{"title":"闲置移动车辆辅助车载边缘计算中的激励驱动计算卸载和资源定价策略","authors":"Shanchen Pang, Baoyun Chen, Xiao He, Nuanlai Wang, Zhi Lu, Shengzhe Zhao, Zixuan Fan, Yanxiang Zhang","doi":"10.1016/j.simpat.2024.103035","DOIUrl":null,"url":null,"abstract":"<div><div>The emergence of vehicular edge computing (VEC) has introduced a new computational paradigm for high-quality processing of computing services in Internet of Vehicles (IoV) scenarios. However, due to the limited computational resources of the VEC server, it is not sufficient to adequately meet the demand for highly concurrent computational services in high-density vehicular communication networks. To address this issue, we consider an idle mobile vehicles assisted vehicular edge computing framework and propose a hybrid Stackelberg-Match cooperative task offloading and resource pricing algorithm (SMOP). The algorithm considers the mobility of vehicles and the duration of channels, coordinating the computational resources of fixed VEC servers and idle mobile vehicles within the vehicular network (VN). This enhances offloading efficiency and maximizes participant benefits. Specifically, the Stackelberg game is used to derive differentiated pricing schemes for idle mobile vehicles and VEC servers for different vehicular tasks, and the stable matching method is employed to determine task offloading strategies. Finally, we conduct experiments on a real Chengdu traffic dataset. The results demonstrate that the proposed solution effectively reduces offloading costs and exhibits strong robustness in handling latency-sensitive and data-intensive service requests.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"138 ","pages":"Article 103035"},"PeriodicalIF":3.5000,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Incentive-driven computation offloading and resource pricing strategy in vehicular edge computing assisted with idle mobile vehicles\",\"authors\":\"Shanchen Pang, Baoyun Chen, Xiao He, Nuanlai Wang, Zhi Lu, Shengzhe Zhao, Zixuan Fan, Yanxiang Zhang\",\"doi\":\"10.1016/j.simpat.2024.103035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The emergence of vehicular edge computing (VEC) has introduced a new computational paradigm for high-quality processing of computing services in Internet of Vehicles (IoV) scenarios. However, due to the limited computational resources of the VEC server, it is not sufficient to adequately meet the demand for highly concurrent computational services in high-density vehicular communication networks. To address this issue, we consider an idle mobile vehicles assisted vehicular edge computing framework and propose a hybrid Stackelberg-Match cooperative task offloading and resource pricing algorithm (SMOP). The algorithm considers the mobility of vehicles and the duration of channels, coordinating the computational resources of fixed VEC servers and idle mobile vehicles within the vehicular network (VN). This enhances offloading efficiency and maximizes participant benefits. Specifically, the Stackelberg game is used to derive differentiated pricing schemes for idle mobile vehicles and VEC servers for different vehicular tasks, and the stable matching method is employed to determine task offloading strategies. Finally, we conduct experiments on a real Chengdu traffic dataset. The results demonstrate that the proposed solution effectively reduces offloading costs and exhibits strong robustness in handling latency-sensitive and data-intensive service requests.</div></div>\",\"PeriodicalId\":49518,\"journal\":{\"name\":\"Simulation Modelling Practice and Theory\",\"volume\":\"138 \",\"pages\":\"Article 103035\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Simulation Modelling Practice and Theory\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1569190X24001497\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Simulation Modelling Practice and Theory","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1569190X24001497","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Incentive-driven computation offloading and resource pricing strategy in vehicular edge computing assisted with idle mobile vehicles
The emergence of vehicular edge computing (VEC) has introduced a new computational paradigm for high-quality processing of computing services in Internet of Vehicles (IoV) scenarios. However, due to the limited computational resources of the VEC server, it is not sufficient to adequately meet the demand for highly concurrent computational services in high-density vehicular communication networks. To address this issue, we consider an idle mobile vehicles assisted vehicular edge computing framework and propose a hybrid Stackelberg-Match cooperative task offloading and resource pricing algorithm (SMOP). The algorithm considers the mobility of vehicles and the duration of channels, coordinating the computational resources of fixed VEC servers and idle mobile vehicles within the vehicular network (VN). This enhances offloading efficiency and maximizes participant benefits. Specifically, the Stackelberg game is used to derive differentiated pricing schemes for idle mobile vehicles and VEC servers for different vehicular tasks, and the stable matching method is employed to determine task offloading strategies. Finally, we conduct experiments on a real Chengdu traffic dataset. The results demonstrate that the proposed solution effectively reduces offloading costs and exhibits strong robustness in handling latency-sensitive and data-intensive service requests.
期刊介绍:
The journal Simulation Modelling Practice and Theory provides a forum for original, high-quality papers dealing with any aspect of systems simulation and modelling.
The journal aims at being a reference and a powerful tool to all those professionally active and/or interested in the methods and applications of simulation. Submitted papers will be peer reviewed and must significantly contribute to modelling and simulation in general or use modelling and simulation in application areas.
Paper submission is solicited on:
• theoretical aspects of modelling and simulation including formal modelling, model-checking, random number generators, sensitivity analysis, variance reduction techniques, experimental design, meta-modelling, methods and algorithms for validation and verification, selection and comparison procedures etc.;
• methodology and application of modelling and simulation in any area, including computer systems, networks, real-time and embedded systems, mobile and intelligent agents, manufacturing and transportation systems, management, engineering, biomedical engineering, economics, ecology and environment, education, transaction handling, etc.;
• simulation languages and environments including those, specific to distributed computing, grid computing, high performance computers or computer networks, etc.;
• distributed and real-time simulation, simulation interoperability;
• tools for high performance computing simulation, including dedicated architectures and parallel computing.