Current Issue : October-December Volume : 2026 Issue Number : 4 Articles : 5 Articles
Quality-aware service integration in cloud computing information infrastructures typically refers to selecting a suitable subset of services out of those available in order to fulfill a user’s request, considering multiple quality of service (QoS) metrics like response time, cost, availability, and reliability constraints. Since this optimization problem is NP-complete and possesses a large search space, it remains an active topic of cloud computing research. Most metaheuristic techniques developed to solve this problem work under assumptions of relatively static-QoS conditions and require continuous service monitoring in order to function. However, modern cloud environments often exhibit highly dynamic workloads and changing resource availabilities, leading to varying QoS characteristics. This work proposes a hybrid deep learning and metaheuristic-based framework for QoS-aware cloud service integration. It synergizes a deep learning QoS prediction model and the elephant herding optimization (EHO) algorithm for search and delivers near-optimal service integration decisions. EHO was chosen over the most recent/metahybrid evolutionary algorithms due to its empirical efficiency and effectiveness in tackling the large-scale QoS-aware service integration problem. The proposed method consistently outperforms existing baselines like genetic algorithm (GA), particle swarm optimization (PSO), and standalone EHO in terms of QoS metrics measured on a simulated cloud platform with publicly available service datasets. The average response time of the selected services under the proposed method is between 20% and 45% lower, service availability is always above 99.5%, the service cost can be up to 40% lower, and reliability can be increased by about 2%–4%. In addition to these performance gains, the benefits of the proposed framework include decoupling service integration decisions from real-time monitoring of cloud resources, due to QoS prediction. Specifically, a deep learning–based forecasting model is employed to quickly adapt to dynamic/cloud workloads and produce reliable predictions of QoS values even when the underlying cloud infrastructure is changing rapidly. This allows for applying the proposed framework to challenging large-scale and complicated environments like IoT infrastructures, smart cities, and industrial cloud platforms, which need to take scalability, heterogeneity of service compositions, and limited resource capabilities of edge devices into account. This predictive model also offers a valuable tool for designing future cloud orchestration systems, integrating seamlessly with current real-time cloud management platforms. The limitation of this work is the dependency on acquiring enough historical data to train the deep learning model. One possible way to address this is through transfer learning or data augmentation techniques....
Data centers are energy end users with the fastest growing need for electricity in the United States, mainly because of the rapid expansion of cloud computing and artificial intelligence (AI). A substantial portion of this electricity, between 10% and 40%, is used for cooling. As the number of data centers increases and the sector’s energy demand continues to rise exponentially, there is an urgent need to explore the use of alternative energy systems that are more efficient and sustainable. This article explores aquifer thermal energy storage (ATES) as a technically feasible and currently underutilized solution for data center cooling in the United States. Previous case studies from Europe and assessments based in the United States are considered, and the potential of ATES for reducing electricity usage for data centers, which would reduce overall greenhouse gas emissions and support sustainable energy operations....
Gridded precipitation datasets are increasingly used as operational tools, with growing emphasis on cloud-native processing to handle multi-decadal archives through reproducible and auditable workflows. This paper presents an end-to-end pipeline that uses Google Earth Engine for the automated extraction of ERA5-Land precipitation, enabling on-the-fly analysis and targeted spatiotemporal data retrieval. The extracted outputs are subsequently evaluated through station-based comparisons using one linear and one non-linear biascorrection technique. The workflow emphasizes scalable data access, consistent station alignment, and distribution-aware diagnostics for extremes. It is designed to support rapid national screening and to provide a transferable blueprint for hydrometeorological applications....
In modern computing environments characterized by high variability and complex workloads, traditional load-balancing algorithms such as Round Robin and Least Connections are often found to be less effective in distributing tasks and maintaining optimal performance. In this paper, a hybrid load-balancing algorithm is proposed, where the strengths of Fastest Response Load Balancing (FRLB) and Priority-Based Load Balancing (PBLB) are combined. Through this adaptive approach, response times are minimized and load distribution across heterogeneous server environments is balanced more effectively. In the recent literature, the need for enhanced load-balancing solutions that can adapt to dynamic conditions in cloud computing, IoT, and large-scale web services has been increasingly emphasized. By integrating a hybrid mechanism, a robust solution is provided by the hybrid algorithm, which is designed to merge between FRLB and PBLB. As demonstrated through simulations, a noticeable improvement in performance is achieved, with a significant reduction in average response time when compared to FRLB and PBLB. The proposed algorithm outperforms the classical FRLB and PBLB....
Data center management, the foundation of contemporary cloud computing, has made energy saving a top priority. Among other difficulties, the placement of virtual machines (VMs) has a major impact on data center resource and energy usage. Assigning VMs to physical machines (PMs) is a challenging NP-hard problem, especially in large-scale infrastructures where it is computationally infeasible to find an ideal solution. To solve the VM placement problem, the proposed study formulates it as a restricted optimization problem with the goal of preserving performance while lowering energy consumption. The explosive growth of data centers has resulted in higher energy consumption and higher carbon dioxide (CO2) emissions, which are a primary cause of climate change. Globally, governments, energy-focused organizations, and business executives have taken notice of this expanding environmental impact. This study provides a comprehensive analysis of data center energy consumption patterns, environmental effects, and trends in energy consumption. It also suggests doable energy-saving measures, such as installing energy-efficient infrastructure and upgrading air conditioning systems. The paper also presents an improved genetic algorithm–based method that is tailored for energy-conscious VM deployment, successfully striking a balance between computing economy and convergence accuracy. The suggested solution highly increased data centers' energy efficiency by incorporating this strategy within a profile-based virtual resource management model. Additionally, policy suggestions for sustainable data center management are delineated, advancing the more general objective of ecologically conscious cloud computing. Experimental results demonstrate that the proposed method achieves up to 50% reduction in execution time, 48% fewer generations for convergence, and approximately 7% reduction in energy consumption compared to traditional first fit decreasing (FFD) methods. Additionally, the integration of task classification improves energy efficiency by up to 15% and reduces the number of active PMs. These findings highlight the effectiveness of the proposed framework in enabling scalable, energy-efficient, and environmentally sustainable cloud data center management....
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