Multi-Component V2X Applications Placement in Edge Computing Environment

Shaer, Ibrahim; Haque, Anwar; Shami, Abdallah

doi:10.1109/icc40277.2020.9148960

Cited by 24 publications

(13 citation statements)

References 11 publications

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“…This work does not study the changing traffic patterns and time-varying nature of vehicular environment while making service placement decision. In [18], the authors consider a highway environment for V2X service placement. They propose a binary ILP model for minimization of communication and download link delay for five different V2X applications.…”

Section: Related Workmentioning

confidence: 99%

DRLD-SP: A Deep Reinforcement Learning-based Dynamic Service Placement in Edge-Enabled Internet of Vehicles

Talpur,

Gurusamy

2021

Preprint

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The growth of 5G and edge computing has enabled the emergence of Internet of Vehicles. It supports different types of services with different resource and service requirements. However, limited resources at the edge, high mobility of vehicles, increasing demand, and dynamicity in service request-types have made service placement a challenging task. A typical static placement solution is not effective as it does not consider the traffic mobility and service dynamics. Handling dynamics in IoV for service placement is an important and challenging problem which is the primary focus of our work in this paper. We propose a Deep Reinforcement Learning-based Dynamic Service Placement (DRLD-SP) framework with the objective of minimizing the maximum edge resource usage and service delay while considering the vehicle's mobility, varying demand, and dynamics in the requests for different types of services. We use SUMO and MATLAB to carry out simulation experiments. The experimental results show that the proposed DRLD-SP approach is effective and outperforms other static and dynamic placement approaches.

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Section: Related Workmentioning

confidence: 99%

DRLD-SP: A Deep Reinforcement Learning-based Dynamic Service Placement in Edge-Enabled Internet of Vehicles

Talpur,

Gurusamy

2021

Preprint

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“…The delay experienced by the vehicles for V2X communication is also based on randomly assigned values from a given range. In [7], the authors consider a more realistic scenario of the highway environment for V2X service placement. They consider 5 different V2X applications for minimization of communication and download link delay using binary ILP model.…”

Section: Related Workmentioning

confidence: 99%

“…Constraint (6) guarantees the edge node has the capacity to handle the number of UEs requesting service . Finally, condition (7) defines the decision variable as a binary integer decision variable.…”

Section: Proposed Rl-based Dynamic (Rl-dynamic) Service Placement Fra...mentioning

confidence: 99%

Reinforcement Learning-based Dynamic Service Placement in Vehicular Networks

Talpur,

Gurusamy

2021

Preprint

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“…An ITS is the culmination of various interacting networking technologies and architectures, including Network Function Virtualization (NFV), MEC, 5G+ networks, the Internet of Vehicles (IoV) as well as ML and AI. Through distributed points of presence known as Roadside Units (RSUs), an ITS is able to collect and process data at the edge of the network and provide functionalities to vehicular clients [5]. The basic setup of an ITS is presented in Fig.…”

Section: Introductionmentioning

confidence: 99%

Concept Drift Detection in Federated Networked Systems

Manias,

Shaer,

Yang

et al. 2021

Preprint

Self Cite

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As next-generation networks materialize, increasing levels of intelligence are required. Federated Learning has been identified as a key enabling technology of intelligent and distributed networks; however, it is prone to concept drift as with any machine learning application. Concept drift directly affects the model's performance and can result in severe consequences considering the critical and emergency services provided by modern networks. To mitigate the adverse effects of drift, this paper proposes a concept drift detection system leveraging the federated learning updates provided at each iteration of the federated training process. Using dimensionality reduction and clustering techniques, a framework that isolates the system's drifted nodes is presented through experiments using an Intelligent Transportation System as a use case. The presented work demonstrates that the proposed framework is able to detect drifted nodes in a variety of non-iid scenarios at different stages of drift and different levels of system exposure.

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Multi-Component V2X Applications Placement in Edge Computing Environment

Cited by 24 publications

References 11 publications

DRLD-SP: A Deep Reinforcement Learning-based Dynamic Service Placement in Edge-Enabled Internet of Vehicles

DRLD-SP: A Deep Reinforcement Learning-based Dynamic Service Placement in Edge-Enabled Internet of Vehicles

Reinforcement Learning-based Dynamic Service Placement in Vehicular Networks

Concept Drift Detection in Federated Networked Systems

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