikaspatanker

Authors: Arjun Prasad

Abstract: Artificial intelligence (AI) has emerged as a transformative force across numerous technological domains, with its impact acutely felt in the design and operation of modern data centers. As the demand for cloud services, big data analytics, and internet-based applications surges, data centers have grown exponentially in size and complexity, concurrently escalating their energy consumption. Addressing energy efficiency within these large-scale computing infrastructures is paramount not only from an operational cost perspective but also for environmental sustainability. AI-based workload schedulers have been increasingly adopted as innovative solutions to optimize resource utilization and curtail energy wastage. These intelligent schedulers leverage machine learning algorithms, predictive analytics, and real-time monitoring to dynamically allocate workloads based on energy profiles, cooling capacities, and computing requirements. The integration of AI fosters adaptive scheduling strategies that can respond to fluctuating workloads, minimize idle hardware, and optimize server usage, thereby enhancing energy efficiency. This article comprehensively explores the multifaceted impact of AI-driven workload scheduling on the operation of energy-efficient data centers. It delves into state-of-the-art AI scheduling techniques, mechanisms for workload prediction, energy consumption modeling, and the synergies between hardware infrastructure and intelligent scheduling systems. Furthermore, the article discusses challenges such as scalability, algorithmic complexity, and integration with existing data center management frameworks. By synthesizing contemporary research findings and industry practices, this work aims to provide a detailed understanding of how AI can revolutionize energy management in data centers, ultimately contributing to reduced carbon footprints and sustainable growth in the digital era.

DOI: http://doi.org/10.5281/zenodo.17777795

Published by: vikaspatanker

Authors: Meera Reddy

Abstract: The adoption and integration of automated compliance tools have revolutionized cloud governance, transforming how organizations ensure regulatory adherence, security, and operational efficiency. These tools leverage automation, artificial intelligence, and real-time monitoring to streamline compliance processes that were traditionally manual, time-consuming, and error-prone. Automated compliance tools enable continuous compliance monitoring, instant remediation of violations, and comprehensive evidence gathering, which collectively enhance governance frameworks' effectiveness and responsiveness. With multi-cloud environments becoming standard, the complexity of governance increases, making automation indispensable to maintaining oversight, cost management, and regulatory compliance across diverse platforms. AI-driven predictive analytics empower these tools to detect anomalies and risks proactively, facilitating smarter policy enforcement and reducing human intervention. Furthermore, automated compliance reduces operational expenses by minimizing manual audit preparation and accelerating reporting and decision-making cycles. This article explores how automated compliance tools improve cloud governance efficiency through policy automation, integration into DevOps pipelines, and real-time compliance dashboards. It also examines best practices for adopting these tools, challenges faced during implementation, and their impact on security, cost optimization, and regulatory alignment in cloud ecosystems. The discussion is framed within the context of evolving compliance frameworks and the pressing need for scalable, adaptive governance strategies essential to modern cloud operations.

DOI: http://doi.org/10.5281/zenodo.17777729

Published by: vikaspatanker

Authors: Rohit Desai

Abstract: Quantum-safe cryptography, also known as post-quantum cryptography, is rapidly emerging as a cornerstone for future-proofing cloud security architectures. As quantum computing accelerates toward practical realization, current cryptographic schemes—especially those foundational to cloud trust models—face an existential threat due to quantum algorithms’ potential to break widely utilized methods such as RSA and ECC. This article offers a comprehensive exploration of the transformative shift toward quantum-safe cryptographic primitives, detailing the strategies cloud service providers, enterprises, and governments are deploying to preempt the quantum risk. It delves into the integration challenges, operational complexity, regulatory mandates, performance considerations, and ecosystem readiness associated with post-quantum security. By examining the evolving landscape of cryptographic standards, the interplay between hardware and software solutions, and the required architectural adaptations, the article provides a nuanced forecast for the next decade of cloud security. Across eight thematic sections, it synthesizes insights from leading research initiatives, governmental policy frameworks, and industry trial deployments, presenting forward-thinking recommendations for stakeholders navigating the quantum leap. The necessity for comprehensive cryptographic agility, layered security frameworks, and global collaboration is emphasized, ensuring that data sovereignty, confidentiality, and integrity are preserved across distributed cloud environments. The analysis concludes with a critical assessment of future-proofing strategies, advocating a multidisciplinary approach to achieving quantum resilience in cloud platforms. The article is tailored for professionals, researchers, and policymakers involved in cloud security, cryptography, and digital trust ecosystems, equipping them with actionable intelligence and a strategic roadmap for quantum-safe transformation.

DOI: http://doi.org/10.5281/zenodo.17777695

Published by: vikaspatanker

Authors: Priya Narayanan

Abstract: Neural network optimization has become a critical driver in advancing real-time cloud decision systems, fundamentally transforming how cloud resources and workloads are managed dynamically and efficiently. As cloud computing infrastructures grow in complexity and scale, neural networks—especially deep learning models—offer powerful capabilities to process vast amounts of data, detect intricate patterns, and predict future states of cloud environments with high accuracy. These capabilities enable cloud platforms to allocate resources, balance loads, and automate decision-making in real-time, thus improving performance, reducing latency, enhancing cost-effectiveness, and boosting energy efficiency. This article explores the multifaceted impact of neural network optimization on cloud decision systems, examining key techniques such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), Bayesian neural networks (BNNs), and graph neural networks (GNNs). It discusses the integration of these models in workload forecasting, resource allocation, and system adaptability, highlighting their role in enabling cloud environments to respond proactively to changing demands. Furthermore, the analysis covers challenges such as model interpretability, real-time processing constraints, and scalability. The article concludes with insights on emerging trends and future directions, emphasizing how neural network optimization will continue to shape the agility and intelligence of cloud decision-making frameworks.

