Introduction

2010 ◽  
pp. 1-22
Author(s):  
Valentin Cristea ◽  
Ciprian Dobre ◽  
Corina Stratan ◽  
Florin Pop
Keyword(s):  
Distributed Computing ◽  
Distributed System ◽  
High Performance ◽  
Large Scale ◽  
Future Trends ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Computing Services ◽  
Peer To Peer Systems ◽  
Theoretical Results

The general presentation of Large Scale Distributed Computing and Applications can be done from different perspectives: historical, conceptual, architectural, technological, social, and others. This Introduction takes a pragmatic approach. It starts with a short presentation of definitions, goals, and fundamental concepts that frame the subjects targeted in the book: the Internet, the Web, Enterprise Information Systems, Peer-to-Peer Systems, Grids, Utility Computer Systems, and others. Then, each of these actual large scale distributed system categories is characterized in terms of typical applications, motivation of use, requirements and problems posed by their development: specific concepts, models, paradigms, and technologies. The focus is on describing the Large Scale Distributed Computing such as it appears today. Nevertheless, presenting actually used solutions will offer the opportunity to found that older theoretical results can still be exploited to build high performance artifacts. Also, the ever-ending stimulating relationship between users, who require better computing services, and providers, who discover new ways to satisfy them, is the motivation to introduce future trends in the domain, which pave the way towards the next generation Cyberinfrastructure. The chapter introduces a comprehensive set of concepts, models, and technologies, which are discussed in details in the next chapters.

Open Source for Accounting and Enterprise Systems

2011 ◽  
pp. 555-569
Author(s):  
Thomas Tribunella ◽  
James Baroody
Keyword(s):  
Information Systems ◽  
Open Source ◽  
Large Scale ◽  
Enterprise Systems ◽  
Future Trends ◽  
Enterprise Information ◽  
Maturity Models ◽  
Accounting Systems ◽  
Enterprise Information Systems ◽  
Financial Applications

This chapter introduces open source software (OSS) for accounting and enterprise information systems. It covers the background, functions, maturity models, adoption issues, strategic considerations, and future trends for small accounting systems as well as large-scale enterprise systems. The authors hope that understanding OSS for financial applications will not only inform readers of how to better analyze accounting and enterprise information systems but will also assist in the understanding of relationships among the various functions.

Using Network Analysis and Visualization to Analyze Problematic Enterprise Information Systems

2013 ◽  
pp. 291-310
Author(s):  
David Greenwood ◽  
Ian Sommerville
Keyword(s):  
Information Systems ◽  
Network Analysis ◽  
Large Scale ◽  
Research Question ◽  
Analytical Techniques ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Complex Information ◽  
Or Groups ◽  
Technical Issues

Society is demanding larger and more complex information systems to support increasingly complex and critical organisational work. Whilst troubleshooting socio-technical issues in small-to-medium scale situations may be achievable using approaches such as ethnography, troubleshooting enterprise scale situations is an open research question because of the overwhelming number of socio-technical elements and interactions involved. This paper demonstrates proof-of-concept tools for network analysis and visualisation that may provide a promising avenue for identifying problematic elements and interactions among an overwhelming number of socio-technical elements. The findings indicate that computers may be used to aid the analysis of problematic large-scale complex socio-technical situations by using analytical techniques to highlighting elements, or groups of interacting elements, that are important to the overall outcome of a problematic situation.

Synchronizing Execution of Big Data in Distributed and Parallelized Environments

Big Data ◽  
2016 ◽  
pp. 1555-1581
Author(s):  
Gueyoung Jung ◽  
Tridib Mukherjee
Keyword(s):  
Big Data ◽  
Distributed System ◽  
Data Analytics ◽  
High Performance ◽  
Large Scale ◽  
Big Data Analytics ◽  
Loosely Coupled ◽  
Current Trends ◽  
Distributed Computing Infrastructures ◽  
Performance Computing

In the modern information era, the amount of data has exploded. Current trends further indicate exponential growth of data in the future. This prevalent humungous amount of data—referred to as big data—has given rise to the problem of finding the “needle in the haystack” (i.e., extracting meaningful information from big data). Many researchers and practitioners are focusing on big data analytics to address the problem. One of the major issues in this regard is the computation requirement of big data analytics. In recent years, the proliferation of many loosely coupled distributed computing infrastructures (e.g., modern public, private, and hybrid clouds, high performance computing clusters, and grids) have enabled high computing capability to be offered for large-scale computation. This has allowed the execution of the big data analytics to gather pace in recent years across organizations and enterprises. However, even with the high computing capability, it is a big challenge to efficiently extract valuable information from vast astronomical data. Hence, we require unforeseen scalability of performance to deal with the execution of big data analytics. A big question in this regard is how to maximally leverage the high computing capabilities from the aforementioned loosely coupled distributed infrastructure to ensure fast and accurate execution of big data analytics. In this regard, this chapter focuses on synchronous parallelization of big data analytics over a distributed system environment to optimize performance.

A Review: Map Reduce Framework for Cloud Computing

2018 ◽  
Vol 7 (4.6) ◽  
pp. 13
Author(s):  
Mekala Sandhya ◽  
Ashish Ladda ◽  
Dr. Uma N Dulhare ◽  
. . ◽  
. .
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Distributed Computing ◽  
Data Storage ◽  
High Performance ◽  
Large Scale ◽  
Distributed Storage ◽  
Large Data ◽  
Mass Data ◽  
Internet Information

In this generation of Internet, information and data are growing continuously. Even though various Internet services and applications. The amount of information is increasing rapidly. Hundred billions even trillions of web indexes exist. Such large data brings people a mass of information and more difficulty discovering useful knowledge in these huge amounts of data at the same time. Cloud computing can provide infrastructure for large data. Cloud computing has two significant characteristics of distributed computing i.e. scalability, high availability. The scalability can seamlessly extend to large-scale clusters. Availability says that cloud computing can bear node errors. Node failures will not affect the program to run correctly. Cloud computing with data mining does significant data processing through high-performance machine. Mass data storage and distributed computing provide a new method for mass data mining and become an effective solution to the distributed storage and efficient computing in data mining. 

