...1 : Basic of Cluster Computing 1. INTRODUCTION 1.1 Background study Parallel computing has seen many changes since the days of the highly expensive and proprietary super computers. Changes and improvements in performance have also been seen in the area of mainframe computing for many environments. But these compute environments may not be the most cost effective and flexible solution for a problem. Over the past decade, cluster technologies have been developed that allow multiple low cost computers to work in a coordinated fashion to process applications. The economics, performance and flexibility of compute clusters makes cluster computing an attractive alternative to centralized computing models and the attendant to cost, inflexibility, and scalability issues inherent to these models. Many enterprises are now looking at clusters of high-performance, low cost computers to provide increased application performance, high availability, and ease of scaling within the data center. Interest in and deployment of computer clusters has largely been driven by the increase in the performance of off-the-shelf commodity computers, high-speed, low-latency network switches and the maturity of the software components. Application performance continues to be of significant concern for various entities including governments, military, education, scientific and now enterprise organizations. This document provides a review of cluster computing, the various types of clusters and their associated...
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...and configuring licensing on a cluster-enabled server. These steps assume you configured the clustering on the hardware on which you intend to install the license server. A detailed procedure follows. 1. Ensure that the first node has control of the cluster resources. 2. On the first node of the cluster, start the Citrix Licensing installation from the command-line and install it on the first node to the shared cluster drive (not the quorum drive). 3. Move the resources from the active node in the cluster to the second node. 4. Install the license server on the second node to the same shared location as the first node. 5. Obtain license files that specify the cluster name of the license server as the host name. After obtaining license files, you must add them to the license server and then reread them. 6. Configure your Citrix product to use the cluster name—not the node name—of the license server cluster. Note: When a clustered license server fails over, the cluster service renames the lmgrd_debug.log to the name of the node that previously hosted the services. Then it starts the services on the new active node and creates a new lmgrd_debug.log. To install licensing on a cluster-enabled server 1. Install Java on both cluster nodes. You can find a supported version on the Citrix product CD in the Support folder. 2. Ensure that the cluster IP address, cluster name, and a shared disk are configured as cluster resources and that all the cluster resources are owned by the first...
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...When I search the logo company, there are many different price. Some logo company can only quote price from the information provider. I provide one website information with price and service. There are four different price list which is Special, Silver, Golden, Express in LogoBees.com They charge Special service for $249 with 6 different custom initial logo concepts and will ready in 5 business days and will service unlimited revision until customer is satisfied. Silver service charge for $379 with 8 initial concepts will ready in 5 business days and will service unlimited revision until satisfied. Golden service charge for $449 include Stationery Design with 8 initial concepts will ready in 5 business days and will service unlimited revision. Express service charge for $549 include Stationery Design with 8 initial concepts will ready in 2 business and will service unlimited revision until customer is satisfied. 1.Trivas Group company with a new logo with organization name in a stylized, an abstract shape or symbol is used to convey the values of your business. The contest prize is $499. Elite Business Management company wants a new logo with image or shape that is easily recognizable and used to represent your business. The contest prize is $299. RPfB Consulting company wants a new logo with image, shape, letter to recognized their business name using darker gray, white, black, red color. The contest prize is $799. MFS Insurance, Inc wants a new logo with organization name...
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...FINAL REPORT DATA MINING Reported by: Nguyen Bao An – M9839920 Date: 99/06/16 Outline In this report I present my study in the Data mining course. It includes my two proposed approaches in the field of clustering, my learn lessons in class and my comment on this class. The report’s outline is as following: Part I: Proposed approaches 1. Introduction and backgrounds 2. Related works and motivation 3. Proposed approaches 4. Evaluation method 5. Conclusion Part II: Lessons learned 1. Data preprocessing 2. Frequent pattern and association rule 3. Classification and prediction 4. Clustering Part III: My own comments on this class. I. Proposed approach • An incremental subspace-based K-means clustering method for high dimensional data • Subspace based document clustering and its application in data preprocessing in Web mining 1. Introduction and background High dimensional data clustering has many applications in real world, especially in bioinformatics. Many well-known clustering algorithms often use a whole-space distance score to measure the similarity or distance between two objects, such as Euclidean distance, Cosine function... However, in fact, when the dimensionality of space or the number of objects is large, such whole-space-based pairwise similarity scores are no longer meaningful, due to the distance of each pair of object nearly the same [5]. ...