DOI: http://doi.org/10.5281/zenodo.17777606

Published by: vikaspatanker

Authors: Maya Fernandes

Abstract: Blockchain technology has emerged as a groundbreaking innovation with the potential to enhance transparency, security, and trust in various digital ecosystems. Its integration into cloud service auditing represents a significant evolution in how organizations manage and verify cloud-based operations. Cloud services, being foundational to modern IT infrastructure, demand rigorous auditing to ensure compliance, reliability, and security. However, traditional auditing methods often face challenges related to trustworthiness, data integrity, and real-time verification. Blockchain’s decentralized, immutable ledger offers a promising solution to these limitations by providing an auditable, tamper-resistant record of transactions and events within cloud environments. This article explores the multifaceted impact of blockchain integration on transparent cloud service auditing. It begins with an analysis of the existing challenges in cloud auditing and the essential attributes for effective auditing frameworks. Subsequently, it delves into the fundamental principles of blockchain technology and its synergy with cloud services. Through specific use cases and the evaluation of emerging frameworks, the discussion highlights how blockchain can transform cloud auditing by enabling continuous monitoring, enhancing data provenance, and facilitating regulatory compliance. The article also addresses potential concerns, including scalability, privacy, and integration complexities, along with current advancements aimed at overcoming these hurdles. By providing a comprehensive overview, this article underscores blockchain’s transformative potential to establish more transparent, trustworthy, and efficient cloud auditing mechanisms that benefit service providers, auditors, and end-users alike.

DOI: http://doi.org/10.5281/zenodo.17777553

Published by: vikaspatanker

Authors: Akash Gupta

Abstract: Serverless computing has emerged as a transformative paradigm in enterprise IT, fundamentally altering how organizations approach infrastructure management and cost efficiency. This model abstracts away the complexities of server administration, allowing enterprises to focus solely on developing and deploying applications without the burden of maintaining underlying hardware. By leveraging a pay-as-you-go pricing mechanism, serverless computing enables significant cost reductions, as organizations are billed exclusively for the actual resources consumed during code execution rather than for idle infrastructure. Such dynamic resource allocation facilitates optimal usage, particularly beneficial for companies with unpredictable or fluctuating workloads. Furthermore, serverless architectures streamline development workflows by eliminating the need for manual scaling and capacity planning, resulting in improved operational agility and innovation throughput. Nonetheless, enterprises must be mindful of potential challenges, including workload-specific cost unpredictability, cold start latencies, and vendor lock-in. The strategic adoption of serverless models, underpinned by comprehensive cost monitoring and optimizations, can unlock substantial benefits in terms of both direct and indirect cost savings, agility, and overall IT modernization. As this article will demonstrate, serverless computing stands at the intersection of technological advancement and cost optimization—reshaping enterprise architectures for the demands of today’s digital economy.

DOI: https://doi.org/10.5281/zenodo.17776418

Published by: vikaspatanker

Authors: Nisha Choudhury

Abstract: The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is revolutionizing industrial automation ecosystems. This integration is driving the transformation of traditional manufacturing and production processes into smart, connected systems that optimize operations, enhance productivity, and facilitate predictive maintenance. IoT facilitates the collection and communication of vast amounts of real-time data from sensors, devices, and machinery across an industrial environment. Coupling this with AI’s ability to analyze complex data patterns, predict outcomes, and automate decision-making processes creates unprecedented opportunities for efficiency and innovation in industrial automation. The synergy between IoT and AI fosters smarter factories, where machines self-optimize, autonomous robots collaborate with human workers, and supply chain logistics are seamlessly managed. This article explores how this dynamic convergence alters industrial workflow, impacts operational resilience, improves safety standards, and cultivates new business models. It also examines the challenges related to cybersecurity, data privacy, and system integration that accompany this digital evolution. Ultimately, the IoT-AI alliance is not only reshaping industrial automation with smarter, more adaptive ecosystems but also paving the way for Industry 4.0 and beyond, ushering in a new era where intelligent systems drive industrial competitiveness and sustainability.

DOI: https://doi.org/10.5281/zenodo.17776309

 

Published by: vikaspatanker

Authors: Priyanka Sharma

Abstract: Hybrid storage systems have increasingly become a pivotal architecture in the realm of large-scale data analytics, addressing the ever-growing demand for managing vast volumes of diverse data with speed and efficiency. By integrating multiple types of storage media, typically solid-state drives (SSDs) and hard disk drives (HDDs), hybrid storage optimizes data accessibility and throughput by leveraging the performance benefits of faster storage technologies alongside the cost-effectiveness and capacity of traditional drives. This synergy is particularly crucial in large-scale data analytics, where substantial datasets must be rapidly processed to derive actionable insights, impacting industries such as finance, healthcare, telecommunications, and scientific research. The influence of hybrid storage systems transcends mere data warehousing, affecting the efficiency of data retrieval, latency, system throughput, and computing cost. These systems support the flexible caching of hot data in faster tiers, while colder, less frequently accessed data remains in slower storage, thereby creating a dynamic environment that can adapt to workload variations. Furthermore, the architecture of hybrid systems is conducive to scalability and fault tolerance, essential features when dealing with petabyte-scale analytics clusters and distributed frameworks like Apache Hadoop and Spark. This article explores the architecture of hybrid storage systems, the performance implications they bear on large-scale data analytics, and the cost-performance balance they offer. Additionally, it examines case studies demonstrating improvements in real-world analytics applications, the challenges in managing hybrid storage environments, and future trends in storage technologies impacting analytics performance. By understanding these aspects, enterprises can better architect their storage infrastructure to meet the demanding requirements of data-intensive analytics workloads.

DOI: https://doi.org/10.5281/zenodo.17776151

Published by: vikaspatanker