AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives

Walia, Guneet Kaur; Kumar, Mohit; Gill, Sukhpal Singh

doi:10.1109/comst.2023.3338015

Cited by 29 publications

(5 citation statements)

References 202 publications

(227 reference statements)

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“…[7][8][9] Additionally, some studies have delved into the analysis of offloading techniques within specific contexts such as mobile network environments, [9][10][11] vehicular environments [12][13][14] or IoT. 15 Meanwhile, other research endeavors have aimed to elucidate the broader application of specific techniques to offloading in general. 14,[16][17][18] The rest of this work is organized as follows.…”

Section: Contributions Of This Workmentioning

confidence: 99%

Harnessing idle edge resources for execution of cloud services: A comprehensive review

Trajano,

de Souza

2024

Trans Emerging Tel Tech

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This article presents a comprehensive investigation into voluntary resource sharing at the edge of the internet for cloud service execution. Through a systematic review, 460 scientific articles published between 2019 and 2023 were analyzed, compiling the primary recent contributions in the field. The study presents a taxonomy of the results and highlights the state of the art. Despite the growing interest in the topic in recent years due to the expansion of Edge and Fog Computing, numerous challenges and open research gaps were identified for the effective utilization of idle resources for cloud service execution at the edge. This paper delves into these challenges, emphasizing the need for extensive research in areas such as resource management, interoperability, incentive model establishment, and security and privacy concerns.

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Section: Contributions Of This Workmentioning

confidence: 99%

Harnessing idle edge resources for execution of cloud services: A comprehensive review

Trajano,

de Souza

2024

Trans Emerging Tel Tech

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“…The article by Walia et al ( 2023 ) provides a thorough examination of the most advanced solutions available, including both AI and non-AI approaches. It includes detailed discussions on related quality of service measures, datasets, constraints, and issues.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

Saad,

Enam,

Qureshi

2024

Front. Big Data

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As the volume and velocity of Big Data continue to grow, traditional cloud computing approaches struggle to meet the demands of real-time processing and low latency. Fog computing, with its distributed network of edge devices, emerges as a compelling solution. However, efficient task scheduling in fog computing remains a challenge due to its inherently multi-objective nature, balancing factors like execution time, response time, and resource utilization. This paper proposes a hybrid Genetic Algorithm (GA)-Particle Swarm Optimization (PSO) algorithm to optimize multi-objective task scheduling in fog computing environments. The hybrid approach combines the strengths of GA and PSO, achieving effective exploration and exploitation of the search space, leading to improved performance compared to traditional single-algorithm approaches. The proposed hybrid algorithm results improved the execution time by 85.68% when compared with GA algorithm, by 84% when compared with Hybrid PWOA and by 51.03% when compared with PSO algorithm as well as it improved the response time by 67.28% when compared with GA algorithm, by 54.24% when compared with Hybrid PWOA and by 75.40% when compared with PSO algorithm as well as it improved the completion time by 68.69% when compared with GA algorithm, by 98.91% when compared with Hybrid PWOA and by 75.90% when compared with PSO algorithm when various tasks inputs are given. The proposed hybrid algorithm results also improved the execution time by 84.87% when compared with GA algorithm, by 88.64% when compared with Hybrid PWOA and by 85.07% when compared with PSO algorithm it improved the response time by 65.92% when compared with GA algorithm, by 80.51% when compared with Hybrid PWOA and by 85.26% when compared with PSO algorithm as well as it improved the completion time by 67.60% when compared with GA algorithm, by 81.34% when compared with Hybrid PWOA and by 85.23% when compared with PSO algorithm when various fog nodes are given.

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“…The insights and methodologies described in this study offer a framework for future studies to build upon. As a result, more effective and economical cloud resource management solutions can potentially be developed [21].…”

Section: Research Direction and Conclusionmentioning

confidence: 99%

Empowering Sustainable Industrial and Service Systems through AI-Enhanced Cloud Resource Optimization

Seo,

Yoo,

Lee

2024

Sustainability

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This study focuses on examining the shift of an application system from a traditional monolithic architecture to a cloud-native microservice architecture (MSA), with a specific emphasis on the impact of this transition on resource efficiency and cost reduction. In order to evaluate whether artificial intelligence (AI) and application performance management (APM) tools can surpass traditional resource management methods in enhancing cost efficiency and operational performance, these advanced technologies are integrated. The research employs the refactor/rearchitect methodology to transition the system to a cloud-native framework, aiming to validate the enhanced capabilities of AI tools in optimizing cloud resources. The main objective of the study is to demonstrate how AI-driven strategies can facilitate more sustainable and economically efficient cloud computing environments, particularly in terms of managing and scaling resources. Moreover, the study aligns with model-based approaches that are prevalent in sustainable systems engineering by structuring cloud transformation through simulation-supported frameworks. It focuses on the synergy between endogenous AI integration within cloud management processes and the overarching goals of Industry 5.0, which emphasize sustainability and efficiency that not only benefit technological advancements but also enhance stakeholder engagement in a human-centric operational environment. This integration exemplifies how AI and cloud technology can contribute to more resilient and adaptive industrial and service systems, furthering the objectives of AI and sustainability initiatives.

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AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives

Cited by 29 publications

References 202 publications

Harnessing idle edge resources for execution of cloud services: A comprehensive review

Harnessing idle edge resources for execution of cloud services: A comprehensive review

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

Empowering Sustainable Industrial and Service Systems through AI-Enhanced Cloud Resource Optimization

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