Energy-efficient computation offloading via deep reinforcement learning in mobility-aware multi-access edge computing systems with diverse users

Expert Systems with Applications, Год журнала: 2025, Номер 286, С. 127942 - 127942

Опубликована: Май 14, 2025

Язык: Английский

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Go Where Energy Can be Saved: A Vision for a Green Infrastructure Evaluation, Optimization, and Alignment System

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 220 - 241

Опубликована: Янв. 1, 2025

Язык: Английский

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Fault reconfiguration control strategy of islanded marine ranching power supply system based on deep reinforcement learning

International Journal of Electrical Power & Energy Systems, Год журнала: 2025, Номер 169, С. 110796 - 110796

Опубликована: Июнь 5, 2025

Язык: Английский

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EETS: An energy-efficient task scheduler in cloud computing based on improved DQN algorithm

Journal of King Saud University - Computer and Information Sciences, Год журнала: 2024, Номер 36(8), С. 102177 - 102177

Опубликована: Авг. 31, 2024

Язык: Английский

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Fuzzy Reinforcement Learning Algorithm for Efficient Task Scheduling in Fog-Cloud IoT-Based Systems

Journal of Grid Computing, Год журнала: 2024, Номер 22(4)

Опубликована: Сен. 23, 2024

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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"Fault Reconfiguration Control Strategy of Isolated Ocean Ranch Power Supply System Based on Deep Reinforcement Learning"

Опубликована: Янв. 1, 2024

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Язык: Английский

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Fault Reconfiguration Control Strategy of Isolated Marine Ranching Power Supply System Based on Deep Reinforcement Learning

Опубликована: Янв. 1, 2024

Язык: Английский

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Integral-Valued Pythagorean Fuzzy-Set-Based Dyna Q+ Framework for Task Scheduling in Cloud Computing

Sensors, Год журнала: 2024, Номер 24(16), С. 5272 - 5272

Опубликована: Авг. 14, 2024

Task scheduling is a critical challenge in cloud computing systems, greatly impacting their performance. nondeterministic polynomial time hard (NP-Hard) problem that complicates the search for nearly optimal solutions. Five major uncertainty parameters, i.e., security, traffic, workload, availability, and price, influence task decisions. The primary rationale selecting these parameters lies of accurately measuring values, as empirical estimations often diverge from actual values. integral-valued Pythagorean fuzzy set (IVPFS) promising mathematical framework to deal with parametric uncertainties. Dyna Q+ algorithm updated form Q agent designed specifically dynamic environments by providing bonus rewards non-exploited states. In this paper, enriched IVPFS make intelligent performance proposed scheduler tested using CloudSim 3.3 simulator. execution reduced 90%, makespan also operation cost below 50%, resource utilization rate improved 95%, all meeting desired standards or expectations. results are further validated an expected value analysis methodology confirms good scheduler. A better balance between exploration exploitation through rigorous action-based learning achieved agent.

Язык: Английский

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Cloud Data Centre Optimisation for Various Client Classes

Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 461 - 471

Опубликована: Янв. 1, 2024

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