An Overloading State Computation and Load Sharing Mechanism in Fog Computing

2021 ◽  
Vol 14 (4) ◽  
pp. 94-106
Author(s):  
Pushpa Singh ◽  
Rajeev Agrawal
Keyword(s):  
Data Storage ◽  
Fog Computing ◽  
Load Sharing ◽  
Resource Provisioning ◽  
Time Data ◽  
High Demand ◽  
Node Failure ◽  
Proposed Model ◽  
Real Time Data ◽  
Efficient Resource

Fog computing is used to enrich the ability of cloud computing applications. Fog is a kind of buffer area placed between the data processing location and the data storage equipment in the network and plays a significant role in processing the real time data. The lack of resource provisioning approaches and high demand for IoT services make the fog node overloaded. Load balancing is a method to realize efficient resource utilization to avoid bottlenecks, overload, and fog node failure. This study suggests a concept to compute the probabilistic overloading state of a fog node and identification of fog node for load sharing. Each fog node computes Fstate and sends the message at regular intervals to the fog node coordinator (FNC). FNC maintains a fog that is utilized for offloading in case of fog overloading. A comparative study shows that the proposed model avoids an overloading state by the transfer of a certain number of requests to an underloaded fog node before actual overloading occurs. Numerical results validate theoretical investigation and efficiency of the proposed study.

scholarly journals Analisa Real-Time Data log honeypot menggunakan Algoritma K-Means pada serangan Distributed Denial of Service

Repositor ◽  
2020 ◽  
Vol 2 (5) ◽  
pp. 541
Author(s):  
Denni Septian Hermawan ◽  
Syaifuddin Syaifuddin ◽  
Diah Risqiwati
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Denial Of Service ◽  
The Internet ◽  
Distributed Denial Of Service ◽  
Time Data ◽  
Local Networks ◽  
Real Time Data ◽  
Internet Networks ◽  
Data Log

AbstrakJaringan internet yang saat ini di gunakan untuk penyimpanan data atau halaman informasi pada website menjadi rentan terhadap serangan, untuk meninkatkan keamanan website dan jaringannya, di butuhkan honeypot yang mampu menangkap serangan yang di lakukan pada jaringan lokal dan internet. Untuk memudahkan administrator mengatasi serangan digunakanlah pengelompokan serangan dengan metode K-Means untuk mengambil ip penyerang. Pembagian kelompok pada titik cluster akan menghasilkan output ip penyerang.serangan di ambil sercara realtime dari log yang di miliki honeypot dengan memanfaatkan MHN.Abstract The number of internet networks used for data storage or information pages on the website is vulnerable to attacks, to secure the security of their websites and networks, requiring honeypots that are capable of capturing attacks on local networks and the internet. To make it easier for administrators to tackle attacks in the use of attacking groupings with the K-Means method to retrieve the attacker ip. Group divisions at the cluster point will generate the ip output of the attacker. The strike is taken as realtime from the logs that have honeypot by utilizing the MHN.

scholarly journals Drilling Efficiency Improvement and Rate of Penetration Optimization by Machine Learning and Data Analytics

2020 ◽  
Vol 5 (3) ◽  
pp. 381-394
Author(s):  
Sridharan Chandrasekaran ◽  
G. Suresh Kumar
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Storage ◽  
Predictive Accuracy ◽  
Empirical Models ◽  
Operational Parameters ◽  
Time Data ◽  
Bottom Line ◽  
Rate Of Penetration ◽  
Real Time Data

Rate of Penetration (ROP) is one of the important factors influencing the drilling efficiency. Since cost recovery is an important bottom line in the drilling industry, optimizing ROP is essential to minimize the drilling operational cost and capital cost. Traditional the empirical models are not adaptive to new lithology changes and hence the predictive accuracy is low and subjective. With advancement in big data technologies, real- time data storage cost is lowered, and the availability of real-time data is enhanced. In this study, it is shown that optimization methods together with data models has immense potential in predicting ROP based on real time measurements on the rig. A machine learning based data model is developed by utilizing the offset vertical wells’ real time operational parameters while drilling. Data pre-processing methods and feature engineering methods modify the raw data into a processed data so that the model learns effectively from the inputs. A multi – layer back propagation neural network is developed, cross-validated and compared with field measurements and empirical models.