Using Network Analysis and Visualization to Analyze Problematic Enterprise Information Systems

2011 ◽  
Vol 2 (4) ◽  
pp. 54-71 ◽  
Author(s):  
David Greenwood ◽  
Ian Sommerville
Keyword(s):  
Information Systems ◽  
Network Analysis ◽  
Large Scale ◽  
Research Question ◽  
Analytical Techniques ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Complex Information ◽  
Or Groups ◽  
Technical Issues

Society is demanding larger and more complex information systems to support increasingly complex and critical organisational work. Whilst troubleshooting socio-technical issues in small-to-medium scale situations may be achievable using approaches such as ethnography, troubleshooting enterprise scale situations is an open research question because of the overwhelming number of socio-technical elements and interactions involved. This paper demonstrates proof-of-concept tools for network analysis and visualisation that may provide a promising avenue for identifying problematic elements and interactions among an overwhelming number of socio-technical elements. The findings indicate that computers may be used to aid the analysis of problematic large-scale complex socio-technical situations by using analytical techniques to highlighting elements, or groups of interacting elements, that are important to the overall outcome of a problematic situation.

scholarly journals Service Request Scheduling based on Quantification Principle using Conjoint Analysis and Z-score in Cloud

2018 ◽  
Vol 8 (2) ◽  
pp. 1238
Author(s):  
R. Arokia Paul Rajan
Keyword(s):  
Distributed Computing ◽  
Conjoint Analysis ◽  
Distributed System ◽  
Large Scale ◽  
Subjective Assessment ◽  
Considerable Improvement ◽  
Z Score ◽  
Computing Environment ◽  
Service Request ◽  
Cloud Systems

Service request scheduling has a major impact on the performance of the service processing design in a large-scale distributed computing environment like cloud systems. It is desirable to have a service request scheduling principle that evenly distributes the workload among the servers, according to their capacities. The capacities of the servers are termed high or low relative to one another. Therefore, there is a need to quantify the server capacity to overcome this subjective assessment. Subsequently, a method to split and distribute the service requests based on this quantified server capacity is also needed. The novelty of this research paper is to address these requirements by devising a service request scheduling principle for a heterogeneous distributed system using appropriate statistical methods, namely Conjoint analysis and Z-score. Suitable experiments were conducted and the experimental results show considerable improvement in the performance of the designed service request scheduling principle compared to a few other existing principles. Areas of further improvement have also been identified and presented.

Data Mining for High Performance Computing

2015 ◽  
pp. 331-349
Author(s):  
Shen Lu
Keyword(s):  
Data Analysis ◽  
Distributed Computing ◽  
High Performance ◽  
Large Datasets ◽  
Distributed Data ◽  
Future Trends ◽  
Global Synchronization ◽  
Distributed Data Processing ◽  
Topology Changes ◽  
Performance Computing

With the development of information technology, the size of the dataset becomes larger and larger. Distributed data processing can be used to solve the problem of data analysis on large datasets. It partitions the dataset into a large number of subsets and uses different processors to store, manage, broadcast, and synchronize the data analysis. However, distributed computing gives rise to new problems such as the impracticality of global communication, global synchronization, dynamic topology changes of the network, on-the-fly data updates, the needs to share resources with other applications, frequent failures, and recovery of resource. In this chapter, the concepts of distributed computing are introduced, the latest research are presented, the advantage and disadvantage of different technologies and systems are analyzed, and the future trends of the distributed computing are summarized.

Synchronizing Execution of Big Data in Distributed and Parallelized Environments

2013 ◽  
pp. 47-71 ◽  
Author(s):  
Gueyoung Jung ◽  
Tridib Mukherjee
Keyword(s):  
Big Data ◽  
Distributed System ◽  
Data Analytics ◽  
High Performance ◽  
Large Scale ◽  
Big Data Analytics ◽  
Loosely Coupled ◽  
Current Trends ◽  
Distributed Computing Infrastructures ◽  
Performance Computing

In the modern information era, the amount of data has exploded. Current trends further indicate exponential growth of data in the future. This prevalent humungous amount of data—referred to as big data—has given rise to the problem of finding the “needle in the haystack” (i.e., extracting meaningful information from big data). Many researchers and practitioners are focusing on big data analytics to address the problem. One of the major issues in this regard is the computation requirement of big data analytics. In recent years, the proliferation of many loosely coupled distributed computing infrastructures (e.g., modern public, private, and hybrid clouds, high performance computing clusters, and grids) have enabled high computing capability to be offered for large-scale computation. This has allowed the execution of the big data analytics to gather pace in recent years across organizations and enterprises. However, even with the high computing capability, it is a big challenge to efficiently extract valuable information from vast astronomical data. Hence, we require unforeseen scalability of performance to deal with the execution of big data analytics. A big question in this regard is how to maximally leverage the high computing capabilities from the aforementioned loosely coupled distributed infrastructure to ensure fast and accurate execution of big data analytics. In this regard, this chapter focuses on synchronous parallelization of big data analytics over a distributed system environment to optimize performance.

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