Modified Neural Network Used for Host Utilization Predication in Cloud Computing Environment

Computers, materials & continua/Computers, materials & continua (Print), Год журнала: 2025, Номер 82(3), С. 5185 - 5204

Опубликована: Янв. 1, 2025

Язык: Английский

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Wind power generation prediction using LSTM model optimized by sparrow search algorithm and firefly algorithm

Energy Informatics, Год журнала: 2025, Номер 8(1)

Опубликована: Март 11, 2025

Язык: Английский

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EEVMC: An Energy Efficient Virtual Machine Consolidation Approach for Cloud Data Centers

IEEE Access, Год журнала: 2024, Номер 12, С. 105234 - 105245

Опубликована: Янв. 1, 2024

The dynamic landscape of cloud computing design presents significant challenges regarding power consumption and quality service (QoS). Virtual machine (VM) consolidation is essential for reducing usage enhancing QoS by relocating VMs between hosts. OpenStack Neat, a leading framework VM consolidation, employs the Modified Best-Fit Decreasing (MBFD) placement technique, which faces issues related to energy QoS. To address these issues, we propose an Energy Efficient Consolidation (EEVMC) approach. Our method introduces novel host selection criterion based on incurred loss during identify most efficient host. For validation, conducted simulations using real-time workload traces from Planet-Lab Materna over ten days, leveraging latest CloudSim toolkit compare our approach with state-of-the-art techniques. Planet-Lab's workload, EEVMC shows reduction in 80.35%, 59.76%, 21.59%, 7.40%, fewer system-level agreement (SLA) violations 94.51%, 94.85%, 47.17%, 17.78% when compared (MBFD), Power-Aware Best Fit (PABFD), Medium Power (MFPED), Power-Efficient (PEBFD), respectively. Similarly, Materna, achieves 16.10%, 61.0%, 4.94%, 4.82%, SLA 76.99%, 88.88%, 12.50%, 48.65% against same benchmarks. Additionally, Loss-Aware Performance (LAPED) significantly reduces total number migrations time per active host, indicating substantial improvement efficiency.

Язык: Английский

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EBWO‐GE: An innovative approach to dynamic VM consolidation for cloud data centers

Concurrency and Computation Practice and Experience, Год журнала: 2024, Номер unknown

Опубликована: Сен. 30, 2024

Summary Cloud data centers (CDCs) have revolutionized global computing by offering extensive storage and processing capabilities. Nevertheless, the environmental impact of these processes, including their substantial energy consumption carbon emissions, calls for implementing more efficient techniques. Efficient virtual machine (VM) consolidation is crucial in optimizing resource utilization reducing consumption. Current methods enhancing efficiency often lead to issues such as service level agreements (SLAs) violations quality services (QoS) degradation. This study presents a novel approach host selection using grey‐extreme (GE) learning model, which accurately predicts over underutilized hosts. In addition, VM placement technique called enhanced black widow optimization (EBWO) utilizes heuristic techniques differential evolutionary optimize placement. The proposed dynamic optimizes while meeting strict SLA requirements QoS metrics CDCs. Extensive analyses were conducted Cloudsim toolkit validate approach's effectiveness. These encompassed conditions random workloads heterogeneous environments. simulation results showed that GE‐EBWO outperforms other improves 12%–15%. it significantly decreases migrations 11%–14% compared advanced methods. validates practicality moving towards environmentally friendly

Язык: Английский

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Enhancing virtual machine placement efficiency in cloud data centers through fluctuations-aware resource management

Computers & Electrical Engineering, Год журнала: 2024, Номер 122, С. 109885 - 109885

Опубликована: Дек. 5, 2024

Язык: Английский

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E-Waste Management

Practice, progress, and proficiency in sustainability, Год журнала: 2024, Номер unknown, С. 56 - 73

Опубликована: Фев. 7, 2024

Green computing can be described as efficient resource management in distributed environments such cloud, fog, and edge environments. In green computing, refers to the eco-friendly environment with environmental responsibility efficiently manage resources. Mainly, two significant reasons are associated global warming issues from perspective. They high power consumption of cloud datacenters CO2 emission rate. According many survey reports, every year, alone produce nearly 90 million metric tons into environment, now, this has become one primary for issues. Also, it stated that rate would increase by 8% year if did not identify proper control measurements. Therefore, is crucial enhance e-waste increasing efficiency usage minimizing consumption, rate, inefficient recycling policies, etc.