Status Monitoring and Fault Diagnosis Network System Based on UPnP

2007 ◽  
Vol 353-358 ◽  
pp. 2632-2635
Author(s):  
Pei Yu Li ◽  
Da Peng Tan ◽  
Tao Qing Zhou ◽  
Bo Yu Lin
Keyword(s):  
Fault Diagnosis ◽  
Embedded System ◽  
Real Time ◽  
Data Storage ◽  
Network Architecture ◽  
Network System ◽  
Time Data ◽  
Status Monitoring ◽  
Self Recognition ◽  
Real Time Data

Aiming at some problems in the fields of industry monitoring technology (IMT) such as bad dynamic ability and poor versatility, this paper brought forward a kind of intelligent Status monitoring and Fault diagnosis Network System (SFNS) based on UPnP-Universal Plug and Play. The model for fault diagnosis network system was established according to characteristics and requirements of IMT network, and system network architecture was designed and realized by UPnP. Using embedded system technology, real-time data collection node, monitoring center node and data storage server were designed, and that supplies powerful real-time data support for SFNS. Industry fields experiments proved that this system can realize self recognition, seamless linkage and other self adapting ability, and can break through the limitation of real IP address to achieve real-time remote monitoring on line.

Resource Provisioning and Scheduling Techniques of IoT Based Applications in Fog Computing

2019 ◽  
Vol 2 (2) ◽  
pp. 57-70 ◽  
Author(s):  
Rajni Gupta
Keyword(s):  
Oil And Gas ◽  
Health Care Systems ◽  
Fog Computing ◽  
Fast Response ◽  
Resource Provisioning ◽  
Time Data ◽  
Computing Paradigm ◽  
Care Systems ◽  
Cloud Environments ◽  
Suitable Technique

Internet of Things (IoT) has emerged as a computing paradigm to develop smart applications such e-health care systems, smart city, smart waste management systems, etc. It contains a large number of different devices and heterogeneous networks, which make it difficult to provide secure and fast response to the end user. To provide the faster response services, there is a need to use the concept of Fog computing Recently, the use of fog computing is a rapidly increasing in many industries for the development of applications such as manufacturing, e-health, oil and gas, As more and more users have started to store/process their real-time data in Fog-based Cloud environments, resource provisioning and scheduling of IoT based applications becomes a key element of consideration for efficient execution of these applications. This article will help to select the most suitable technique for processing smart IoT based applications in Fog computing environments.

Towards cost-efficient resource provisioning with multiple mobile users in fog computing

2020 ◽  
Vol 146 ◽  
pp. 96-106
Author(s):  
Shuaibing Lu ◽  
Jie Wu ◽  
Yubin Duan ◽  
Ning Wang ◽  
Juan Fang
Keyword(s):  
Fog Computing ◽  
Resource Provisioning ◽  
Mobile Users ◽  
Efficient Resource ◽  
Cost Efficient

scholarly journals Genetic Algorithm based Predictive model for COVID 19 – Theoretical Model based on an evolutionary approach

2021 ◽  
Author(s):  
Srikanth Rangarajan ◽  
Srikanth Poranki ◽  
Bahgat Sammakia
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Theoretical Method ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Time Data ◽  
Proposed Model ◽  
Fitness Value ◽  
Real Time Data ◽  
Unique Part

