Algorithm of Text Categorization Based on Cloud Computing

2013 ◽  
Vol 311 ◽  
pp. 158-163 ◽  
Author(s):  
Li Qin Huang ◽  
Li Qun Lin ◽  
Yan Huang Liu
Keyword(s):  
Cloud Computing ◽  
Text Categorization ◽  
Experimental Results ◽  
Support Vector ◽  
Computing Environment ◽  
Mapreduce Framework ◽  
Cloud Computing Environment ◽  
Environment Map ◽  
Vector Machines ◽  
Parallel Text

MapReduce framework of cloud computing has an effective way to achieve massive text categorization. In this paper a distributed parallel text training algorithm in cloud computing environment based on multi-class Support Vector Machines(SVM) is designed. In cloud computing environment Map tasks realize distributing various types of samples and Reduce tasks realize the specific SVM training. Experimental results show that the execution time of text training decreases with the number of Reduce tasks increasing. Also a parallel text classifying based on cloud computing is designed and implemented, which classify the unknown type texts. Experimental results show that the speed of text classifying increases with the number of Map tasks increasing.

Resource Scheduling Optimization Model in Cloud Computing Environment

2014 ◽  
Vol 687-691 ◽  
pp. 1645-1648
Author(s):  
Chun Yan Kang ◽  
Tie Jun Shi
Keyword(s):  
Support Vector Machine ◽  
Cloud Computing ◽  
Computational Complexity ◽  
Support Vector ◽  
Computing Environment ◽  
Scheduling Optimization ◽  
Cloud Computing Environment ◽  
Computing Model ◽  
Resource Index ◽  
Different Levels

In the process of cloud computing, the dynamic hierarchical resource index is researched, and the independent confusion cloud computing is studied. This problem has become the focus of data processing. Therefore, it needs to establish improved dynamic layered resource index independent confuse cloud computing model. According to the theory of support vector machine, all of the resources are taken with dynamical layered processing, different levels of resources are taken with the independent confusion cloud computing. The experiment results show that, this algorithm is taken for the dynamic layered resource cloud computing, calculation efficiency can be improved, computational complexity and redundancy are reduced, meet the practical demands of dynamic hierarchical resource index independent confused cloud computing. It has good application value in the cloud computing application.

scholarly journals Design and implementation of endogenous security container based on union file system

2021 ◽  
Vol 2078 (1) ◽  
pp. 012080
Author(s):  
Fukang Xing ◽  
Zheng Zhang ◽  
Bolin Ma ◽  
Bingzheng Li
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Fault Tolerance ◽  
Data Processing ◽  
File System ◽  
Experimental Results ◽  
Computing Environment ◽  
Big Data Processing ◽  
Cloud Computing Environment ◽  
Design And Implementation

Abstract In order to solve the increasing attacks on container file system and the IO errors of containers in big data processing scenarios in cloud computing environment, a scheme based on the idea of heterogeneous redundancy in endogenous security and transformation of container union file system was proposed to improve the security and fault tolerance of containers. Based on the above scheme, experiments are carried out on Docker, the most popular container technology, and OverlayFS, the most representative union file system. The experimental results show that this scheme can improve the security and fault tolerance of containers on the premise of ensuring availability, and realize the endogenous security of containers.

scholarly journals Deadline Constrained based Resource Allocation in Cloud Environment

2019 ◽  
Vol 9 (1) ◽  
pp. 1486-1491
Keyword(s):  
Cloud Computing ◽  
Performance Improvement ◽  
Experimental Results ◽  
Cloud Environment ◽  
Computing Environment ◽  
Task Execution ◽  
Scientific Application ◽  
Improvement Rate ◽  
Cloud Computing Environment ◽  
Deadline Constraint

Cloud computing faces a challenge of handling huge amounts of data. The users keep on pushing the data without knowing the challenge in increased storage. Task Scheduling deals with allocating the task to a respective resource pool on a demand basis. Approaches have been built that handle requests from users with deadlines on the amount of request that can be handled. It is important to understand that the mechanism is available to handle the deadlines. The experimental results show that the proposed algorithm produces remarkable performance improvement rate on the total execution cost and total transfer time under meeting the deadline constraint. In view of the experimental results, the proposed algorithm provides a better-quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment.

scholarly journals Survey and Performance Analysis of Machine Learning Based Security Threats Detection Approaches in Cloud Computing

2021 ◽  
pp. 49-58
Author(s):  
Rajesh Keshavrao Sadavarte ◽  
Dr. G. D. Kurundkar
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Cloud Services ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Threat Detection ◽  
Security Threats ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Learning Techniques

Cloud computing is gaining a lot of attention, however, security is a major obstacle to its widespread adoption. Users of cloud services are always afraid of data loss, security threats and availability problems. Recently, machine learning-based methods of threat detection are gaining popularity in the literature with the advent of machine learning techniques. Therefore, the study and analysis of threat detection and prevention strategies are a necessity for cloud protection. With the help of the detection of threats, we can determine and inform the normal and inappropriate activities of users. Therefore, there is a need to develop an effective threat detection system using machine learning techniques in the cloud computing environment. In this paper, we present the survey and comparative analysis of the effectiveness of machine learning-based methods for detecting the threat in a cloud computing environment. The performance assessment of these methods is performed using tests performed on the UNSW-NB15 dataset. In this work, we analyse machine learning models that include Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), Random Forests (RF) and the K-Nearest neighbour (KNN). Additionally, we have used the most important performance indicators, namely, accuracy, precision, recall and F1 score to test the effectiveness of several methods.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

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