مقاله انگلیسی رایگان در مورد تخلیه رایانش لبه-ابر در شبکه خودرویی برای الگوریتم ژنتیک انرژی SLA – الزویر ۲۰۲۲

Abstract

     Vehicular Ad Hoc Networks (VANET) is an emerging technology that enables a comfortable, safe, and efficient travel experience by providing mechanisms to execute applications related to traffic congestions, road accidents, autonomous driving, and entertainment. The mobile vehicles in VANET are characterized by low computational and storage capabilities. In such scenarios, to meet applications’ performance requirements, requests from vehicles are offloaded to edge and cloud servers. The high energy consumption of these servers increases operating costs and threatens the environment. Energy-aware offloading strategies have been introduced to tackle this problem. Existing works on computation offloading focus on optimizing the energy consumption of either the IoT devices/mobile/vehicles and/or the edge servers. This paper proposes a novel offloading algorithm that optimizes the energy of edge–cloud integrated computing platforms based on Evolutionary Genetic Algorithm (EGA) while maintaining applications’ Service Level Agreement (SLA). The proposed algorithm employs an adaptive penalty function to incorporate the optimization constraints within EGA. Comparative analysis and numerical experiments are carried out between the proposed algorithm, random and genetic algorithm-based offloading, and no offloading baseline approaches.

Introduction

     Vehicular Ad Hoc Networks (VANET) [1] is an emerging technology where vehicles acting as network nodes are equipped with computational resources and connectivity such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-roadside (V2R), vehicle-to-sensors (V2S), vehicle-to-pedestrian (V2P), and vehicle-to-everything (V2X) communications. It enables a comfortable, safer, convenient, and efficient travel experience for users by using applications such as sending alerts for congestions and accidents, autonomous driving, video-enabled real-time navigation, interactive gaming, and entertainment [2], [3], [4]. These applications often require high computation and storage resources, and low latency to process complex operations. However, mobile vehicles have limited onboard computing and storage capabilities to process resource-intensive applications while maintaining the Quality of Services (QoS). To address this issue, a cloud-based vehicular network has been introduced. Cloud computing [5], [6] provides on-demand computational and storage resources to mobile vehicles over the Internet. The remote cloud servers have high computation capabilities that would satisfy applications processing times. However, a high latency between the vehicle and cloud resources hinders the deployment of time-critical applications such as autonomous driving. In addition, a delayed response for applications such as traffic congestion and interactive gaming becomes less reliable. To overcome vehicles-to-cloud latency issues, Vehicular Edge Computing (VEC) [7] has been introduced.

Conclusions and future work

     Computation offloading is important in edge–cloud integrated vehicular networks to execute computationally intensive applications having strict SLA requirements. However, the energy consumption of the edge–cloud integrated computing platform should be considered energy-efficiency is crucial. In this paper, an Energy-SLA-Aware evolutionary genetic algorithm is proposed for edge–cloud computation offloading in a vehicular network that executes a vehicle’s request either on the edge server to which the request is submitted or offloads the request to one of the cloud servers. The offloading decision is made in a way that the total energy consumption of a set of requests is minimized and the SLA requirements of each request are maintained in terms of latency and processing time. The SLA constraints in the proposed algorithm are handled using the adaptive penalty function. This is the first work to propose an energy-SLA-aware offloading in the vehicular network using EGA that optimizes the energy consumption of the edge and cloud servers simultaneously, while adhering to the latency and processing time constraints. Comparative analysis and numerical experiments carried out revealed that the proposed algorithm outperforms no offloading and random offloading approaches in terms of energy consumption, and no offloading, random and energy-non-SLA-aware genetic-based baseline approaches in terms of percentage of SLA violations.