Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain

Sensors, Journal Year: 2023, Volume and Issue: 23(11), P. 5349 - 5349

Published: June 5, 2023

The emergence of the Internet Things (IoT) and its subsequent evolution into Everything (IoE) is a result rapid growth information communication technologies (ICT). However, implementing these comes with certain obstacles, such as limited availability energy resources processing power. Consequently, there need for energy-efficient intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes data. This paper proposes novel, energy-aware artificial intelligence (AI)-based load balancing model that employs Chaotic Horse Ride Optimization Algorithm (CHROA) big data analytics (BDA) cloud-enabled IoT environments. CHROA technique enhances optimization capacity (HROA) using chaotic principles. proposed balances load, optimizes available AI techniques, evaluated various metrics. Experimental results show outperforms existing models. For instance, while Artificial Bee Colony (ABC), Gravitational Search (GSA), Whale Defense Firefly (WD-FA) techniques attain average throughputs 58.247 Kbps, 59.957 60.819 respectively, achieves an throughput 70.122 Kbps. CHROA-based presents innovative approach to highlight potential address critical challenges contribute developing efficient sustainable IoT/IoE solutions.

Language: Английский

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Review and analysis of secure energy efficient resource optimization approaches for virtual machine migration in cloud computing

Measurement Sensors, Journal Year: 2022, Volume and Issue: 24, P. 100504 - 100504

Published: Oct. 11, 2022

Language: Английский

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A systematic review on effective energy utilization management strategies in cloud data centers

Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2022, Volume and Issue: 11(1)

Published: Dec. 17, 2022

Abstract Data centers are becoming considerably more significant and energy-intensive due to the exponential growth of cloud computing. Cloud computing allows people access computer resources on demand. It provides amenities pay-as-you-go basis across data center locations spread over world. Consequently, consume a lot electricity leave proportional carbon impact environment. There is need investigate efficient energy-saving approaches reduce massive energy usage in servers. This review paper focuses identifying research done field consumption (EC) using different techniques machine learning, heuristics, metaheuristics, statistical methods. Host CPU utilization prediction, underload/overload detection, virtual selection, migration, placement have been performed manage achieve utilization. In this review, savings achieved by compared. Many researchers tried various methods service level agreement violations (SLAV) centers. By heuristic approach, saved 5.4% 90% with their proposed compared existing Similarly, metaheuristic from 7.68% 97%, learning 1.6% 88.5%, 84% when benchmark for variety settings parameters. So, making use could cut down air pollution, greenhouse gas (GHG) emissions, even amount water needed make power. The overall outcome work understand used save

Language: Английский

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Energy-aware QoS-based dynamic virtual machine consolidation approach based on RL and ANN

Cluster Computing, Journal Year: 2023, Volume and Issue: 27(1), P. 827 - 843

Published: Feb. 22, 2023

Language: Английский

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Memory orchestration mechanisms in serverless computing: a taxonomy, review and future directions

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(5), P. 5489 - 5515

Published: Feb. 8, 2024

Language: Английский

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A novel approach for energy consumption management in cloud centers based on adaptive fuzzy neural systems

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(10), P. 14515 - 14538

Published: July 21, 2024

Language: Английский

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SPP: stochastic process-based placement for VM consolidation in cloud environments

Computing, Journal Year: 2025, Volume and Issue: 107(1)

Published: Jan. 1, 2025

Language: Английский

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A relax-and-round optimization algorithm for online NUMA-aware virtual machine placement

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: 271, P. 126653 - 126653

Published: Jan. 31, 2025

Language: Английский

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A Multidimensional Virtual Resource Allocation Framework With Energy‐Aware Physical Resource Mapping for Green Cloud Computing

Concurrency and Computation Practice and Experience, Journal Year: 2025, Volume and Issue: 37(4-5)

Published: Feb. 28, 2025

ABSTRACT Cloud computing has seen a surge in demand, driven by its scalability and cost efficiency. However, the growing energy consumption of data centers poses significant environmental challenges. This study introduces multidimensional resource allocation model designed to allocate place virtual resources an energy‐efficient manner using combinatorial auction approach. Unlike current approaches, which rely on predefined resources, this allows users request with specific features capacities tailored their workflows. Furthermore, it incorporates flexible bidding language that supports simultaneous requests for multiple logical AND/OR relations. The accommodates various centers, allowing indicate preferred locations. Through optimization problem, identifies most resource‐efficient allocations placements. provides mathematical definition formulation problem. Given complexity explores several heuristic methods, including ant colony genetic algorithms. A test case generator is developed simulate real‐life scenarios. effectiveness proposed solutions assessed through experiments, demonstrating these methods can achieve near‐optimal within reasonable timeframes.

Language: Английский

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A discrete chaotic multi-objective SCA-ALO optimization algorithm for an optimal virtual machine placement in cloud data center

Journal of Ambient Intelligence and Humanized Computing, Journal Year: 2020, Volume and Issue: 12(10), P. 9323 - 9339

Published: Nov. 11, 2020

Language: Английский

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