0% Complete
فارسی
Home
/
دوازدهمین کنفرانس بین المللی فناوری اطلاعات و دانش
Improving Fog Computing Scalability in Software Defined Network using Critical Requests Prediction in IoT
Authors :
Hajar Ghanbari
1
1- دانشگاه اصفهان
Keywords :
Internet of Things، Request Prediction، Software Defined Network، Fog Computing، Scalability
Abstract :
With the advent of technology, the Internet of Things (IoT) network has been confronted with large volumes of data and production requests like Critical Requests Cloud usage is not cost-effective due to the distance from the Cloud Data Centre. One of the best solutions to solve these problems. Use the Fog Computing auxiliary layer. Fog nodes also face processing limitations due to the large volume of requests. Inability to cooperate. Between Fog Nodes in this layer has resulted in Fog Computing Scalability being compromised. In this research, using the method of predicting the number of Critical. Requests and providing the required resources in Fog nodes as well as making Fog Nodes interoperable with each other by Software-Defined Network (SDN) tried to use the resources in the Fog layer to serve as much as possible to unforeseen requests. In this proposal, it has been able to reduce the service delay, utilization rate of fog layer resources and bandwidth consumption in comparison with the other two methods by 2, 6 and 13% Improve.
Papers List
List of archived papers
LLM-Driven Feature Extraction for Stock Market Prediction: A case study of Tehran Stock Exchange
Siavash Hosseinpour Saffarian - Saman Haratizadeh
IT-based and Non-IT-based methods to separate and collect waste
Hoda Harati - Farzad Haghighi-Rad - Reza Yousefi Zenouz
Load Balancing in Software-Defined Networks Using Multi-Level Thresholds and Hybrid Switch Migration Strategies
Alireza Karimi - Mohammad yousef Darmani
ElectroCNN: Regressive CNN-based Energy Consumption Forecasting Leveraging Weather Data
Dharmi Patel - Mann Patel - Krisha Darji - Rajesh Gupta - Sudeep Tanwar - Jitendra Bhatia - Hossein Shahinzadeh
مدل یادگیری عمیق با بازنمایی چند مقیاسی زمان برای پیشبینی آبشار اطلاعاتی در شبکههای اجتماعی
مبینا پناهی - مهدی عمادی
A Comparison between Slimed Network and Pruned Network for Head Pose Estimation
Amir Salimiparsa - Hadi Veisi - Mohammad-shahram Moin
Context Awareness Gate for Retrieval Augmented Generation
Mohammad Hassan Heydari - Arshia Hemmat - Erfan Naman - Afsaneh Fatemi
Predicting Concentration of Particulate Matter (PM2.5) in Hamedan using Machine Learning Algorithms
Anita Karim Ghassabpour - Hatam Abdoli - Muharram Mansoorizadeh - Saeid Seyedi
A Mathematical Optimization Approach for Preference Learning in Movie Recommender Systems with Shared Accounts
Milad Khademali - Fazlollah Aghamohammadi - Marjan Kaedi - Alireza Nasiri
آسیب شناسی استقرار بلاکچین در صنعت بانکی کشور ایران
نیلوفر مرادحاصل
more
Samin Hamayesh - Version 43.8.0