Artificial Intelligence

1301 Submissions

[5] viXra:1301.0183 [pdf] submitted on 2013-01-29 17:59:27

Computer Vision Applications on the Cloud

Authors: Yu Zhou
Comments: 3 Pages.

Cloud computing offers the potential to help scientists to process massive number of computing resources often required in machine learning application such as computer vision problems. This proposal would like to show that which benefits can be obtained from cloud in order to help medical image analysis users (including scientists, clinicians, and research institutes). As security and privacy of algorithms are important for most of algorithms’ inventors, these algorithms can be hidden in a cloud to allow the users to use the algorithms as a package without any access to see/change their inside. In another word, in the user part, users send their images to the cloud and configure the algorithm via an interface. In the cloud part, the algorithms are applied to this image and the results are returned back to the user. My proposal has two parts: (1) investigate the potential of cloud computing for computer vision problems and (2) study the components of a proposed cloud-based framework for medical image analysis application and develop them (depending on the length of the internship). The investigation part will involve a study on several aspects of the problem including security, usability (for medical end users of the service), appropriate programming abstractions for vision problems, scalability and resource requirements. In the second part of this proposal I am going to thoroughly study of the proposed framework components and their relations and develop them. The proposed cloud-based framework includes an integrated environment to enable scientists and clinicians to access to the previous and current medical image analysis algorithms using a handful user interface without any access to the algorithm codes and procedures.
Category: Artificial Intelligence

[4] viXra:1301.0107 [pdf] replaced on 2013-01-20 20:57:23

Automatic Tuning of MapReduce Jobs using Uncertain Pattern Matching Analysis

Authors: Nikzad Babaii Rizvandi, Javid Taheri, Reza Moraveji, Albert Y. Zomaya
Comments: 19 Pages.

In this paper, we study CPU utilization time patterns of several MapReduce applications. After extracting running patterns of several applications, the patterns along with their statistical information are saved in a reference database to be later used to tweak system parameters to efficiently execute future unknown applications. To achieve this goal, CPU utilization patterns of new applications along with its statistical information are compared with the already known ones in the reference database to find/predict their most probable execution patterns. Because of different pattern lengths, the Dynamic Time Warping (DTW) is utilized for such comparison; a statistical analysis is then applied to DTWs’ outcomes to select the most suitable candidates. Furthermore, under a hypothesis, we also proposed another algorithm to classify applications under similar CPU utilization patterns. Finally, dependency between minimum distance/maximum similarity of applications and their scalability (in both input size and number of virtual nodes) are studied. Here, we used widely used applications (WordCount, Distributed Grep, and Terasort) as well as an Exim Mainlog parsing application to evaluate our hypothesis in automatic tweaking MapReduce configuration parameters in executing similar applications scalable on both size of input data and number of virtual nodes. Results are very promising and showed the effectiveness of our approach on a private cloud with up to 25 virtual nodes.
Category: Artificial Intelligence

[3] viXra:1301.0024 [pdf] submitted on 2013-01-05 10:07:02

CloudSVM : Training an SVM Classifier in Cloud Computing Systems

Authors: F. Ozgur Catak, M. Erdal Balaban
Comments: 13 Pages.

In conventional distributed machine learning methods, distributed support vector machines (SVM) algorithms are trained over pre-configured in-tranet/internet environments to find out an optimal classifier. These methods are very complicated and costly for large datasets. Hence, we propose a method that is referred as the Cloud SVM training mechanism (CloudSVM) in a cloud computing environment with MapReduce technique for distributed machine learning applications. Accordingly, (i) SVM algorithm is trained in distributed cloud storage servers that work concurrently; (ii) merge all support vectors in every trained cloud node; and (iii) iterate these two steps until the SVM con-verges to the optimal classifier function. Single computer is incapable to train SVM algorithm with large scale data sets. The results of this study are im-portant for training of large scale data sets for machine learning applications. We provided that iterative training of splitted data set in cloud computing envi-ronment using SVM will converge to a global optimal classifier in finite iteration size.
Category: Artificial Intelligence

[2] viXra:1301.0017 [pdf] submitted on 2013-01-03 17:41:06

Statistical Performance Provisioning and Energy Efficiency in Distributed Computing Systems

Authors: Nikzad Babaii Rizvandi
Comments: 47 Pages.

This is a presenation on my PhD thesis about using statistical machine learning techniques to model and provision the performance of MapReduce and also energy efficient slack reclamation in distributed computing systems.
Category: Artificial Intelligence

[1] viXra:1301.0016 [pdf] submitted on 2013-01-03 17:52:51

High Performance Computing of Seismic Data on MapReduce

Authors: Nikzad Babaii Rizvandi
Comments: 1 Page.

After an overview of forward/inverse Prestack Kirchhoff Time Migration (PKTM) algorithm, we will explain our proposed approach to fit this algorithm for running on Google’s MapReduce framework. Toward the end, we will analyse the relation between MapReduce-based PKTM completion time and the number of map/reduce tasks on pseudo-distributed MapReduce mode.
Category: Artificial Intelligence