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...Advance America Implements Grid Computing Chances are you have seen places that offer payday loans in your town. Payday loans are short-term loans designed for people that run out of money before payday, but can repay the loan when their paycheck arrives. Advance America is the leading payday loan company in the United States. It includes 3,000 centers in 37 states, and employs nearly 7,000 people, according to its Web site. Advance America is big, and growing bigger every day. Its growth in recent years is straining the capabilities of its client-server information system infrastructure and holding the company back from further growth. Advance America used a system in which each center was equipped with an independent hardware and software environment. Installation and maintenance costs were high, and compiling data for all centers was time consuming and difficult. Each night the thousands of centers would upload their data to the main server for consolidation. With the growing number of centers, there wasn’t enough time in the night to process all of the incoming data. Advance America’s system had run up against a wall. It was time for a change. Advance America decided to invest in a new system based on a grid computing architecture. They installed thin client machines to run in each center, connecting via the Web to a fault-tolerant server cluster running Oracle database software. The server cluster consists of a four-node cluster ofIBM P5 series servers, which...
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...A COMPUTER IMPLEMENTATION OF ESTIMATED VARIANCES IN MULTI-STAGE CLUSTER SAMPLING SCHEMES L. A. Nafiu, L. Idris, A. F. Busari and A. B. Olaniyan Department of Mathematics and Statistics, Federal University of Technology, Minna, Niger State (lanconserv@yahoo.com) ABSTRACT The computation of sample variances arising from multi-stage cluster sampling schemes or designs are complex and time-consuming. This paper presents a computer software written with Java programing language for implementing some of the available formulars for estimated variances in multi-stage techniques. The software has the advantages of accessibility, cheapness, and ease of use in computing estimated variances in both one-stage, two-stage and three-stage sampling schemes. A data set for estimating number of diabetic patients in Niger state for 2005 was used for illustration. We recommend that computation involving these estimated variances be done with the aid of this software. Keywords: Software, Computation, Multi-stage, Estimated Variances, Time, Data and Diabetic Patients. Introduction Multistage sampling is where the researcher divides the population into clusters, samples the clusters, and then resample, repeating the process until the ultimate sampling units are selected at the last of the hierarchical levels (Okafor, 2002). For instance, at the top level, states may be sampled (with sampling proportionate to state population size); then cities may be sampled; then schools; then classes; and...
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... 10. Summary of project References Abstract The purpose of this project is to identify the impact of power and politics in Dan Mart Inc management decision in choosing information technology architecture that can provide a high availability and clustering in a business environment like Dan Mart Inc, this project will also identify the limitation power and politics, advantages and cost of implementing each one so as to have a choice of choosing from them all. But for the sake of this project the use of Oracle cooperation high availability and clustering technologies will be the target. We would be discussing different types of technologies by Oracle such as real application cluster(RAC), automatic storage management (ASM), data guard, grid infrastructure, grid control, cloud control, Flash back technology, database e-memory that will be suitable for Dan Mart Inc business environment. Brief Company Background DanMart is a high volume customer oriented business organization that require 24/7 availability of their services, they handle online sales...
