Machine learning-based solutions for resource management in fog computing

Fahimullah, Muhammad; Ahvar, Shohreh; Agarwal, Mihir; Trocan, Maria

doi:10.1007/s11042-023-16399-2

Cited by 11 publications

(5 citation statements)

References 75 publications

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“…In such cases, machine learning techniques can be utilized to predict future network conditions and adjust service provisioning in advance, reducing the need for frequent optimization. Consequently, we introduce in the next a DRLbased framework to solve the problem [14], [15], [51], [52].…”

Section: 𝑇𝑡𝑟𝑎𝑛𝑠 𝑖𝑎mentioning

confidence: 99%

“…Heterogeneity stands as a fundamental principle in the fog computing layer, manifesting in various aspects such as the power source of fog computing nodes (whether linked to the power grid or battery-powered), resource capacity, mobility, and others [1], [10], [11]. Therefore, resource management is a critical concern in fog computing and has been explored and categorized in numerous studies [1], [12][13][14][15]. Resource management of fog computing nodes encompasses many aspects, including service placement, resource scheduling, task offloading, load balancing, resource allocation, resource procurement, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, in the fog computing resource management domain, there has been a burgeoning interest in adopting learning-based solutions grounded in reinforcement learning (RL). This heightened focus can be attributed to the effectiveness of reinforcement learning in addressing decision-making challenges, its adaptability to dynamic environments, and its ability to learn from interactions with the environment [14], [15], [30], [31]. Therefore, reinforcement learning circumvents the limitations associated with traditional optimization modeling, making it a favored option for fog computing resource management.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Service Provisioning in Heterogenous Fog Computing Architecture Using Deep Reinforcement Learning

Govarchinghaleh,

Sabaei

2024

Preprint

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The exponential growth of IoT devices and the surge in the data volume, coupled with the rise of latency-intensive applications, has led to a heightened interest in Fog computing to meet user demands. In this context, the service provisioning problem consists of dynamically selecting desirable fog computing nodes and routing user traffic to these nodes. Given that the fog computing layer is composed of heterogeneous nodes, which vary in resource capacity, availability, and power sources, the service provisioning problem becomes challenging. Existing solutions, often using classical optimization approaches or heuristics algorithms due to the NP-hardness of the problem, have struggled to address the issue effectively, particularly in accounting for the heterogeneity of fog nodes and uncertainty of the ad hoc fog nodes. These techniques show exponential computation times and deal only with small network scales. To overcome these issues, we are motivated to replace these approaches with Deep Reinforcement Learning (DRL) techniques, specifically employing the Proximal Policy Optimization (PPO) algorithm to understand the dynamic behavior of the environment. The main objective of the proposed DRL-based dynamic service provisioning (DDSP) algorithm is minimizing service provisioning costs while considering service delay constraints, the uncertainty of ad hoc fog nodes, and the heterogeneity of both ad hoc and dedicated fog nodes. Extensive simulations demonstrate that our approach provides a near-optimal solution with high efficiency. Notably, our proposed algorithm selects more stable fog nodes for service provisioning and successfully minimizes cost even with uncertainty regarding ad hoc fog nodes, compared to heuristic algorithms.

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Section: 𝑇𝑡𝑟𝑎𝑛𝑠 𝑖𝑎mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Service Provisioning in Heterogenous Fog Computing Architecture Using Deep Reinforcement Learning

Govarchinghaleh,

Sabaei

2024

Preprint

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“…Learning-based heuristics represented a paradigm shift in task scheduling within fog computing, harnessing the power of machine learning to create dynamic and adaptable algorithms ( Memari et al, 2022 ; Fahimullah et al, 2023 ; Ibrahim & Askar, 2023 ). Unlike traditional methods, learning-based approaches observed fog environments, collected data on resource availability and task characteristics, and dynamically adjusted scheduling strategies based on learned insights.…”

Section: Heuristic Approaches For Task Schedulingmentioning

confidence: 99%

“… Fahimullah et al (2023) conducted a thorough analysis of existing literature concerning machine learning-based approaches to tackle resource management challenges within fog computing. These challenges encompassed diverse aspects such as resource provisioning, application placement, scheduling, allocation, task offloading, and load balancing.…”

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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A Load-Balanced Task Scheduling in Fog-Cloud Architecture: A Machine Learning Approach

Keshri,

Vidyarthi

2024

Communications in Computer and Information Science

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Machine learning-based solutions for resource management in fog computing

Cited by 11 publications

References 75 publications

Dynamic Service Provisioning in Heterogenous Fog Computing Architecture Using Deep Reinforcement Learning

Dynamic Service Provisioning in Heterogenous Fog Computing Architecture Using Deep Reinforcement Learning

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

A Load-Balanced Task Scheduling in Fog-Cloud Architecture: A Machine Learning Approach

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