1,720,972 research outputs found
Implementation of Microsoft SQL Server using ‘AlwaysOn’ for High Availability and Disaster Recovery without Shared Storage
Get PDFIt is important for any organization to handle increase in data; normally this is the job of the DBA in organization to take care of this data growth, along with its protection and its availability. High availability of data is also most important for organizations, for that purpose, shared storage is usually the solution, for example, SAN or NAS storage as Microsoft Cluster Database instances. Mostly Organization does not have shared storage in their infrastructure, which was required for Microsoft cluster SQL database instance; due to that they used other native methods to implement like log shipping or database mirroring, which was not efficient method for high-availability and disaster-recovery solution. After that, Microsoft introduced native method called SQL AlwaysOn method that could be implemented without using SAN and NAS storage method to implement high availability and disaster recovery without using shared storage. SQL AlwaysOn option is using only Microsoft cluster service and shared folder between the nodes. This paper written for DBA and System Administrators; and is implementation of Microsoft SQL Database tier step by step
Green Computing: From Current to Future Trends
No full textDuring recent years, attention in 'Green Computing'
has moved research into energy-saving techniques for home
computers to enterprise systems' Client and Server machines. Saving
energy or reduction of carbon footprints is one of the aspects of
Green Computing. The research in the direction of Green Computing
is more than just saving energy and reducing carbon foot prints. This
study provides a brief account of Green Computing. The emphasis of
this study is on current trends in Green Computing; challenges in the
field of Green Computing and the future trends of Green Computing
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Big Data Analysis: Apache Spark Perspective
No full textBig Data have gained enormous attention in recent years. Analyzing big data is very common requirement today and such requirements become nightmare when analyzing of bulk data source such as twitter twits are done, it is really a big challenge to analyze the bulk amount of twits to get relevance and different patterns of information on timely manner. This paper will explore the concept of Big Data Analysis and recognize some meaningful information from some sample big data source, such as Twitter twits, using one of industries emerging tool, known as Spark by Apache
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