{"title":"使用 JAYA-GA 元搜索算法的混合模型,用于在雾集成云中放置雾节点","authors":"Satveer Singh, Deo Prakash Vidyarthi","doi":"10.1007/s12652-024-04796-w","DOIUrl":null,"url":null,"abstract":"<p>It has been observed that Cloud services exhibit suboptimal performance for real-time requests due to increased network delay. Fog computing has emerged to address this issue by deploying Fog nodes at the network's edge. However, determining the optimal placement of Fog nodes for efficient service processing poses a significant challenge. The multiple ways to deploy a Fog node makes the Fog node placement an NP-class problem. It leverages the potential benefit of metaheuristic to solve this problem. In this work, we formulate a linear mathematical model for fog node placement (FNP), considering two important objectives for minimization, i.e., deployment cost (DC) and network latency (NL). A hybrid metaheuristic approach using genetic algorithm (GA) and JAYA, called JAYA-GA, is proposed to address this multi-objective optimization. The proposed model is simulated and the experimental results are compared against three popularly used metaheuristics: particle swarm optimization (PSO), GA, and JAYA. The proposed model consistently outperforms JAYA, PSO, and GA by the averages of 18.40%, 33.58%, and 30.75%, respectively, in terms of fitness (a weighted sum of DC and NL). Additionally, it exhibits superior performance on average convergence rate (16.60%, 53.42%, and 86.59%) and computation time (15.76%, 34.74%, and 59.22%) compared to JAYA, PSO, and GA respectively. Thus, the simulation results establish that the hybrid JAYA-GA technique surpasses the state-of-the-art alternatives on DC, NL, besides computation time and convergence rate.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A hybrid model using JAYA-GA metaheuristics for placement of fog nodes in fog-integrated cloud\",\"authors\":\"Satveer Singh, Deo Prakash Vidyarthi\",\"doi\":\"10.1007/s12652-024-04796-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>It has been observed that Cloud services exhibit suboptimal performance for real-time requests due to increased network delay. Fog computing has emerged to address this issue by deploying Fog nodes at the network's edge. However, determining the optimal placement of Fog nodes for efficient service processing poses a significant challenge. The multiple ways to deploy a Fog node makes the Fog node placement an NP-class problem. It leverages the potential benefit of metaheuristic to solve this problem. In this work, we formulate a linear mathematical model for fog node placement (FNP), considering two important objectives for minimization, i.e., deployment cost (DC) and network latency (NL). A hybrid metaheuristic approach using genetic algorithm (GA) and JAYA, called JAYA-GA, is proposed to address this multi-objective optimization. The proposed model is simulated and the experimental results are compared against three popularly used metaheuristics: particle swarm optimization (PSO), GA, and JAYA. The proposed model consistently outperforms JAYA, PSO, and GA by the averages of 18.40%, 33.58%, and 30.75%, respectively, in terms of fitness (a weighted sum of DC and NL). Additionally, it exhibits superior performance on average convergence rate (16.60%, 53.42%, and 86.59%) and computation time (15.76%, 34.74%, and 59.22%) compared to JAYA, PSO, and GA respectively. Thus, the simulation results establish that the hybrid JAYA-GA technique surpasses the state-of-the-art alternatives on DC, NL, besides computation time and convergence rate.</p>\",\"PeriodicalId\":14959,\"journal\":{\"name\":\"Journal of Ambient Intelligence and Humanized Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Ambient Intelligence and Humanized Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s12652-024-04796-w\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Ambient Intelligence and Humanized Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12652-024-04796-w","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
A hybrid model using JAYA-GA metaheuristics for placement of fog nodes in fog-integrated cloud
It has been observed that Cloud services exhibit suboptimal performance for real-time requests due to increased network delay. Fog computing has emerged to address this issue by deploying Fog nodes at the network's edge. However, determining the optimal placement of Fog nodes for efficient service processing poses a significant challenge. The multiple ways to deploy a Fog node makes the Fog node placement an NP-class problem. It leverages the potential benefit of metaheuristic to solve this problem. In this work, we formulate a linear mathematical model for fog node placement (FNP), considering two important objectives for minimization, i.e., deployment cost (DC) and network latency (NL). A hybrid metaheuristic approach using genetic algorithm (GA) and JAYA, called JAYA-GA, is proposed to address this multi-objective optimization. The proposed model is simulated and the experimental results are compared against three popularly used metaheuristics: particle swarm optimization (PSO), GA, and JAYA. The proposed model consistently outperforms JAYA, PSO, and GA by the averages of 18.40%, 33.58%, and 30.75%, respectively, in terms of fitness (a weighted sum of DC and NL). Additionally, it exhibits superior performance on average convergence rate (16.60%, 53.42%, and 86.59%) and computation time (15.76%, 34.74%, and 59.22%) compared to JAYA, PSO, and GA respectively. Thus, the simulation results establish that the hybrid JAYA-GA technique surpasses the state-of-the-art alternatives on DC, NL, besides computation time and convergence rate.
期刊介绍:
The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to):
Pervasive/Ubiquitous Computing and Applications
Cognitive wireless sensor network
Embedded Systems and Software
Mobile Computing and Wireless Communications
Next Generation Multimedia Systems
Security, Privacy and Trust
Service and Semantic Computing
Advanced Networking Architectures
Dependable, Reliable and Autonomic Computing
Embedded Smart Agents
Context awareness, social sensing and inference
Multi modal interaction design
Ergonomics and product prototyping
Intelligent and self-organizing transportation networks & services
Healthcare Systems
Virtual Humans & Virtual Worlds
Wearables sensors and actuators