Seamless Service Migration Framework for Autonomous Driving in Mobile Edge Cloud

Doan, Tung V.; Fan, Zhongyi; Nguyen, Giang T.; You, Dongho; Kropp, Alexander; Salah, Hani; Fitzek, Frank H. P.

doi:10.1109/ccnc46108.2020.9045586

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…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%

“…An optimal handover strategy was proposed in [8] that considers the Vehicle-to-Infrastructure (V2I) connection Round-Trip Time (RTT) with a vehicle running a selected application. 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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“…This setting is also used by Mouradian et al [83] and Yang et al [84]. The link bandwidth is randomly distributed in the set of [1,10] Gbps [83], [85]. For the objective function in Eq.…”

Section: B Simulation Setupmentioning

confidence: 99%

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

et al. 2021

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Mobile Edge Computing (MEC) brings the benefits of cloud computing, such as computation, networking, and storage resources, close to end users, thus reducing end-to-end latency and enabling various novel use cases, such as vehicle platooning, autonomous driving, and the tactile internet. However, frequent user mobility makes it challenging for the MEC to guarantee the close proximity to the users. To tackle this challenge, the underlying network has to be capable of seamlessly migrating applications between multiple MEC sites. This application migration requires the quick and flexible migration of the application states without service interruption, while minimizing the state transfer cost. In this article, we first study the state transfer optimization problem in the MEC. To solve this problem, we propose a metaheuristic algorithm based on Tabu search. We then propose Flexible and Low-Latency State Transfer in Mobile Edge Computing (FAST), the first programmable state forwarding framework. FAST flexibly and directly forwards states between source instance and destination instance based on Software-Defined Networking (SDN). Both simulation results and practical testbed results demonstrate the favorable performance of the proposed Tabu search algorithm and the FAST framework compared to the state-of-the-art schemes.INDEX TERMS Application state transfer, multi-access edge computing (MEC), network function virtualization (NFV), software-defined networking (SDN).

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“…Typically, deployments in softwarized networks include a combination of technologies to fulfill the requirements of real-time use cases: Software-Defined Networking (SDN) [20], Network Function Virtualization (NFV) [21], and Service Function Chaining (SFC) [22]. As the network becomes softwarized, Computing in the Network (COIN) and the Mobile Edge Cloud (MEC) [23] become powerful concepts to combine mobile, local, and far computing resources in a flexible fashion per use-case. Computing in the network will significantly reduce latency and issues that stem from extended packet switching across multiple networks, such as congestion.…”

mentioning

confidence: 99%

You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks

2021

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With increasing numbers of computer vision and object detection application scenarios, those requiring ultra-low service latency times have become increasingly prominent; e.g., those for autonomous and connected vehicles or smart city applications. The incorporation of machine learning through the applications of trained models in these scenarios can pose a computational challenge. The softwarization of networks provides opportunities to incorporate computing into the network, increasing flexibility by distributing workloads through offloading from client and edge nodes over in-network nodes to servers. In this article, we present an example for splitting the inference component of the YOLOv2 trained machine learning model between client, network, and service side processing to reduce the overall service latency. Assuming a client has 20% of the server computational resources, we observe a more than 12-fold reduction of service latency when incorporating our service split compared to on-client processing and and an increase in speed of more than 25% compared to performing everything on the server. Our approach is not only applicable to object detection, but can also be applied in a broad variety of machine learning-based applications and services.

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Seamless Service Migration Framework for Autonomous Driving in Mobile Edge Cloud

Cited by 9 publications

References 4 publications

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

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

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

You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks

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