Abstract In this manuscript we propose a novel theoretical method that models the evolution, spread and transmission of COVID 19 pandemic. The proposed model is inspired partly from the evolutionary based state of the art genetic algorithm. The rate of virus evolution, spread and transmission of the COVID 19 and its associated recovery and death rate are modeled using the principle inspired from evolutionary algorithm. Furthermore, the interaction within a community and interaction outside the community is modeled. The constraint with respect to interaction has been implemented by a machine learning type algorithm and becomes the unique part of our study . Using this model, the maximum healthcare threshold is fixed as a constraint. Our evolutionary based model distinguishes between individuals in the population depending on the severity of their symptoms/infection based on the fitness value of the individuals. There is a need to differentiate between virus infected diagnosed (Self isolated) and virus infected non-diagnosed (Highly interacting) sub populations/group. In this study the model results does not compare the number outcomes with any actual real time data based curves. However, the results from the model demonstrates that a strict lockdown, social-distancing measures in conjunction with more number of testing and contact tracing is required to flatten the ongoing COVID-19 pandemic curve. A reproductive number of 2.4 during the initial spread of virus is predicted from the model for the randomly considered population. The proposed model has the potential to be further fine-tuned and matched accurately against real time data.

scholarly journals Multivariate Data Quality Enhancement by Ranked Imputation

2020 ◽  
Vol 9 (3) ◽  
pp. 2387-2391
Keyword(s):  
State Of The Art ◽  
Multivariate Data ◽  
Quality Enhancement ◽  
Data Imputation ◽  
Time Data ◽  
Proposed Model ◽  
Real Time Data ◽  
Attribute Order ◽  
Negative Impacts

Organizational decisions are based on data-based-analysis and predictions. Effective decisions require accurate predictions, which in-turn depend on the quality of the data. Real time data is prone to inconsistencies, which exhibit negative impacts on the quality of the predictions. This mandates the need for data imputation techniques. This work presents a prediction-based data imputation technique, Rank Based Multivariate Imputation (RBMI) that operates on multivariate data. The proposed model is composed of the ranking phase and the imputation phase. Ranking dictates, the attribute order in which imputation is to be performed. The proposed model utilizes tree-based approach for the actual imputation process. Experiments were performed on Pima, a diabetes dataset. The data was amputed in range between 5% - 30%. The obtained results were compared with existing state-of-the-art models in terms of MAE and MSE levels. The proposed RBMI model exhibits a reduction of 0.03 in MAE levels and 0.001 in MSE levels.

Vehicular Fog Computing

2017 ◽  
Vol 1 (2) ◽  
pp. 15-23 ◽  
Author(s):  
Varun G. Menon ◽  
Joe Prathap
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Fog Computing ◽  
Security And Privacy ◽  
Future Research ◽  
Collision Warning ◽  
Efficient Resource ◽  
Hoc Networks

In recent years Vehicular Ad Hoc Networks (VANETs) have received increased attention due to its numerous applications in cooperative collision warning and traffic alert broadcasting. VANETs have been depending on cloud computing for networking, computing and data storage services. Emergence of advanced vehicular applications has led to the increased demand for powerful communication and computation facilities with low latency. With cloud computing unable to satisfy these demands, the focus has shifted to bring computation and communication facilities nearer to the vehicles, leading to the emergence of Vehicular Fog Computing (VFC). VFC installs highly virtualized computing and storage facilities at the proximity of these vehicles. The integration of fog computing into VANETs comes with a number of challenges that range from improved quality of service, security and privacy of data to efficient resource management. This paper presents an overview of this promising technology and discusses the issues and challenges in its implementation with future research directions.

The Real-Time Optimization Strategy on the Mission Database

2014 ◽  
Vol 1049-1050 ◽  
pp. 2001-2005
Author(s):  
Hua Wang ◽  
Bing Liu ◽  
Huan Ming Liu ◽  
Hui Fen Duan ◽  
Jun Lei Bao
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Relational Database ◽  
Optimization Strategy ◽  
Time Data ◽  
The Real ◽  
Storage Mechanism ◽  
Synchronization Mechanism ◽  
Real Time Data ◽  
And Control

In order to make up the real-time performance of tracking and control information database, this paper design a kind of two-layer’s real-time data storage model based on memory database and relational database. In this article, the two-layer’s real-time data storage mechanism and life cycle are expounded in detail, analyzing and inducing the real-time data characteristic and storage strategy, putting forward the memory database’s self-adaptive index algorithm of T-tree index and hash index, and introducing the database synchronization mechanism between the memory database and relational database and so on. In this way, so as to improve and optimize the real-time, reliability and security of database, provides a reliable data guarantee for future expansion of the real-time application.

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