Multiprocessor task scheduling using multi-objective hybrid genetic Algorithm in Fog–cloud computing

Gupta, Sachi; Ahuja, Rakesh; Agarwal, Gaurav; Kumar, Atul

doi:10.1016/j.knosys.2023.110563

Cited by 18 publications

(3 citation statements)

References 30 publications

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“…Deep Q-Networks (DQN) extend Q-learning by employing deep neural networks to approximate the Q-function, enabling more complex and scalable task scheduling policies. Hybrid heuristics: Hybrid heuristics integrate multiple heuristic approaches to exploit their complementary strengths and improve solution quality ( Agarwal et al, 2023 ; Yadav, Tripathi & Sharma, 2022a ). This includes combinations of greedy and metaheuristic approaches, as well as the fusion of learning-based and metaheuristic approaches ( Leena, Divya & Lilian, 2020 ).…”

Section: Taxonomy Of Heuristic Approaches For Task Scheduling In Fog ...mentioning

confidence: 99%

“… Agarwal et al (2023) introduced a novel methodology termed Hybrid Genetic Algorithm and Energy Conscious Scheduling (Hgecs) to tackle the challenges of multiprocessor task scheduling in fog-cloud computing systems. It integrated a genetic algorithm and energy-conscious scheduling to optimize task scheduling in environments with increasing numbers of clients and services, which posed issues related to scheduling and energy consumption.…”

Section: Heuristic Approaches For Task Schedulingmentioning

confidence: 99%

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Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Alsadie

2024

PeerJ Computer Science

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Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring the timely execution of tasks within fog computing environments. This article presents a comprehensive review of the advancements in task scheduling methodologies for fog computing systems, covering priority-based, greedy heuristics, metaheuristics, learning-based, hybrid heuristics, and nature-inspired heuristic approaches. Through a systematic analysis of relevant literature, we highlight the strengths and limitations of each approach and identify key challenges facing fog computing task scheduling, including dynamic environments, heterogeneity, scalability, resource constraints, security concerns, and algorithm transparency. Furthermore, we propose future research directions to address these challenges, including the integration of machine learning techniques for real-time adaptation, leveraging federated learning for collaborative scheduling, developing resource-aware and energy-efficient algorithms, incorporating security-aware techniques, and advancing explainable AI methodologies. By addressing these challenges and pursuing these research directions, we aim to facilitate the development of more robust, adaptable, and efficient task-scheduling solutions for fog computing environments, ultimately fostering trust, security, and sustainability in fog computing systems and facilitating their widespread adoption across diverse applications and domains.

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Section: Taxonomy Of Heuristic Approaches For Task Scheduling In Fog ...mentioning

confidence: 99%

Section: Heuristic Approaches For Task Schedulingmentioning

confidence: 99%

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Alsadie

2024

PeerJ Computer Science

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“…For the purpose of allocation, a static method of task scheduling is used rather than a dynamic one. A cloud-fog infrastructure-based job scheduling approach that was developed by Agarwal et al (2023) and was based on the genetic algorithm (GA) mechanism was also presented. Comparing the proposed method to the Bee Life algorithm revealed that the proposed method is superior.…”

Section: Literature Surveymentioning

confidence: 99%

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Sheikh,

Enam,

Qureshi

2023

Front. Comput. Sci.

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Fog Computing has emerged as a pivotal technology for enabling low-latency, context-aware, and efficient computing at the edge of the network. Effective task scheduling plays a vital role in optimizing the performance of fog computing systems. Traditional task scheduling algorithms, primarily designed for centralized cloud environments, often fail to cater to the dynamic, heterogeneous, and resource-constrained nature of Fog nodes. To overcome these limitations, we introduce a sophisticated machine learning-driven methodology that adapts task allocation to the ever-changing Fog environment's conditions. Our approach amalgamates K-Means clustering algorithm enhanced with fuzzy logic, a robust unsupervised learning technique, to efficiently group Fog nodes based on their resource characteristics and workload patterns. The proposed method combines the clustering capabilities of K-means with the adaptability of fuzzy logic to dynamically allocate tasks to fog nodes. By leveraging machine learning techniques, we demonstrate how tasks can be intelligently allocated to fog nodes, resulting in reducing execution time, response time and network usage. Through extensive experiments, we showcase the effectiveness and adaptability of our proposed approach in dynamic fog environments. Clustering proves to be a time-effective method for identifying groups of jobs per virtual machine (VM) efficiently. To model and evaluate our proposed approach, we have utilized iFogSim. The simulation results affirm the effectiveness of our scheduling technique, showcasing significant enhancements in execution time reduction, minimized network utilization, and improved response time when compared to existing machine learning and non-machine learning based scheduling methods within the iFogSim framework.

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Workflow Scheduling in the Cloud-Edge Continuum

Zanussi,

Tessera,

Massari

et al. 2024

Lecture Notes on Data Engineering and Communications Technologies

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Multiprocessor task scheduling using multi-objective hybrid genetic Algorithm in Fog–cloud computing

Cited by 18 publications

References 30 publications

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Workflow Scheduling in the Cloud-Edge Continuum

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