Computation offloading and content caching and delivery in Vehicular Edge Network: A survey

Dziyauddin, Rudzidatul Akmam; Niyato, Dusit; Luong, Nguyen Cong; Atan, Ahmad Ariff Aizuddin Mohd; Izhar, Mohd Azri Mohd; Azmi, Marwan Hadri; Daud, Salwani Mohd

doi:10.1016/j.comnet.2021.108228

Cited by 33 publications

(16 citation statements)

References 74 publications

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“…The importance of VEC in the VN scenarios has been highlighted in several survey papers; to this aim [19], [20] constitute two outstanding starting points for understanding the working scenario and main challenges. Among several challenges, the partial computation offloading problem in the VECenabled VNs for the latency-critical applications is considered by several authors.…”

Section: Related Workmentioning

confidence: 99%

“…Belonging to the class of the ML approaches, FL has been recently introduced as an effective way for performing data augmentation and significantly reducing the communication overhead in comparison with direct data-sample exchanges, allowing also to enhance VUs privacy issues. In order to properly address the latency and energy constrained offloading problem defined in (20), we propose to exploit a FL framework for estimating the set A, composing the offloading portions of all VUs, based on the VU side parameters.…”

Section: B Partial Offloading Modelmentioning

confidence: 99%

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On the Design of Federated Learning in Latency and Energy Constrained Computation Offloading Operations in Vehicular Edge Computing Systems

Shinde

Bozorgchenani

Tarchi

et al. 2022

IEEE Trans. Veh. Technol.

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With the advent of smart vehicles, several new latency-critical and data-intensive applications are emerged in Vehicular Networks (VNs). Computation offloading has emerged as a viable option allowing to resort to the nearby edge servers for remote processing within a requested service latency requirement. Despite several advantages, computation offloading over resource-limited edge servers, together with vehicular mobility, is still a challenging problem to be solved. In particular, in order to avoid additional latency due to out-of-coverage operations, Vehicular Users (VUs) mobility introduces a bound on the amount of data to be offloaded towards nearby edge servers. Therefore, several approaches have been used for finding the correct amount of data to be offloaded. Among others, Federated Learning (FL) has been highlighted as one of the most promising solving techniques, given the data privacy concerns in VNs and limited communication resources. However, FL consumes resources during its operation and therefore incurs an additional burden on resource-constrained VUs. In this work, we aim to optimize the VN performance in terms of latency and energy consumption by considering both the FL and the computation offloading processes while selecting the proper number of FL iterations to be implemented. To this end, we first propose an FL-inspired distributed learning framework for computation offloading in VNs, and then develop a constrained optimization problem to jointly minimize the overall latency and the energy consumed. An evolutionary Genetic Algorithm is proposed for solving the problem in-hand and compared with some benchmarks. The simulation results show the effectiveness of the proposed approach in terms of latency and energy consumption.

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Section: Related Workmentioning

confidence: 99%

Section: B Partial Offloading Modelmentioning

confidence: 99%

On the Design of Federated Learning in Latency and Energy Constrained Computation Offloading Operations in Vehicular Edge Computing Systems

Shinde

Bozorgchenani

Tarchi

et al. 2022

IEEE Trans. Veh. Technol.

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“…Services hosted by MEC-BS or cloud server are considered for certain applications or software for executing the workload requested by user terminals, such as VR, body gaming, and real-time mapping. 1 To complete these computational-intensive requests, it is vital that MEC-BSs preallocate specific service caching for the associated computation workloads.…”

Section: Network Modelmentioning

confidence: 99%

“…Computation‐intensive mobile applications (workloads) such as real‐time maps, virtual reality/augmented reality (VR/AR), and online gaming generated by user terminals require sufficient computational resources [1]. The computation capability and battery level of user terminals are insufficient to meet the stringent deadline for completing the computation workloads.…”

Section: Introductionmentioning

confidence: 99%

Service caching decision‐making policy for mobile edge computing using deep reinforcement learning

Wang

Yang

et al. 2022

IET Communications

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Mobile user terminals in 5G networks can generate massive computational workloads, which require sufficient computation and caching resources, and the processors of user terminals cannot tackle these workloads. Emerging mobile edge computing (MEC) has become the key to solving the computation problem by offloading computation‐intensive workloads to the MEC server. To make full use of the limited resources on the MEC side, service caching can pre‐store specific executable programs, databases, or libraries for executing offloaded workloads from user terminals. In this study, a service caching‐assisted MEC model is designed. According to the proposed MEC model, a decentralized model‐free deep reinforcement learning algorithm‐based server caching optimization policy (DDSCOP) is proposed to minimize the long‐term weighted average cost. Considering the time‐varying request workloads from user terminals, the stochastic channel state, and the arrival of renewable resources of the MEC server, DDSCOP can find the near‐optimal service caching decision‐making policy by training the neural networks, that is, whether the service caching can be hosted by the MEC server, and which MEC server hosts the service caching. The numerical experimental results verify the convergence and effectiveness of DDSCOP through extensive parameters configuration, and DDSCOP outperforms the three baseline algorithms.

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“…Notice that the distance of cloud and cloudlets are farthest from a mobile device, so offloading to cloud server leads to delay in user request 4–6 . Alternative techniques or adaptive algorithms are needed to offload intensive task to small cells.…”

Section: Introductionmentioning

confidence: 99%

QoE‐aware mobile computation offloading in mobile edge computing

Sivasakthi

Raja

2022

Concurrency and Computation

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Due to the rapid development of smart phone utilization, the use of fast response with delay sensitive applications leads to high traffic demands. These issues are not yet fulfill by current researchers. When there is a massive crowd of users gathering in hotspot areas such as, railway stations, corporate centers, and malls requires service accessing social networks, then there cause uncertain traffic with congestion and network delay. In this article, an Efficient Offloading Mechanism (EOM) is proposed to transfer the user's intensive computations to picocells, where each picocell (PE) is integrated with the functionalities of edge. Moreover, a novel distributed multihop mesh middle layer (DMMM) framework is proposed for seamless communication, where the self‐healing network provides high resilience, immediate response, and multiuser content sharing and saves battery power. Generally, a picocell is a distributed antenna system that is an alternative to the repeater utilized to extend wireless services to 100 users. An Adaptive Ant Optimization (AAO) algorithm is developed for backhaul routing conflicts. Simulation outcomes of EdgeCloudSim prove that the offloading tiny devices incorporated with considerable additional computational capacities to fulfill all the demands within 15 ms delay from each network.

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Computation offloading and content caching and delivery in Vehicular Edge Network: A survey

Cited by 33 publications

References 74 publications

On the Design of Federated Learning in Latency and Energy Constrained Computation Offloading Operations in Vehicular Edge Computing Systems

On the Design of Federated Learning in Latency and Energy Constrained Computation Offloading Operations in Vehicular Edge Computing Systems

Service caching decision‐making policy for mobile edge computing using deep reinforcement learning

QoE‐aware mobile computation offloading in mobile edge computing

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