Single System Image with Virtualization Technology for Cluster Computing Environment

2008 ◽  
Author(s):  
Liang Yong
Keyword(s):  
Cluster Computing ◽  
Single System ◽  
Computing Environment ◽  
Single System Image ◽  
Virtualization Technology

Grids

2011 ◽  
pp. 247-308
Author(s):  
Jana Polgar ◽  
Robert Mark Braum ◽  
Tony Polgar
Keyword(s):  
Operating System ◽  
Cluster Computing ◽  
Single System ◽  
Grid System ◽  
Single System Image ◽  
One Machine ◽  
Multiple Machines

This section describes some of the more important goals of a Grid system. The Grid carries with it many ideas central to other sharing technologies, such as the network operating system (NOS), and cluster computing. A common aim among these technologies is to maintain a single system image. Such a system would run across multiple machines, allowing its users to reasonably think they were using only one machine, when in fact they are accessing resources on any of the connected machines (Biswas, Lazar, Huard, Lim, Mahjoub, Pau, et al., 1998).

On Construction of a Diskless Cluster Computing Environment in a Computer Classroom

2012 ◽  
Vol 4 (4) ◽  
pp. 68-88
Author(s):  
Chao-Tung Yang ◽  
Wen-Feng Hsieh
Keyword(s):  
High Performance ◽  
Cluster Computing ◽  
Relevant Information ◽  
Computing Environment ◽  
Cluster Architecture ◽  
Computer Classroom ◽  
Computation Node ◽  
Cluster Environment ◽  
Computing Performance ◽  
Performance Computing

This paper’s objective is to implement and evaluate a high-performance computing environment by clustering idle PCs (personal computers) with diskless slave nodes on campuses to obtain the effectiveness of the largest computer potency. Two sets of Cluster platforms, BCCD and DRBL, are used to compare computing performance. It’s to prove that DRBL has better performance than BCCD in this experiment. Originally, DRBL was created to facilitate instructions for a Free Software Teaching platform. In order to achieve the purpose, DRBL is applied to the computer classroom with 32 PCs so to enable PCs to be switched manually or automatically among different OS (operating systems). The bioinformatics program, mpiBLAST, is executed smoothly in the Cluster architecture as well. From management’s view, the state of each Computation Node in Clusters is monitored by “Ganglia”, an existing Open Source. The authors gather the relevant information of CPU, Memory, and Network Load for each Computation Node in every network section. Through comparing aspects of performance, including performance of Swap and different network environment, they attempted to find out the best Cluster environment in a computer classroom at the school. Finally, HPL of HPCC is used to demonstrate cluster performance.

Research on Data Mining Optimization and Security Based on MapReduce

2014 ◽  
Vol 631-632 ◽  
pp. 1053-1056
Author(s):  
Hui Xia
Keyword(s):  
Data Mining ◽  
Execution Time ◽  
Cluster Computing ◽  
Limited Resource ◽  
Experimental Results ◽  
Computing Environment ◽  
Cluster Systems ◽  
National Education ◽  
Distributed Cluster ◽  
Data Optimization

The paper addressed the issues of limited resource for data optimization for efficiency, reliability, scalability and security of data in distributed, cluster systems with huge datasets. The study’s experimental results predicted that the MapReduce tool developed improved data optimization. The system exhibits undesired speedup with smaller datasets, but reasonable speedup is achieved with a larger enough datasets that complements the number of computing nodes reducing the execution time by 30% as compared to normal data mining and processing. The MapReduce tool is able to handle data growth trendily, especially with larger number of computing nodes. Scaleup gracefully grows as data and number of computing nodes increases. Security of data is guaranteed at all computing nodes since data is replicated at various nodes on the cluster system hence reliable. Our implementation of the MapReduce runs on distributed cluster computing environment of a national education web portal and is highly scalable.

Single System Image

2011 ◽  
pp. 1820-1827
Author(s):  
Jack Dongarra ◽  
Piotr Luszczek ◽  
Felix Wolf ◽  
Jesper Larsson Träff ◽  
Patrice Quinton ◽  
...  
Keyword(s):  
Single System ◽  
Single System Image

Research on Security and Defense Technologies in the Cloud Computing

2013 ◽  
Vol 347-350 ◽  
pp. 3312-3316
Author(s):  
Hong Jun Chen ◽  
Xue Qin Wu
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Service Providers ◽  
Cloud Platform ◽  
Computing Environment ◽  
Security Risks ◽  
Cloud Computing Environment ◽  
Virtualization Technology ◽  
Legal Constraints ◽  
Secure Cloud Computing

The positive factors of the cloud computing: the great commercial value, the cheap and convenient use, has been recognized widely. But there are many risks on security. These security risks will greatly restrict the development of cloud computing. Cloud security risks such as: the cloud platform is easily vulnerable, cloud resources is easily abused, the cloud platform data is accessed by unauthorized user or the data is missing, the violations in the network is difficult to trace and survey, and the service providers replacement will affect the migration of the users data and services. So we research the virtualization technology, data backup and disaster recovery technology, encryption technology of data storage and transmission, the authentication technology of service providers and users, risk assessment, and legal constraints and safety training. Our research is strived to obtain a secure cloud computing environment.

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