A Probability Based Joint-Clustering Algorithm for Application Placement in Fog-to-Cloud Computing

2021 ◽  
Author(s):  
M.Sri Raghavendra ◽  
Priyanka Chawla ◽  
Y. Narasimhulu
Keyword(s):  
Cloud Computing ◽  
Clustering Algorithm ◽  
Application Placement

A Dynamic Clustering Algorithm for Cloud Computing

2013 ◽  
Vol 12 (18) ◽  
pp. 4637-4641
Author(s):  
Zhongxue Yang ◽  
Xiaolin Qin ◽  
Wenrui Li ◽  
Yingjie Yang
Keyword(s):  
Cloud Computing ◽  
Clustering Algorithm ◽  
Dynamic Clustering

A privacy‐preserving density peak clustering algorithm in cloud computing

2020 ◽  
Vol 32 (11) ◽  
Author(s):  
Liping Sun ◽  
Shang Ci ◽  
Xiaoqing Liu ◽  
Xiaoyao Zheng ◽  
Qingying Yu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Clustering Algorithm ◽  
Privacy Preserving ◽  
Density Peak ◽  
Density Peak Clustering

Intrusion Detection Based on Dynamic Gemini Population DE-K-mediods Clustering on Hadoop Platform

2020 ◽  
pp. 2150001
Author(s):  
Wentie Wu ◽  
Shengchao Xu
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Differential Evolution ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Detection System ◽  
Differential Evolution Algorithm ◽  
Detection Algorithm ◽  
Local Optimum ◽  
Evolution Algorithm

In view of the fact that the existing intrusion detection system (IDS) based on clustering algorithm cannot adapt to the large-scale growth of system logs, a K-mediods clustering intrusion detection algorithm based on differential evolution suitable for cloud computing environment is proposed. First, the differential evolution algorithm is combined with the K-mediods clustering algorithm in order to use the powerful global search capability of the differential evolution algorithm to improve the convergence efficiency of large-scale data sample clustering. Second, in order to further improve the optimization ability of clustering, a dynamic Gemini population scheme was adopted to improve the differential evolution algorithm, thereby maintaining the diversity of the population while improving the problem of being easily trapped into a local optimum. Finally, in the intrusion detection processing of big data, the optimized clustering algorithm is designed in parallel under the Hadoop Map Reduce framework. Simulation experiments were performed in the open source cloud computing framework Hadoop cluster environment. Experimental results show that the overall detection effect of the proposed algorithm is significantly better than the existing intrusion detection algorithms.

scholarly journals Research on the Development of Innovation Path of Ideological and Political Education in Colleges and Universities Based on Cloud Computing and K-Means Clustering Algorithm Model

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Juan Hong
Keyword(s):  
Cloud Computing ◽  
Communication Technology ◽  
Clustering Algorithm ◽  
College Education ◽  
Colleges And Universities ◽  
Ideological And Political Education ◽  
Political Education ◽  
Main Trend ◽  
Computing Technology ◽  
Innovative Methods

As a distributed computing technology, cloud computing has the characteristics of fast processing speed, large-capacity data processing, and high overall efficiency. With the continuous development of communication technology today, cloud computing technology has begun to be applied in various industries and has promoted the development and progress of the industry. As an important part of college education, ideological and political education in colleges and universities is also constantly developing. The integration of cloud computing and ideological and political education is the main trend in the future. At present, the application research of cloud computing and wireless communication technology in the ideological and political education of colleges and universities has obtained some results, but there are still some problems. Therefore, the research on innovative methods of ideological and political education is extremely important. At present, under the attention of all walks of life, scholars have strengthened the research on new paths of ideological and political education in colleges and universities, and they are also constantly experimenting with innovative methods. In this context, this paper studies the current situation of ideological and political education in colleges and universities through questionnaire surveys and analyzes the innovative mode of ideological and political education in colleges and universities in the context of cloud computing through the K-means clustering algorithm model.

A Cloud Intrusion Detection System Using Novel PRFCM Clustering and KNN Based Dempster-Shafer Rule

2016 ◽  
Vol 6 (4) ◽  
pp. 18-35 ◽  
Author(s):  
Partha Ghosh ◽  
Shivam Shakti ◽  
Santanu Phadikar
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Clustering Algorithm ◽  
Detection System ◽  
Cloud Environment ◽  
Dempster Shafer Theory ◽  
Computing Paradigm ◽  
Rule Set ◽  
Shafer Theory

Cloud computing has established a new horizon in the field of Information Technology. Due to the large number of users and extensive utilization, the Cloud computing paradigm attracts intruders who exploit its vulnerabilities. To secure the Cloud environment from such intruders an Intrusion Detection System (IDS) is required. In this paper the authors have proposed an anomaly based IDS which classifies an incoming connection by taking the deviation of it from the normal behaviors. The proposed method uses a novel Penalty Reward based Fuzzy C-Means (PRFCM) clustering algorithm to generate a rule set and the best rule set is extracted from it using a modified approach for KNN algorithm. This best rule set is used in evidential reasoning of Dempster Shafer Theory for classification. The IDS has been trained and tested with NSL-KDD dataset for performance evaluation. The results prove the proposed IDS to be highly efficient and reliable.

scholarly journals Secured and Dynamic Decentralized Computational Offloading Framework for Mobile Cloud Computing

2019 ◽  
Vol 8 (11) ◽  
pp. 2589-2593
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Clustering Algorithm ◽  
Mobile Cloud Computing ◽  
Image Data ◽  
Input Image ◽  
Mobile Cloud ◽  
Computing Technique ◽  
Fuzzy C Means Clustering ◽  
Significant Performance

Mobile Cloud Computing is represented as an advanced mobile computing technique by integrating with resource rich servers of various clouds and networks towards infinite mobility, computation, functionality and storage in favour to overcome the restrictions of mobile devices. Mobile Cloud Computing is a platform where storing and processing of data are performed away from mobile devices and processed in the cloud and bringing back results to mobile device to improve the capabilities of mobile devices. The main objective of this study is to minimize response time and to improve the battery performance of mobile devices. In this research, an intelligent secure and dynamic decentralized framework is proposed which will provide accurate decision for execution environment for the application either local or at cloud using SENN classifier and DCNN Model. Modified Fuzzy C Means Clustering algorithm is put forward to create alike clusters for the profiler parameters information collected by smonitor. Moreover, the proposed framework provides more security by encrypting the input image data while transferred to cloud server using blowfish technique can protect the application data from threats. The test results in simulation environment proved that SDCNN framework achieved significant performance by minimizing the consumption of energy and execution time by offloading computation intensive task to clouds.

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