A hybrid model using JAYA-GA metaheuristics for placement of fog nodes in fog-integrated cloud

3区 计算机科学 Q1 Computer Science Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-21 DOI:10.1007/s12652-024-04796-w
Satveer Singh, Deo Prakash Vidyarthi
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Abstract

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.

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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
据观察,由于网络延迟增加,云服务在处理实时请求时表现出不理想的性能。为解决这一问题,雾计算应运而生,在网络边缘部署雾节点。然而,确定雾节点的最佳位置以实现高效服务处理是一项重大挑战。部署雾节点的多种方法使雾节点的布置成为一个 NP 级问题。这就需要利用元启发式的潜在优势来解决这个问题。在这项工作中,我们建立了一个雾节点放置(FNP)的线性数学模型,考虑了两个重要的最小化目标,即部署成本(DC)和网络延迟(NL)。为解决这一多目标优化问题,提出了一种使用遗传算法(GA)和 JAYA 的混合元启发式方法,称为 JAYA-GA。对所提出的模型进行了仿真,并将实验结果与粒子群优化 (PSO)、GA 和 JAYA 这三种常用的元启发式方法进行了比较。就适配度(DC 和 NL 的加权和)而言,拟议模型始终优于 JAYA、PSO 和 GA,平均分别为 18.40%、33.58% 和 30.75%。此外,与 JAYA、PSO 和 GA 相比,它在平均收敛率(16.60%、53.42% 和 86.59%)和计算时间(15.76%、34.74% 和 59.22%)方面也表现出更优越的性能。因此,仿真结果表明,除了计算时间和收敛速度外,JAYA-GA 混合技术在 DC、NL 方面都超过了最先进的替代技术。
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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
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
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