Online MEC Offloading for V2V Networks

Liu, Fangming; Chen, Jian; Zhang, Qixia; Li, Bo

doi:10.1109/tmc.2022.3186893

Cited by 20 publications

(14 citation statements)

References 50 publications

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“…(15) signifies that the present load of the server Y t i should be greater than the threshold load Y t th . The microservice flow rate S t ij should be greater than the threshold microservice flow rate S t th as depicted in (16). (17) demonstrates that the capacity of server J cap j should be greater than the threshold capacity of server J cap th .…”

Section: B Identification Of Resource-agnostic Propertymentioning

confidence: 98%

“…cloud platform and the access network. The edge platform provides a plethora of composite value-added microservices to distributed mobile applications [13]- [16], while providing a set of new functionalities for mission-critical applications. Expansion of MEC is mainly concentrated on performance improvement in terms of flexibility, microservice latency, and power consumption over the typical cloud computing platform.…”

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confidence: 99%

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Distributed Resource Distribution and Offloading for Resource-Agnostic Microservices in Industrial IoT

Samanta

Nguyen

2023

IEEE Trans. Veh. Technol.

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Due to increase in real-time mobile applications and Industrial Internet-of-Things (IIoT) devices, the edge computing paradigm provides a systematic and eccentric platform for real-time Internet-of-Things applications. Though the paradigm provides an effective infrastructure, however the resource requirements of IIoT devices change radically with time, which is described as a resource-agnostic property. Therefore, the estimation of resource requirements of IIoT devices is a critical and resilient assignment. In addition, it requires an extensive amount of resources to process the data traffic flows and microservice offloading. Hence, we present RAISE, a novel resource-agnostic microservice offloading scheme for mobile IIoT devices. RAISE efficiently estimates the resource-agnostic nature of IIoT devices to maximize their resource utilization in the network. Based on the estimated resource requirement, we propose a resourceagnostic microservice offloading scheme to maximize the success rate. Extensive experiments show that RAISE provides better performance in terms of network throughput and Quality-of-Service (QoS) than the other existing methods, SDTO and DTOS, in terms of cost and reliability.

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Section: B Identification Of Resource-agnostic Propertymentioning

confidence: 98%

mentioning

confidence: 99%

Distributed Resource Distribution and Offloading for Resource-Agnostic Microservices in Industrial IoT

Samanta

Nguyen

2023

IEEE Trans. Veh. Technol.

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“…Scientific works using MEC for vehicular applications are mainly focused on the theoretical and mathematical analysis of the allocation of resources to offload heavy processing tasks over a pool of computing infrastructures to boost latency and reliability [22], [23], [24], [25], [26], and save energy and costs [27]. More practical ones explore the actual limits of these architectures, studying the limits on reducing the latency by the MEC services when compared to cloud infrastructures [28], analysing the interoperability when using multi-Radio Access Technologies (RAT) to grant universal access [29], and reviewing the versatility of MEC infrastructures when using virtualization and containerization technologies to automate the management of MEC services' lifecycle [30], [31].…”

Section: Mec Architecturesmentioning

confidence: 99%

Challenges and Solutions for Service Continuity in Inter-PLMN Handover for Vehicular Applications

et al. 2023

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The reliability and availability of network connectivity, which significantly varies with mobility, is crucial in Connected, Cooperative and Automated Mobility (CCAM). Handover and roaming are the most challenging situations in terms of connectivity of cellular networks, which require switching across cells of the same cellular network or between Public Land Mobile Networks (PLMNs). This paper proposes a set of solutions for vehicular applications to mitigate the impact of mobility in service continuity, including a dual modem solution that reduces the interruption time when switching PLMNs, an adaptive bitrate mechanism for media streaming that increases reliability, a WebRTC server acting as a gateway in media streaming sessions between vehicles, and a MEC discovery and handover method. The proposed solutions have been evaluated executing an Extended Sensors application in several commercial and experimental 5G Non-Standalone (NSA) and Stand Alone (SA) setups with different Multi-access Edge Computing (MEC), edge-cloud and cloud infrastructures to host services. It can be concluded from the results obtained that 5G networks have not yet achieved the required performance for CCAM, and that practitioners need to implement solutions and workarounds, such as the ones proposed in this work, to mitigate the issues. As lessons learnt from the deployment and experimentation, this paper also overviews a detailed set of problems and the proposed solutions that CCAM industry and cellular network stakeholders need to consider.INDEX TERMS 5G, MEC, CCAM, inter-PLMN handover, vehicular communications, testing.

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“…Such parallelism provides an opportunity for further delay improvement. Thus, we consider the placement of each object classification subtask at either an edge server or one of the CAVs, to fully utilize the network-wide computing resources [22], [23]. In this manner, the CAVs not only cooperatively sense the environment but also collaborate with the edge server for computation, referred to as a cooperative sensing and computing scheme.…”

Section: Introductionmentioning

confidence: 99%

Accuracy-Aware Cooperative Sensing and Computing for Connected Autonomous Vehicles

Ye,

Qu,

Zhuang

et al. 2024

IEEE Trans. on Mobile Comput.

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To maintain high perception performance among connected and autonomous vehicles (CAVs), in this paper, we propose an accuracy-aware and resource-efficient raw-level cooperative sensing and computing scheme among CAVs and road-side infrastructure. The scheme enables fined-grained partial raw sensing data selection, transmission, fusion, and processing in per-object granularity, by exploiting the parallelism among object classification subtasks associated with each object. A supervised learning model is trained to capture the relationship between the object classification accuracy and the data quality of selected object sensing data, facilitating accuracy-aware sensing data selection. We formulate an optimization problem for joint sensing data selection, subtask placement and resource allocation among multiple object classification subtasks, to minimize the total resource cost while satisfying the delay and accuracy requirements. A genetic algorithm based iterative solution is proposed for the optimization problem. Simulation results demonstrate the accuracy awareness and resource efficiency achieved by the proposed cooperative sensing and computing scheme, in comparison with benchmark solutions.

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Online MEC Offloading for V2V Networks

Cited by 20 publications

References 50 publications

Distributed Resource Distribution and Offloading for Resource-Agnostic Microservices in Industrial IoT

Distributed Resource Distribution and Offloading for Resource-Agnostic Microservices in Industrial IoT

Challenges and Solutions for Service Continuity in Inter-PLMN Handover for Vehicular Applications

Accuracy-Aware Cooperative Sensing and Computing for Connected Autonomous Vehicles

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