Язык: Английский

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A Two-Stage Ensemble Approach for Analysis of Optimizing Customer Churn with Lime Interpretability

Опубликована: Фев. 9, 2024

Businesses must outbid suppliers to win over new clients in a very competitive industry. Since client retention directly affects company's revenue, it is prominent topic for research. can prevent attrition by proactively addressing when occurs. A two stage predictive ensemble method proposed with Gaussian Naive Bayes, Perceptron, Gradient Boosting Classifier, Decision Tree, K-Nearest Neighbors, Logistic Regression, and XGBoost used the first model classification, then models like Adaboost, Bagging Stacking, Voting are second increase forecast accuracy top four chosen stage. The LIME Explainer algorithm also analyzes data instance adjustments their effect on predictions, improving interpretability "black box" models. This two-step approach addresses issues crucial decision-making, optimizing final model's power interpretability.

Язык: Английский

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An Enhanced Virtualization of Resources for High Performance Applications in Cloud Computing Using Deep Regression Model

Опубликована: Март 15, 2024

This paper proposes an Enhanced Virtualization of Resources (EVR) system for high performance applications in Cloud Computing. It uses a Deep Regression Model (DRM) to predict the resource requirements application be deployed on Cloud. The model takes into account various parameters like number users, bandwidth requirements, processing time, I/O requests and server capability make accurate predictions. is further optimized with Genetic Algorithm, which mutation, crossover selection operations ensure produces high-accuracy output. resulting then used by EVR decide nodes should allocated best performance. evaluated using metrics such as query response allocation accuracy. Results demonstrate that proposed can provide up 73.3% more efficiency than existing approaches virtualization.

Язык: Английский

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Tri-Model Synergy: Redefining Depression Detection through the Convergence of ANN, CNN, and SVM

Опубликована: Сен. 18, 2024

Язык: Английский

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Программно-аппаратный комплекс распределенного планировщика ресурсов инфокоммуникационной системы облачного центра обработки данных

Управление большими системами., Год журнала: 2024, Номер 109, С. 268 - 292

Опубликована: Май 31, 2024

В связи с возрастающим спросом на облачные сервисы разработка новых эффективных методов и алгоритмов планирования ресурсов облачных центров обработки данных является актуальной задачей, о чем свидетельствует непрекращающийся поток работ, посвященных данной теме. Критерии лучшего распределения могу быть различными, такие как энергоэффективность, выполнение соглашений об уровне сервиса, надёжность другие. На основе проанализированных работ были выбраны разработаны модели, методы алгоритмы ресурсов, комплекс которых положен в основу предложенной статье архитектуры распределенного планировщика инфокоммуникационной системы облачного ЦОД многокритериальной оптимизации ее характеристик особенностей живой миграции виртуальных машин. Эффективность использованных моделей подтверждена имитационным моделированием. Показано, что предложенный позволяет сократить энергопотребление при выполнении показателей качества обслуживания. With the development of cloud technologies, methods and algorithms for a resource scheduler data centers is an urgent task, as evidenced by continuous flow works devoted to this topic. The criteria best allocation resources can be different, such energy efficiency, fulfillment service level agreements, reliability others. Based on analyzed works, models, distribution were selected developed, complex which forms basis distributed architecture proposed in article infocommunication system center based multi-criteria optimization its characteristics features live migration virtual machines. effectiveness models used has been confirmed simulation modeling makes it possible reduce consumption when meeting quality indicators.

Язык: Русский

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