ABSTRACT This paper addresses the critical intersection of IT Infrastructure Library (ITIL) processes and cloud computing adoption, a relationship requiring structured examination despite ITIL's established role in IT service management. Organizations migrating to the cloud face complexities in risk reduction and seamless transfer, making the synergy between ITIL and cloud strategies essential. Although ITIL is recognized for enhancing business operations, measuring performance, and reducing costs, its specific influence on cloud‐centric ITIL service strategy execution needs further elucidation. Employing a systematic literature review (SLR) methodology, this study analyzes recent research to examine how cloud computing influences ITIL service strategy procedures and their sub‐processes. The findings aim to provide a clear understanding of this evolving landscape for IT professionals and researchers. The systematic review reveals that integrating ITIL with cloud computing not only drives substantial cost savings and enhances user experience but also necessitates adapting ITIL processes to the dynamic nature of cloud environments, particularly in areas of security and configuration management.
The Internet of Things (IoT) has been developed progressively because of its effective data transmission. Still, the IoT devices are affected by malware attacks. The malware detection in IoT is complex owing to the varied capacities of devices. The Federated Learning (FL) is utilized for enhancing privacy against malware activities. Hence, this paper develops the Taylor Series-enabled Activity and Trust Aware Federated Learning Aggregation (TaTFedLA) for malware detection. The client and the server are the components in FL, in which steps like data acquisition, preprocessing, feature fusion, and malware detection are performed in the training. The Min-Max normalization is utilized for preprocessing. Deep Kronecker Network (DKN) with a non-correlation similarity is utilized in feature fusion. The Fractional Lotus Effect Optimization-based Deep Belief Neural Network Fused SpinalNet (FLEO_DBNFSpinalNet) using a FL approach is employed for malicious detection. After evaluating the trained model, the server controls model aggregation. Hence, trust is established between the server and the IoT device, and aggregation is done using the Taylor series concept. Moreover, the TaTFedLA-based malware detection achieves the accuracy, Mean Average Precision, False Positive Rate (FPR), loss, Mean Square Error (MSE), Root MSE (RMSE), bandwidth, energy, precision, recall and F-measure, of 94.99%, 94.25%, 0.121, 5.008, 0.107, 0.326, 36.156 Mbps, 484.318 J, 96.14%, 87.90%, and 91.84%.
To address the requirement of latency-sensitive intelligent transportation applications, vehicular fog computing has proven to be a promising paradigm to push the computation towards the vehicles. However, efficient task offloading and scheduling is a challenging optimization problem because of the highly dynamic nature of vehicular environments, heterogeneous resource availability, task dependency, and varying network conditions. To address these issues, this paper proposes a joint task optimization framework that simultaneously optimizes task-to-node assignment and execution scheduling within a unified optimization model. The task scheduling problem is formulated as a two-dimensional bin-packing variant capable of capturing both temporal execution constraints and computational resource limitations. The proposed framework integrates a heuristic-guided genetic algorithm for global exploration with simulated annealing for local refinement, enabling an effective balance between exploration and exploitation. Furthermore, a cost-aware heuristic mutation strategy is introduced to improve workload balancing while reducing communication and computation costs. The framework also incorporates dynamic task adaptation to support real-time task arrivals and changing vehicular network conditions. Extensive simulations are conducted under varying vehicular densities, workload intensities, queue stability conditions, and dynamic task arrival rates. Experimental results demonstrate that the proposed framework consistently outperforms existing approaches in terms of response time, energy consumption, task completion time, offloading cost, and load balancing efficiency. In particular, the proposed achieves an average response time of 0.42 s, an energy consumption of 1200 J, an offloading cost of 100 vehicles, and a task completion time of 620 s for 200 tasks. Scalability analysis also validates the stable performance up to 300 vehicles and 500 computational tasks, while the confidence interval analysis shows the robustness and reliability of the optimization process.
Operators of low Earth orbit (LEO) access networks increasingly diagnose quality-of-service (QoS) degradation with observational machine-learning attribution, yet when the candidate causes are produced by the resource-management policy itself, observational attribution is unsafe: it assigns the degradation to factors that an operator cannot act on or that only proxy the true cause. We treat the satellite-user assignment policy as the object of root-cause analysis and use the assignment simulator as an intervention oracle, decomposing per-user degradation into policy-induced congestion and channel impairment through structural interventions, and contrasting interventional attribution with observational Shapley attribution and constraint-based causal discovery. Across seven scenarios spanning three regions and assignment instances of up to half a million binary variables, policy-induced congestion is the dominant degradation and the load-balancing control recovers per-user throughput by factors of 1.8 to 10 at fixed coverage, with paired tests that remain statistically significant under multiple-comparison correction. Shapley attribution assigns the degradation to geography under its observational and interventional forms, and under the causal form in the main scenario. Constraint-based discovery orients the load relation against its structural direction, and the rank correlation between link signal-to-noise ratio and delivered throughput inverts as offered load grows. The interventional analysis identifies the policy as the actionable cause and separates degradation that the policy can repair from degradation bounded by fleet capacity or by the channel, a distinction that observational root-cause analysis does not provide.
This paper presents the Blockchain-Enhanced Secure Data-Driven Quality of Service (QoS) Framework (BES-DQF) for security and efficiency of wireless sensor networks (WSNs) and Internet of Things (IoT) systems. The framework combines data integrity verification using blockchain, smart contract enforcement, security compliance, and deep learning algorithms for QoS optimization. The novel combination of these technologies ensures that only verified data enters the deep learning model, avoiding adversarial attacks, such as data poisoning and Sybil attacks. Experimental results show that the accuracy of BESDQF in QoS prediction is 94%, and the attack detection rate is 92%, which is better than that of traditional QoS systems, which are only 93% accurate and 61% attack detection. In addition, BES-DQF maintains an acceptable latency overhead (+6.8%) and energy consumption (+5.4%), proving it to be practical for large-scale IoT applications. Our contributions include (1) the development of a secure blockchain-integrated QoS optimization model, (2) the introduction of a novel attack-resistant learning pipeline, and (3) a comprehensive experimental validation to show the superior performance and robustness of BES-DQF in adversarial conditions. Future work will be done on consensus optimization for scalability and federated learning for enhanced privacy and decentralization.
