DPPACS: A Novel Data Partitioning and Placement Aware Computation Scheduling Scheme for Data-Intensive Cloud Applications

2015 ◽  
pp. bxv062 ◽  
Author(s):  
K. Hemant Kumar Reddy ◽  
Diptendu Sinha Roy
Keyword(s):  
Data Partitioning ◽  
Data Intensive ◽  
Scheduling Scheme ◽  
Cloud Applications

scholarly journals MiCADO – Towards a microservice-based cloud application-level dynamic orchestrator

2016 ◽  
Author(s):  
Hannu Visti ◽  
Tamas Kiss ◽  
Gabor Terstyanszky ◽  
Gregoire Gesmier ◽  
Stephen Winter
Keyword(s):  
Dynamic Capabilities ◽  
Web Applications ◽  
End User ◽  
Data Intensive ◽  
Generic Framework ◽  
Cloud Application ◽  
Technical Details ◽  
Commercial Applications ◽  
Application Developers ◽  
Cloud Applications

In order to satisfy end-user requirements, many scientific and commercial applications require access to dynamically adjustable infrastructure resources. Cloud computing has the potential to provide these dynamic capabilities. However, utilising these capabilities from application code is not trivial and requires application developers to understand low-level technical details of clouds. This paper investigates how a generic framework can be developed that supports the dynamic orchestration of cloud applications both at deployment and at run-time. The advantages and challenges of designing such framework based on microservices is analysed, and a generic framework, called MiCADO – (Microservices-based Cloud Application-level Dynamic Orchestrator) is proposed. A first prototype implementation of MiCADO to support data intensive commercial web applications is also presented.

A Framework for Data Partitioning for C++ Data-Intensive Applications

2004 ◽  
Vol 9 (2) ◽  
pp. 101-121
Author(s):  
A. Milidonis ◽  
G. Dimitroulakos ◽  
M. D. Galanis ◽  
A. P. Kakarountas ◽  
G. Theodoridis ◽  
...  
Keyword(s):  
Data Partitioning ◽  
Data Intensive ◽  
Data Intensive Applications

scholarly journals MiCADO – Towards a microservice-based cloud application-level dynamic orchestrator

2016 ◽  
Author(s):  
Hannu Visti ◽  
Tamas Kiss ◽  
Gabor Terstyanszky ◽  
Gregoire Gesmier ◽  
Stephen Winter
Keyword(s):  
Dynamic Capabilities ◽  
Web Applications ◽  
End User ◽  
Data Intensive ◽  
Generic Framework ◽  
Cloud Application ◽  
Technical Details ◽  
Commercial Applications ◽  
Application Developers ◽  
Cloud Applications

In order to satisfy end-user requirements, many scientific and commercial applications require access to dynamically adjustable infrastructure resources. Cloud computing has the potential to provide these dynamic capabilities. However, utilising these capabilities from application code is not trivial and requires application developers to understand low-level technical details of clouds. This paper investigates how a generic framework can be developed that supports the dynamic orchestration of cloud applications both at deployment and at run-time. The advantages and challenges of designing such framework based on microservices is analysed, and a generic framework, called MiCADO – (Microservices-based Cloud Application-level Dynamic Orchestrator) is proposed. A first prototype implementation of MiCADO to support data intensive commercial web applications is also presented.

Data Intensive Cloud Computing

Big Data ◽  
2016 ◽  
pp. 639-654
Author(s):  
Jayalakshmi D. S. ◽  
R. Srinivasan ◽  
K. G. Srinivasa
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Cluster Computing ◽  
Resource Provisioning ◽  
Data Intensive ◽  
Scientific Value ◽  
Data Intensive Applications ◽  
Cloud Applications ◽  
Problem Data ◽  
Huge Challenge

Processing Big Data is a huge challenge for today's technology. There is a need to find, apply and analyze new ways of computing to make use of the Big Data so as to derive business and scientific value from it. Cloud computing with its promise of seemingly infinite computing resources is seen as the solution to this problem. Data Intensive computing on cloud builds upon the already mature parallel and distributed computing technologies such HPC, grid and cluster computing. However, handling Big Data in the cloud presents its own challenges. In this chapter, we analyze issues specific to data intensive cloud computing and provides a study on available solutions in programming models, data distribution and replication, resource provisioning and scheduling with reference to data intensive applications in cloud. Future directions for further research enabling data intensive cloud applications in cloud environment are identified.

Data Intensive Cloud Computing

2015 ◽  
pp. 305-320
Author(s):  
Jayalakshmi D. S. ◽  
R. Srinivasan ◽  
K. G. Srinivasa
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Cluster Computing ◽  
Resource Provisioning ◽  
Data Intensive ◽  
Scientific Value ◽  
Data Intensive Applications ◽  
Cloud Applications ◽  
Problem Data ◽  
Huge Challenge

Processing Big Data is a huge challenge for today's technology. There is a need to find, apply and analyze new ways of computing to make use of the Big Data so as to derive business and scientific value from it. Cloud computing with its promise of seemingly infinite computing resources is seen as the solution to this problem. Data Intensive computing on cloud builds upon the already mature parallel and distributed computing technologies such HPC, grid and cluster computing. However, handling Big Data in the cloud presents its own challenges. In this chapter, we analyze issues specific to data intensive cloud computing and provides a study on available solutions in programming models, data distribution and replication, resource provisioning and scheduling with reference to data intensive applications in cloud. Future directions for further research enabling data intensive cloud applications in cloud environment are identified.

Data Partitioning for Highly Scalable Cloud Applications

2012 ◽  
pp. 236-253 ◽  
Author(s):  
Robert Neumann ◽  
Matthias Baumann ◽  
Reiner Dumke ◽  
Andreas Schmietendorf
Keyword(s):  
Cloud Computing ◽  
Data Management ◽  
High Throughput ◽  
Real World ◽  
Data Partitioning ◽  
Management Architecture ◽  
The World ◽  
Data Volume ◽  
New Challenges ◽  
Cloud Applications

Cloud computing has brought new challenges, but also exciting chances to developers. With the illusion of an infinite expanse of computing resources, even individual developers have been put into a position from which they can create applications that scale out all over the world, thus affecting millions of people. One difficulty with developing such mega-scale Cloud applications is to keep the storage backend scalable. In this chapter, we detail ways of partitioning non-relational data among thousands of physical storage nodes, thereby emphasizing the peculiarities of tabular Cloud storage. The authors give recommendations of how to establish a sustainable and scaling data management architecture that – while growing in terms of data volume – still provides the same high throughput. Finally, they underline their theoretical elaborations by featuring insights won from a real-world cloud project with which the authors have been involved.

An Automated C++ Code and Data Partitioning Framework for Data Management of Data-Intensive Applications

2004 ◽  
pp. 122-136
Author(s):  
Athanasios Milidonis ◽  
Grigoris Dimitroulakos ◽  
Michalis D. Galanis ◽  
George Theodoridis ◽  
Costas Goutis ◽  
...  
Keyword(s):  
Data Management ◽  
Data Partitioning ◽  
Data Intensive ◽  
Data Intensive Applications
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