Reinforcement Learning-Based Optimization Framework for Application Component Migration in NFV Cloud-Fog Environments

Afrasiabi, Seyedeh Negar; Ebrahimzadeh, Amin; Mouradian, Carla; Malektaji, Sepideh; Glitho, Roch

doi:10.1109/tnsm.2022.3217723

Cited by 8 publications

(2 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research underscores considerations in cloud offloading decisions for smart grids. Finally, Seyedeh et al [95] address problems related to application migration and service discontinuity to reduce application delay in hybrid cloudfog systems. Additionally, factors such as application types, cloudlet selection strategies, migration overhead, and dynamic performance monitoring contribute to the intelligent optimization of smart-grid operations, ensuring efficient resource utilization and overall system efficiency improvements.…”

Section: Application-specific Factorsmentioning

confidence: 99%

Edge Offloading in Smart Grid

Arcas,

Cioara,

Anghel

et al. 2024

Smart Cities

View full text Add to dashboard Cite

The management of decentralized energy resources and smart grids needs novel data-driven low-latency applications and services to improve resilience and responsiveness and ensure closer to real-time control. However, the large-scale integration of Internet of Things (IoT) devices has led to the generation of significant amounts of data at the edge of the grid, posing challenges for the traditional cloud-based smart-grid architectures to meet the stringent latency and response time requirements of emerging applications. In this paper, we delve into the energy grid and computational distribution architectures, including edge–fog–cloud models, computational orchestration, and smart-grid frameworks to support the design and offloading of grid applications across the computational continuum. Key factors influencing the offloading process, such as network performance, data and Artificial Intelligence (AI) processes, computational requirements, application-specific factors, and energy efficiency, are analyzed considering the smart-grid operational requirements. We conduct a comprehensive overview of the current research landscape to support decision-making regarding offloading strategies from cloud to fog or edge. The focus is on metaheuristics for identifying near-optimal solutions and reinforcement learning for adaptively optimizing the process. A macro perspective on determining when and what to offload in the smart grid is provided for the next-generation AI applications, offering an overview of the features and trade-offs for selecting between federated learning and edge AI solutions. Finally, the work contributes to a comprehensive understanding of edge offloading in smart grids, providing a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to support cost–benefit analysis in decision-making regarding offloading strategies.

show abstract

Section: Application-specific Factorsmentioning

confidence: 99%

Edge Offloading in Smart Grid

Arcas,

Cioara,

Anghel

et al. 2024

Smart Cities

View full text Add to dashboard Cite

show abstract

“…In [16], authors propose a decentralized task offloading method based on Transformer and Policy Decouplingbased Multi-Agent Actor-Critic (TPDMAAC), highlighting the flexibility of this algorithm, as it can be adapted to other scenarios by the fine-tuning of its parameters even with an uncertain load in the edge server. In [17], authors present a new component migration strategy in an NFV-based hybrid cloud/fog system considering the mobility of both end users and fog nodes. In particular, they propose a DRL approach, based on a Double Deep-Q Network (DDQN), to decide where and when to migrate application components, achieving better results in both delay and power consumption compared to other state-of-the-art application migration strategies.…”

Section: Related Workmentioning

confidence: 99%

DRL-FORCH: A Scalable Deep Reinforcement Learning-based Fog Computing Orchestrator

Cicco

Pittalà

Davoli

et al. 2023

2023 IEEE 9th International Conference on Network Softwarization (NetSoft)

View full text Add to dashboard Cite

We consider the problem of designing and training a neural network-based orchestrator for fog computing service deployment. Our goal is to train an orchestrator able to optimize diversified and competing QoS requirements, such as blocking probability and service delay, while potentially supporting thousands of fog nodes. To cope with said challenges, we implement our neural orchestrator as a Deep Set (DS) network operating on sets of fog nodes, and we leverage Deep Reinforcement Learning (DRL) with invalid action masking to find an optimal trade-off between competing objectives. Illustrative numerical results show that our Deep Set-based policy generalizes well to problem sizes (i.e., in terms of numbers of fog nodes) up to two orders of magnitude larger than the ones seen during the training phase, outperforming both greedy heuristics and traditional Multi-Layer Perceptron (MLP)-based DRL. In addition, inference times of our DS-based policy are up to an order of magnitude faster than an MLP, allowing for excellent scalability and near real-time online decision-making.

show abstract

A Geographic Routing Protocol for Flying Ad-Hoc Network Based on Gauss-Markov Mobility Prediction

Liu,

Zhang,

et al. 2024

2024 4th International Symposium on Computer Technology and Information Science (ISCTIS)

View full text Add to dashboard Cite

Reinforcement Learning-Based Optimization Framework for Application Component Migration in NFV Cloud-Fog Environments

Cited by 8 publications

References 65 publications

Edge Offloading in Smart Grid

Edge Offloading in Smart Grid

DRL-FORCH: A Scalable Deep Reinforcement Learning-based Fog Computing Orchestrator

A Geographic Routing Protocol for Flying Ad-Hoc Network Based on Gauss-Markov Mobility Prediction

Contact Info

Product

Resources

About