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Clustering with Terracotta

Authors: Sun, Wei;

Clustering with Terracotta

Abstract

In today’s java community, modern enterprise application products have more constraints and requirements then ever. High availability, application scalability and also good performance are required, which means an application is needed to be deployed on multiple JVMs, in other words, it has to be clustered or distributed. It is essential for the application to scale out well, has better performance and less complexity during development of clustering. This master thesis focuses on clustering with Terracotta which is a JVM level clustering technique. First I start analyzing the complexity when an application comes into scale-out, and also analyzing the shortcomings of the common approaches to clustering an application. Then I do a deep dive to the Terracotta and demonstrate how to utilize Terracotta to conquer these problems with emphasis on providing scalability and high performance using natural java programming. Finally, various scenarios are made as benchmark tests. The final result has shown that Terracotta as redundancy solution is strong recommended to be implemented for high availability and high scalability

Masteroppgave i informasjons- og kommunikasjonsteknologi 2008 – Universitetet i Agder, Grimstad

Country
Norway
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Keywords

IKT590, VDP::Mathematics and natural science: 400::Information and communication science: 420::Theoretical computer science, programming languages and programming theory: 421

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green