Deep reinforcement learning-based resource allocation and seamless handover in multi-access edge computing based on SDN

Li, Chunlin; Zhang, Yong; Luo, Yingwei

doi:10.1007/s10115-021-01590-4

Cited by 15 publications

(9 citation statements)

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“…The programmability aspect of SDN allows MEC to conceal the complexity of the heterogeneous edge network from the end users, thus simplifying network configuration and policy implementation. Deploying an SDNbased MEC environment enhances the ability of terminal devices' switching between network Access Points (APs) [55], [56].…”

Section: Mec Handover Controlmentioning

confidence: 99%

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Multi-Access Edge Computing Handover Strategies, Management, and Challenges: A Review

Alkaabi,

Gregory,

2024

IEEE Access

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The deployment and operation of Multi-Access Edge Computing (MEC) at the network edge provides low latency computing and storage services for end user devices. Support for device mobility is a functional requirement for MEC and a handover strategy that supports the movement of device and application state between MEC nodes is an underlying technology. The handover strategy utilises an MEC node selection technique and a handover technique to provide a smooth transition between one MEC node to another. Research into MEC handover strategies has focused on algorithms, queuing, and other considerations. The migration of MEC device and application state is a complex process that requires resource allocation in the destination MEC node that could involve provisioning application instances and in some scenarios support for containers or Virtual Machines to be received from the originating MEC node. This paper reviews MEC handover strategies and provides a description of the MEC reference architecture and proposed handover algorithms and techniques found in the literature. MEC handover challenges and gaps in the body of knowledge are discussed to provide guidance for future work.

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Section: Mec Handover Controlmentioning

confidence: 99%

“…Application service quality can degrade when the connection between UE and MEC server is affected by reduced bandwidth, increased delay or jitter, and other factors [67]. The state of the network during handover can cause network load imbalance and, in some cases, a decline in QoS [55], [62].…”

Section: A Mobility Migration and Synchronizationmentioning

confidence: 99%

Multi-Access Edge Computing Handover Strategies, Management, and Challenges: A Review

Alkaabi,

Gregory,

2024

IEEE Access

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“…where 𝑝 𝑚 represents the MEC server transmit power, 𝜎 2 represents the noise power, and the 𝐼 ′ 𝑀 represents the accumulated interference from neighboring MEC servers [28], [29]. The achieved data rate can be defined as:…”

Section: B Computation Modelmentioning

confidence: 99%

Toward Network Slicing Enabled Edge Computing: A Cloud-Native Approach for Slice Mobility

Shah¹,

Gregory²,

Li³

2023

Preprint

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<p>Network slicing is a key enabler for 5G and beyond networks that permits operators to provide scalable, flexible, and dedicated networks over a common physical infrastructure. To cope with the rising demand for Ultra-Reliable and Low-Latency Communication (URLLC) in beyond 5G networks, the provision of dedicated secure networks closer to the users is essential. Multi-access Edge Computing (MEC) is a promising technology that provides data and computational resources closer to mobile users. However, MEC servers are resource-constrained, and offering dedicated service-specific network slices at the edge in a highly dynamic and mobile environment is challenging. Network slicing and MEC are being evolved by two different standardization bodies that limit their integration and raise mobility challenges that deserve more attention. We propose a cloud-native microservices architecture for network slice mobility management in MEC that permits each MEC slice to be distributed as stateless and independently deployable microservices. The proposal separates the MEC slice operational data and the user context, as each network function in an MEC slice stores the context in a separate shared database. The proposed architecture leverages new SDN extended federation modules in compliance with the ETSI requirements for inter-MEC system coordination. The federation modules support a more flexible and scalable creation of network slices at MEC servers, efficient resource utilization, and mobility of network slices across MEC servers. The simulation results show that our proposed architecture outperforms the existing SDN-based approaches for network slicing in MEC by achieving high slice acceptance rates and reduced slice migration delay.</p>

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“…The authors 90 proposed a seamless handover scheme based on load perception to address the issue of unbalanced network load and declining service quality in MEC environments. The proposed method utilizes a seamless handover algorithm for wireless access points that calculates the access point with the highest weight and controls the switching process through a software‐defined network controller.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep reinforcement learning‐based resource allocation in multi‐access edge computing

Khani,

Sadr,

Jamali

2023

Concurrency and Computation

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SummaryNetwork architects and engineers face challenges in meeting the increasing complexity and low‐latency requirements of various services. To tackle these challenges, multi‐access edge computing (MEC) has emerged as a solution, bringing computation and storage resources closer to the network's edge. This proximity enables low‐latency data access, reduces network congestion, and improves quality of service. Effective resource allocation is crucial for leveraging MEC capabilities and overcoming limitations. However, traditional approaches lack intelligence and adaptability. This study explores the use of deep reinforcement learning (DRL) as a technique to enhance resource allocation in MEC. DRL has gained significant attention due to its ability to adapt to changing network conditions and handle complex and dynamic environments more effectively than traditional methods. The study presents the results of applying DRL for efficient and dynamic resource allocation in MEC Computing, optimizing allocation decisions based on real‐time environment and user demands. By providing an overview of the current research on resource allocation in MEC using DRL, including components, algorithms, and the performance metrics of various DRL‐based schemes, this review article demonstrates the superiority of DRL‐based resource allocation schemes over traditional methods in diverse MEC conditions. The findings highlight the potential of DRL‐based approaches in addressing challenges associated with resource allocation in MEC.

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Deep reinforcement learning-based resource allocation and seamless handover in multi-access edge computing based on SDN

Cited by 15 publications

References 50 publications

Multi-Access Edge Computing Handover Strategies, Management, and Challenges: A Review

Multi-Access Edge Computing Handover Strategies, Management, and Challenges: A Review

Toward Network Slicing Enabled Edge Computing: A Cloud-Native Approach for Slice Mobility

Deep reinforcement learning‐based resource allocation in multi‐access edge computing

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