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...Personalized Optimization for Android Smartphones WOOK SONG, YESEONG KIM, HAKBONG KIM, JEHUN LIM, and JIHONG KIM, Seoul National University As a highly personalized computing device, smartphones present a unique new opportunity for system optimization. For example, it is widely observed that a smartphone user exhibits very regular application usage patterns (although different users are quite different in their usage patterns). User-specific high-level app usage information, when properly managed, can provide valuable hints for optimizing various system design requirements. In this article, we describe the design and implementation of a personalized optimization framework for the Android platform that takes advantage of user’s application usage patterns in optimizing the performance of the Android platform. Our optimization framework consists of two main components, the application usage modeling module and the usage model-based optimization module. We have developed two novel application usage models that correctly capture typical smartphone user’s application usage patterns. Based on the application usage models, we have implemented an app-launching experience optimization technique which tries to minimize user-perceived delays, extra energy consumption, and state loss when a user launches apps. Our experimental results on the Nexus S Android reference phones show that our proposed optimization technique can avoid unnecessary application restarts by up to 78.4% over the default...
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...Sensors Relevance: Pervasive computing, temporal analysis to discover behaviour Method: MDS, Co-occurrence, HMMs, Agglomerative Clustering, Similarity Analysis Organization: MERL Published: July 2006, Pattern Recognition 39(10) Special Issue on Similarity Based Pattern Recognition Summary: Unsupervised discovery of structure from activations of very low resolution ambient sensors. Methods for discovering location geometry from movement patterns and behavior in an elevator scheduling scenario The context of this work is ambient sensing with a large number of simple sensors (1 bit per second giving on-off info). Two tasks are addressed. Discovering location geometry from patterns of sensor activations. And clustering activation sequences. For the former, a similarity metric is devised that measures the expected time of activation of one sensor after another has been activated, on the assumption that the two activations are resulting from movement. The time is used as a measure of distance between the sensors, and MDS is used to arrive at a geometric distribution. In the second part, the observation sequences are clustered by training HMMs for each sequence, and using agglomerative clustering. Having selected an appropriate number of clusters (chosen by the domain expert) the clusters can be used to train new HMM models. The straightforward mapping of the cluster HMMs is to a composite HMM, where each branch of the HMM corresponds to an HMM in the cluster. The authors note that...
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...such that a single group or cluster have similar characteristics while different groups are dissimilar. ROCK belongs to the class of agglomerative hierarchical clustering algorithms. OCK algorithm has mainly 3 steps namely, ‘Draw random sample’, ‘Cluster with links’, ‘Label data in disk’ the steps are described in the following diagram: ROCK’s hierarchical algorithm accepts as input the set S of N sample points to be clustered, and the number of desired clusters K. The first step in the procedure is to compute the number of links between pairs of points. Initially each point is separate cluster. For each cluster i, we build a local heap q[i] and maintain the heap during the execution of the algorithm. Q[i] contains every cluster j such that link[i,j] is non-zero. The clusters j in q[i] are ordered in the decreasing order of the goodness measure with respect to I, g(i,j). In addition to the local heaps q[i] for each cluster I, the algorithm also maintains an additional global heap q that contains all the clusters. Furthermore, the clusters in q are ordered in the decreasing order of their best goodness measures. Thus, g(j, max(q[j])) is used to order the various clusters j in q, where max(q[j]), the max element in q[j], is the best cluster to merge with cluster j. At each step, the max cluster j in q and the max cluster q[j[ are the best pair of clusters to be merged. Example program in R is as follows: For every point, after computing a list of its neighbors, the...
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...RESPONDENTS The researchers used ____ College Students from Education, Business Administration and Criminology Department who are currently enrolled in Metro Manila College, Novaliches Quezon City during the Academic year 2016 – 2017. SAMPLING TECHNIQUES The researchers utilized the cluster sampling technique in this study. According to Kelly (2013), Cluster sampling technique is a method of survey sampling which selects clusters such as groups defined by area of residence, organizational membership or other group-defining characteristics. It is often used where a complete list of subjects is impossible or impractical to construct. Cluster sampling is a two- (or more) stage process whereby clusters of individual units are first defined and selected and then samples of individual units are taken from each of the defined clusters. After getting the total population of each course from the registrar’s office and computing the 20 percent of each, the researcher came up with the total of ____ respondents for the BS Criminology, ___ respondents for the BS Education and ___ respondents for the BS Business Administration. The researchers randomly selected the respondents taken from each cluster. INSTRUMENT USED...
