A predictive energy-aware scheduling strategy for scientific workflows in fog computing

Nazeri, Mohammadreza; Soltanaghaei, Mohammadreza; Khorsand, Reihaneh

doi:10.1016/j.eswa.2024.123192

Cited by 8 publications

(4 citation statements)

References 39 publications

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“… Nazeri, Soltanaghaei & Khorsand (2024) proposed a predictive energy-aware scheduling framework for fog computing, integrating a MAPE-K control model comprising monitor, analyzer, planner, and executer components with a shared knowledge base. It introduced an Adaptive Network-based Fuzzy Inference System (ANFIS) in the Analyzer component to predict future resource load and a resource management strategy based on the predicted load to reduce energy consumption.…”

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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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: 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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“…Nazeri et al 30 introduced a predictive energy-aware scheduling framework structured around a MAPE-K control model, which comprises the Monitor, Analyzer, Planner, and Executer components with a shared Knowledge base in fog computing. Initially, they introduced a prediction method utilizing an Adaptive Network-based Fuzzy Inference System (ANFIS) within the Analyzer component to forecast future resource load.…”

Section: Related Work Fig:streammentioning

confidence: 99%

“…The need for scientific workflows in cloud-edge computing arises from the ever-increasing complexity and scale of computational tasks in contemporary research 30 . These workflows provide a structured approach to problem-solving, allowing researchers to systematically analyze data, run simulations, and derive meaningful insights.…”

Section: Scientific Workflowmentioning

confidence: 99%

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Enhancing Workflow Efficiency: A Modified Firefly Algorithm for Hybrid Cloud-Edge Environments

Alsadie,

Alsulami

2024

Preprint

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Efficient scheduling of scientific workflows in cloud computing environments is essential for optimizing resource utilization and minimizing completion time. In this study, we comprehensively evaluate different scheduling algorithms, focusing on the Modified Firefly Optimization Algorithm (ModFOA) in comparison with existing methods like Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Our investigation considers key performance metrics such as makespan, resource utilization, and energy consumption across diverse configurations and scenarios. Scientific workflows often involve intricate tasks with dependencies, posing challenges for efficient scheduling. While existing algorithms have shown promise, they may not fully address the unique requirements of cloud environments, leading to suboptimal outcomes. Therefore, we propose evaluating ModFOA’s effectiveness in scheduling scientific workflows in the cloud. Through comparative analysis, ModFOA demonstrates superior performance in terms of makespan, achieving lower completion times across various configurations. Additionally, ModFOA exhibits competitive resource utilization and moderate energy consumption, positioning it as a promising solution for optimizing workflow scheduling in cloud environments. This study underscores the significance of selecting efficient scheduling algorithms and highlights ModFOA’s potential in improving workflow scheduling and resource management in cloud environments. Further research could focus on refining ModFOA parameters and validating its practicality in real-world cloud computing scenarios.

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A distributed load balancing method for IoT/Fog/Cloud environments with volatile resource support

Shamsa,

Rezaee,

Adabi

et al. 2024

Cluster Comput

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A predictive energy-aware scheduling strategy for scientific workflows in fog computing

Cited by 8 publications

References 39 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

Enhancing Workflow Efficiency: A Modified Firefly Algorithm for Hybrid Cloud-Edge Environments

A distributed load balancing method for IoT/Fog/Cloud environments with volatile resource support

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