The rapid growth of cloud computing and Internet of Things (IoT) applications has intensified the need for efficient resource allocation, energy management, and cost optimization in large-scale data centers. Traditional optimization and machine learning approaches, while effective in static environments, often fail to adapt to dynamic workloads and heterogeneous infrastructures, resulting in performance degradation, increased operational costs, and reduced quality of service (QoS). Addressing these challenges is essential for sustainable and scalable cloud service management. Despite recent advances, existing frameworks still suffer from slow convergence, high sensitivity to learning parameters, and limited scalability in real-world deployments. Furthermore, the imbalance between multiple objectives such as cost, energy, and QoS remains unresolved. In this paper, a DQN-driven resource optimization framework is proposed. The method employs advanced variants of Deep Q-Networks and introduces a multidimensional reward function that integrates virtual machine rental cost, energy consumption, and SLA compliance. By dynamically learning adaptive policies, the framework enables real-time resource allocation and scaling decisions in complex cloud environments. The simulation results demonstrate that, compared to the average performance of the related work, the proposed method achieves a 16.3% reduction in average response time, 7.7% lower energy consumption, and 11.3% reduction in operational cost, while simultaneously improving the task acceptance rate by 6.2%.
Peng Yan, Nahideh Derakhshanfard, Ali Asghar Pourhaji Kazem et al.· International Journal of Com...· 0 citations
In modern decentralized environments, designing systems that are not only efficient and secure but also understandable and trustworthy for end-users has emerged as the central challenge. Blockchain and Federated Learning (FL) have become two seminal technologies that facilitate privacy-preserving and transparent processing of data over distributed networks. Nevertheless, although most current Blockchain-FL architectures possess technical strengths, they are still complex, opaque, and challenging for end-users to engage with, causing low trust and limited usage. In order to fill the gap, this study discusses a Human-Centered Design (HCD)–based framework that couples blockchain and federated learning to reconcile technological robustness with human usability. The framework starts by exploring user needs and designing usable, translucent interfaces, then distributing model training without raw data sharing, while blockchain provides transparency and immutability of updates. User feedback gathered by standard User Experience (UX) metrics—System Usability Scale (SUS) and User Experience Questionnaire (UEQ)—instruct iterative interface and training workflow refinements. Experimental assessments performed over three real-world healthcare datasets—Heart Disease, Breast Cancer Wisconsin, and COVID-19 CT—show that the proposed framework enhances model accuracy by 4.1%, reduces latency by 12.5%, decreases communication cost by 24.6%, and improves the fairness index by 9.7% compared with the FL-only and FL + Blockchain without HCD baselines. In addition, the UX evaluation achieved an SUS score of 89 and a UEQ value of 6.3, indicating a 23% improvement in user satisfaction and trust. In totality, the result verifies that incorporating HCD wisdom into Blockchain-FL systems closes the gap between technical robustness and human experience, preparing the path toward transparent, secure, and user-friendly decentralized systems.