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...to be built, and the technology that needs to go into the project. System design is all how all the hardware is set up while the instruction set architecture is the program language. Table of Contents Introduction What OS file system should we use? What types of processors? Cluster Architecture? Data Backup LAN or WAN Web-based diagram Introduction to Coast to Coast computing We can get in a plane and be across the country in a few hours but what if we need to work together faster than a few hours? Is it possible? How hard is it to have users in California work with users in Washington, New York, and Florida? What has to be done to make this happen? How secure will it be to have such a network? This and many other questions come up when a company starts thinking of expansion beyond the building or city they headquartered at. When looking at a network a business wants to be sure it is using the most cost effective form for the network. The base of a network is the architecture. For small businesses a peer to peer architecture would be best. There are three types of peer-to-peer architecture: collaborative computing, instant messaging, and affinity communities. In collaborative computing the unused CPU processing power along with any free disk space is combined with machines on the same network. A very common peer-to-peer networking is instant messaging. Google Hangouts is an example of this where each user can communicate in real time via chat. In affinity communities...
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...Cluster Analysis1 Cluster analysis, like reduced space analysis (factor analysis), is concerned with data matrices in which the variables have not been partitioned beforehand into criterion versus predictor subsets. In reduced space analysis our interest centers on reducing the variable space to a smaller number of orthogonal dimensions, which maintains most of the information–metric or ordinal– contained in the original data matrix. Emphasis is placed on the variables rather than on the subjects (rows) of the data matrix. In contrast, cluster analysis is concerned with the similarity of the subjects–that is, the resemblance of their profiles over the whole set of variables. These variables may be the original set or may consist of a representation of them in reduced space (i.e., factor scores). In either case the objective of cluster analysis is to find similar groups of subjects, where “similarity” between each pair of subjects is usually construed to mean some global measure over the whole set of characteristics–either original variables or derived coordinates, if preceded by a reduced space analysis. In this section we discuss various methods of clustering and the key role that distance functions play as measures of the proximity of pairs of points. We first discuss the fundamentals of cluster analysis in terms of major questions concerning choice of proximity measure, choice of clustering technique, and descriptive measures by which the resultant clusters can...
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...Correlation Based Dynamic Clustering and Hash Based Retrieval for Large Datasets ABSTRACT Automated information retrieval systems are used to reduce the overload of document retrieval. There is a need to provide an efficient method for storage and retrieval .This project proposes the use of dynamic clustering mechanism for organizing and storing the dataset according to concept based clustering. Also hashing technique will be used to retrieve the data from the dataset based on the association rules .Related documents are grouped into same cluster by k-means clustering algorithm. From each cluster important sentences are extracted by concept matching and also based on sentence feature score. Experiments are carried to analyze the performance of the proposed work with the existing techniques considering scientific articles and news tracks as data set .From the analysis it is inferred that our proposed technique gives better enhancement for the documents related to scientific terms. Keywords Document clustering, concept extraction, K-means algorithm, hash-based indexing, performance evaluation 1. INTRODUCTION Now-a-days online submission of documents has increased widely, which means large amount of documents are accumulated for a particular domain dynamically. Information retrieval [1] is the process of searching information within the documents. An information retrieval process begins when a user enters a query; queries are formal statements of...
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...its “natural advantages” and promise that one day a mighty settlement will rise there. Speculative development is proceeding rapidly and unevenly. But right now the settlers seem a little eccentric and the humble structures they have erected lack the scale and elegance of those in better developed regions. Development is uneven and streets fail to connect. The native inhabitants have their ideas about how things should be done, which sometimes causes friction with new arrivals. In a generation everything will be quite unrecognizable, but how? This is a guide to the growing (if ramshackle) secondary literature in the history of information technology: monographs, edited volumes, and journal articles written to explore different aspects of computing from historical perspectives. Except as a frame in which to situate these secondary...
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