FAST: Flexible and Low-Latency State Transfer in Mobile Edge Computing

Doan, Tung V.; Nguyen, Giang T.; Reisslein, Martin; Fitzek, Frank H. P.

doi:10.1109/access.2021.3105583

Cited by 29 publications

(5 citation statements)

References 86 publications

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“…A key challenge of state migration in MEC is to reduce the total cost of the state transfer due to resource limitations. According to Doan et al [29], minimizing the latency to significantly reduce the service disruption and meeting four crucial MEC requirements: high availability, programmability, latency, and flexibility [7], [29] are key challenges in MEC state management. Doan et al [28] proposed a live seamless service migration framework for the autonomous driving system, for testing the effect of MEC on a seamless migration for the autonomous driving system by comparing a central cloud scenario with an MEC scenario where MEC was able to handle and solve the latency issues for live service migration.…”

Section: B State Relocation In Mecmentioning

confidence: 99%

“…Doan et al [28] presented a seamless service migration framework for autonomous driving in MEC environments. Doan et al [29] proposed a programmable framework to minimize the cost of migration in MEC using a handover algorithm called Flexible and Low-Latency State Transfer (FAST), which directly forwards states between source instance and destination instance based on SDN; similar method is also discussed by Gember-Jacobson et al in [30].…”

Section: Introductionmentioning

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: B State Relocation In Mecmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Alkaabi,

Gregory,

2024

IEEE Access

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“…In this section, we have investigated the research work concerning service placement and replication strategies that are regarded as emerging research areas in the 5G cloud infrastructure. Several studies have been conducted on the service placement, migration, and replication scheme in the distributed cloud, or MEC environment [14]- [18].…”

Section: Related Workmentioning

confidence: 99%

“…The EO uses the user movement trajectory and contextual data from the path prediction model, to be detailed in Section III-B and analyze the current load status of each EN. In the context of stateful service instances, we explore the concept of Flexible and Low-Latency State Transfer, namely FAST [18], a programmable state transfer for synchronization state information across multiple instances of the application. The EO governs the direct transfers of application state from the source application instances to the destination.…”

Section: System Model and Assumptions A System Overviewmentioning

confidence: 99%

A Hyper-Heuristic Approach for Quality of Experience Aware Service Placement Scheme in 5G Mobile Edge Computing

Islam,

Ahammed,

Siddique

et al. 2024

IEEE Access

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The 5 th Generation (5G) Mobile Edge Computing (MEC) addresses the problem of high end-to-end delay experienced by traditional cloud computing users by ensuring fast accessible and reliable computing resources. However, the deployment of service instances in MEC resources requires migration due to user mobility. While Proactive Migration of service instances at multiple MECs increases users' Quality-of-Experience (QoE), Reactive Migration might reduce the deployment cost at the expense of user QoE. In this paper, we have developed a framework, that distributes service instances proactively among the Edge Nodes depending on user movement trajectories to ensure faster migration of the service instances and deliver higher QoE within minimum VNF deployment cost considering users' budgets. The aforementioned Proactive Service Placement (PSP) problem is formulated as a Multi-Objective Linear Programming (MOLP) that brings a trade-off between these two conflicting objectives, maximizing user QoE and lowering VNF deployment cost. For large networks, the PSP problem is proven to be an NP-hard problem. Thus, we have developed an artificial intelligence-based Hyper-heuristic algorithm for PSP, called HPSP, which can provide a high-performing solution within polynomial time. The HPSP exploits Tabu Search Optimization as a high-level meta-heuristic algorithm that selects one of the three lower-level metaheuristic algorithms-Golden Eagle Optimizer, Sine Cosine Optimization, and Jellyfish Search Optimization depending on the situation. The results of numerical analysis describe that the HPSP system outperforms the other state-of-the-art works in terms of user QoE, cost, and the ratio of proactive to reactive service placements.

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“…As far as services migration between different edge environments is concerned, Doan et al 8 introduced Flexible and Low-Latency State Transfer in Mobile Edge Computing model, a novel programmable state forwarding framework. The proposed model flexibly and directly forwards states between source instance and destination instance based on Software-Defined Networking.…”

Section: State Of the Artmentioning

confidence: 99%

An intelligent model for supporting edge migration for virtual function chains in next generation internet of things

Tsakanikas

Dagiuklas

Iqbal

et al. 2023

Sci Rep

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The developments on next generation IoT sensing devices, with the advances on their low power computational capabilities and high speed networking has led to the introduction of the edge computing paradigm. Within an edge cloud environment, services may generate and consume data locally, without involving cloud computing infrastructures. Aiming to tackle the low computational resources of the IoT nodes, Virtual-Function-Chain has been proposed as an intelligent distribution model for exploiting the maximum of the computational power at the edge, thus enabling the support of demanding services. An intelligent migration model with the capacity to support Virtual-Function-Chains is introduced in this work. According to this model, migration at the edge can support individual features of a Virtual-Function-Chain. First, auto-healing can be implemented with cold migrations, if a Virtual Function fails unexpectedly. Second, a Quality of Service monitoring model can trigger live migrations, aiming to avoid edge devices overload. The evaluation studies of the proposed model revealed that it has the capacity to increase the robustness of an edge-based service on low-powered IoT devices. Finally, comparison with similar frameworks, like Kubernetes, showed that the migration model can effectively react on edge network fluctuations.

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FAST: Flexible and Low-Latency State Transfer in Mobile Edge Computing

Cited by 29 publications

References 86 publications

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

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

A Hyper-Heuristic Approach for Quality of Experience Aware Service Placement Scheme in 5G Mobile Edge Computing

An intelligent model for supporting edge migration for virtual function chains in next generation internet of things

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