Anomaly network traffic detection algorithm based on information entropy measurement under the cloud computing environment

Image Mosaicing is an image processing technique that arises from the need of having a more realistic view of the real world wider than the view captured by the lenses of the available cameras. In this paper, a sequence of images will be mosaiced using binary edge detection algorithm in a cloud-computing environment to improve processing speed and accuracy. The authors have used Platform as a Service (PaaS) to provide a number of nodes in the cloud to run the computational intensive image processing and stitching algorithms. This increased the processing speed as most of image processing algorithms deal with every single pixel in the image. Message Passing Interface (MPI) is used for message passing among the compute-nodes in the cloud and a MapReduce technique is used for image distribution and collection, where the root node is used as reducer and the others as mappers. After applying the algorithm on different sequence of images and different machines on JUST cloud, the authors have achieved high mosaicing accuracy, and the execution time has been improved when comparing it with sequential execution on the images.

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Image Mosaicing Using Binary Edge Detection Algorithm in a Cloud-Computing Environment

Fog Computing ◽

10.4018/978-1-5225-5649-7.ch008 ◽

2018 ◽

pp. 183-197

Author(s):

Abdullah Alamareen ◽

Omar Al-Jarrah ◽

Inad A. Aljarrah

Keyword(s):

Image Processing ◽

Cloud Computing ◽

Edge Detection ◽

Processing Speed ◽

Message Passing ◽

Detection Algorithm ◽

Computing Environment ◽

Cloud Computing Environment ◽

Edge Detection Algorithm ◽

Binary Edge

Image Mosaicing is an image processing technique that arises from the need of having a more realistic view of the real world wider than the view captured by the lenses of the available cameras. In this paper, a sequence of images will be mosaiced using binary edge detection algorithm in a cloud-computing environment to improve processing speed and accuracy. The authors have used Platform as a Service (PaaS) to provide a number of nodes in the cloud to run the computational intensive image processing and stitching algorithms. This increased the processing speed as most of image processing algorithms deal with every single pixel in the image. Message Passing Interface (MPI) is used for message passing among the compute-nodes in the cloud and a MapReduce technique is used for image distribution and collection, where the root node is used as reducer and the others as mappers. After applying the algorithm on different sequence of images and different machines on JUST cloud, the authors have achieved high mosaicing accuracy, and the execution time has been improved when comparing it with sequential execution on the images.

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Collaborative filtering recommendation algorithm in cloud computing environment

Computer Science and Information Systems ◽

10.2298/csis200119008t ◽

2021 ◽

Vol 18 (2) ◽

pp. 517-534

Author(s):

Pei Tian

Keyword(s):

Cloud Computing ◽

Collaborative Filtering ◽

Programming Model ◽

Detection Algorithm ◽

Personalized Recommendation ◽

Computing Environment ◽

Recommendation Algorithm ◽

Cloud Computing Environment ◽

Detection Technology ◽

Application Data

With the advent of the era of cloud computing, the amount of application data increases dramatically, and personalized recommendation technology becomes more and more important. This paper mainly studies the collaborative filtering detection algorithm in the cloud computing environment. The algorithm migrates the collaborative filtering detection technology and applies it to the cloud computing environment. It shortens the recommendation time by using the advantages of clustering. A new recommendation algorithm can improve the accuracy of recommendation, and proposes a parallel collaborative filtering recommendation algorithm based on project. The algorithm is designed with programming model The experimental results show that the proposed algorithm has shorter running time and better scalability than the existing parallel algorithm.

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