AI-Based Sustainable and Intelligent Offloading Framework for IIoT in Collaborative Cloud-Fog Environments

Kumar, Mohit; Walia, Guneet Kaur; Shingare, Haresh; Singh, Samayveer; Gill, Sukhpal Singh

doi:10.1109/tce.2023.3320673

Cited by 17 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where 𝜓 𝒟𝒮_,J-K signifies computational capacity of cloud datacenter node. Noteworthy, the offloading latency model functions in accordance with offloading decision variable Θ 𝒹,𝒿 is defined as follows [125]:…”

Section: 𝒹𝒿mentioning

confidence: 99%

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

Walia,

Kumar,

Gill

2024

IEEE Commun. Surv. Tutorials

Self Cite

View full text Add to dashboard Cite

The proliferation of ubiquitous Internet of Things (IoT) sensors and smart devices in several domains embracing healthcare, Industry 4.0, transportation and agriculture are giving rise to a prodigious amount of data requiring everincreasing computations and services from cloud to the edge of the network. Fog/Edge computing is a promising and distributed computing paradigm that has drawn extensive attention from both industry and academia. The infrastructural efficiency of these computing paradigms necessitates adaptive resource management mechanisms for offloading decisions and efficient scheduling. Resource Management (RM) is a non-trivial issue whose complexity is the result of heterogeneous resources, incoming transactional workload, edge node discovery, and Quality of Service (QoS) parameters at the same time, which makes the efficacy of resources even more challenging. Hence, the researchers have adopted Artificial Intelligence (AI)-based techniques to resolve the abovementioned issues. This paper offers a comprehensive review of resource management issues and challenges in Fog/Edge paradigm by categorizing them into provisioning of computing resources, task offloading, resource scheduling, service placement, and load balancing. In addition, existing AI and non-AI based state-of-the-art solutions have been discussed, along with their QoS metrics, datasets analysed, limitations and challenges. The survey provides mathematical formulation corresponding to each categorized resource management issue. Our work sheds light on promising research directions on cutting-edge technologies such as Serverless computing, 5G, Industrial IoT (IIoT), blockchain, digital twins, quantum computing, and Software-Defined Networking (SDN), which can be integrated with the existing frameworks of fog/edge-of-things paradigms to improve business intelligence and analytics amongst IoT-based applications.

show abstract

Section: 𝒹𝒿mentioning

confidence: 99%

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

Walia,

Kumar,

Gill

2024

IEEE Commun. Surv. Tutorials

Self Cite

View full text Add to dashboard Cite

show abstract

“…Kumar et al ( 2023a ) discuss the shortcomings of the cloud model in fulfilling the latency requirements of Industrial IoT (IIoT) applications. This article presents a new AI-based framework designed to optimize a multi-layered integrated cloud-fog environment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In summary, the article provides a thorough examination, analysis, and plan for tackling obstacles in managing Fog/Edge resources for IoT applications. Kumar et al (2023a) discuss the shortcomings of the cloud model in fulfilling the latency requirements of Industrial IoT (IIoT) applications. This article presents a new AI-based framework designed to optimize a multi-layered integrated cloudfog environment.…”

Section: Hybrid Algorithmsmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Resource Scheduling in Integrated IoT and Fog Computing Environments: A Taxonomy, Survey and Future Directions

Alshammari,

Gill,

Pervaiz

et al. 2024

Studies in Big Data

View full text Add to dashboard Cite

AI-Based Sustainable and Intelligent Offloading Framework for IIoT in Collaborative Cloud-Fog Environments

Cited by 17 publications

References 18 publications

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

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

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

Resource Scheduling in Integrated IoT and Fog Computing Environments: A Taxonomy, Survey and Future Directions

Contact Info

Product

Resources

About