The paper uses the technology–organization–environment (TOE) framework to investigate the determinants of cloud adoption intention among firms operating in the upstream oil and gas industry. The issues of security, regulatory uncertainty, and trust deficit continue to delay cloud implementation despite the clear benefits offered by cloud computing technology. Survey data were collected from 190 credentialed information technology professionals and decision makers drawn from the Society of Petroleum Engineers and the LinkedIn Upstream Oil & Gas professional community. Multiple linear regression and factor analysis were employed to test three composite technology–organization–environment predictors against behavioral adoption intention. The final model accounted for 49.8% of variance in adoption intention (F(3, 186) = 63.535, p < .001, Adj. R2 = .498). Notably, environmental trust, fostered by confidence in cloud providers and regulatory clarity, emerged as the most influential factor, encouraging a sense of security and optimism about digital transformation.
Serverless computing has emerged as an efficient paradigm for scalable and cost-effective cloud application deployment through dynamic resource allocation. However, widely used autoscaling mechanisms, particularly CPU-threshold-based approaches such as the Knative Pod Autoscaler (KPA), often exhibit limited adaptability under highly dynamic and bursty workloads, leading to increased latency, SLA violations, scaling instability, and inefficient resource utilization. To address these limitations, this paper proposes a hybrid congestion-aware autoscaling framework for serverless platforms inspired by TCP slow-start and congestion control principles. The proposed method integrates multiple runtime metrics, including workload intensity, CPU utilization, memory usage, request latency, queue congestion, and TCP-inspired congestion window dynamics, into a unified weighted decision model to achieve both responsiveness and stability. The framework is implemented in a Knative-based Kubernetes environment and evaluated using 100,000 requests under 100 concurrent users, with comparisons against Knative KPA and an LSTM-based predictive autoscaling baseline. Experimental results demonstrate that the proposed approach consistently outperforms both baselines across key performance metrics. Specifically, it reduces P50, P90, and P99 response times to 0.29 s, 0.56 s, and 1.12 s, respectively, compared to 0.41 s, 0.88 s, and 2.31 s in KPA. Average response time is reduced to 0.32 s, while throughput increases to 246.80 req/s. In addition, the proposed method improves resource efficiency to 0.87 and decreases total execution time to 405.19 s. Stability is also significantly enhanced, with scaling oscillation rate reduced to 1.5, SLA violation rate reduced to 1.9%, and cold start delay reduced to 0.49 s. Overall, the results confirm that the proposed TCP-inspired hybrid autoscaling approach significantly improves latency performance, scalability, and resource efficiency in serverless environments.
Cloud computing has become a critical technology for modern businesses, offering scalable services and capabilities. Ensuring the security of cloud infrastructure is vital, as organizations rely heavily on these cloud resources. Although traditional Intrusion Detection Systems (IDS) often struggle to effectively detect and mitigate sophisticated security threats in dynamic cloud environments. This comprehensive survey explores the integration of Machine Learning (ML) and IDS techniques to enhance cloud computing security. The paper examines the key challenges in securing cloud environments, including the evolving nature of security threats and the limitations of conventional IDS. The study delves into the various ML-IDS models proposed to address these challenges. It reviews the fundamental concepts, architectures, and techniques employed in these integrated systems, covering a range of ML and IDS approaches. Furthermore, the survey discusses the emerging trends and future research directions in this field, highlighting the need for improved cloud security.
Traditional payroll systems maintain dedicated infrastructure on rigid batch schedules, yielding idle capacity rates of 97–99% between execution cycles. This paper introduces an artificial intelligence (AI)-enhanced, event-driven framework integrating managed container services with machine learning agents for payroll processing. Empirical analysis of 94 enterprise implementations spanning 500 to 50,000 employees demonstrates 71–74% monthly cost reduction, improvement in payroll accuracy from 94.2% to 99.7%, and an 87% reduction in errors relative to traditional architectures. A quantitative decision framework incorporating payroll complexity, regulatory requirements, and AI model selection is presented, alongside comparative analysis of Amazon Web Services Elastic Container Service Fargate, Azure Container Instances, and Google Cloud Run implementations. Four pseudocode algorithms formalize the event dispatch, anomaly detection, tax form extraction, and pipeline orchestration logic. Real-world validation with an 8,500-employee healthcare organization confirms $906 monthly infrastructure cost with zero compliance violations over 120 days.
Multi-cloud adoption is increasingly driven by resilience, regulatory compliance, and vendor independence. Serverless-container platforms such as AWS Fargate, Google Cloud Run, and Azure Container Instances promise portability through similar abstraction models, yet they exhibit differences in configuration semantics and runtime behavior. This study presents a metric-driven evaluation of multi-cloud compatibility using the Cloud Compatibility System, distinguishing between feature-level configuration coverage and empirical execution reliability. Unified container configurations are deployed across providers under controlled conditions to measure execution success, startup latency, and runtime variability. Results show high execution reliability despite limited feature compatibility, revealing a weak correlation between documented configuration support and runtime behavior. These findings highlight the limitations of feature-centric portability assessments and establish an empirically grounded framework for evaluating interoperability in serverless-container environments.
