SCore: an integrated cluster system software package for high performance cluster computing

2001 ◽  
Author(s):  
A. Hori
Keyword(s):  
Software Package ◽  
High Performance ◽  
Cluster Computing ◽  
Cluster System ◽  
System Software

scholarly journals High Availability Server Using Raspberry Pi 4 Cluster and Docker Swarm

2021 ◽  
Vol 6 (1) ◽  
pp. 43-51
Author(s):  
T Yudi Hadiwandra ◽  
Feri Candra
Keyword(s):  
High Performance ◽  
Cluster Computing ◽  
Low Cost ◽  
Web Server ◽  
Performance Testing ◽  
High Availability ◽  
Cluster System ◽  
Raspberry Pi ◽  
Server Infrastructure ◽  
Server System

In the Industrial 4.0 era, almost all activities and transactions are carried out via the internet, which basically uses web technology. For this reason, it is absolutely necessary to have a high-performance web server infrastructure capable of serving all the activities and transactions required by users without any constraints. This research aims to design a high-performance (high availability) web server infrastructure with low cost (low cost) and energy efficiency. low power) using Cluster Computing technology on the Raspberry Pi Single Board Computing and Docker Container technology. The cluster system is built using five raspberry Pi type 4B modules as cluster nodes, and the Web server system is built using docker container virtualization technology. Meanwhile, cluster management uses Docker Swarm technology. Performance testing (Quality of Service) of the cluster system is done by simulating a number of loads (requests) and measuring the response of the system based on the parameters of Throughput and Delay (latency). The test results show that the Raspberry Pi Cluster system using Docker Swarm can be used to build a High Availability Server system that is able to handle very high requests that reach Throughput = 161,812,298 requests / sec with an Error rate = 0%.

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.

scholarly journals Improved Model of Scheduling Algorithm

2018 ◽  
pp. 104-106
Author(s):  
Artur Vardanyan
Keyword(s):  
High Performance ◽  
Cluster Computing ◽  
Scheduling Algorithm ◽  
Great Influence ◽  
Cluster Management ◽  
Task Scheduling Algorithm ◽  
Long Time ◽  
Improved Model ◽  
First In First Out

Cluster computing is becoming increasingly practical for high performance computing research and development. A computer cluster is a set of connected computers that work together so that, they can be viewed as a single system. Clusters offer a scalable means of linking computers together to provide an expansive environment for hosting enterprise applications. As the number of nodes in cluster configurations grows, the cluster administration becomes more challenging. We need to study the challenges of cluster management and to provide a solution. To have an effective cluster management we need to have an effective task scheduling algorithm. With the explosive growth of information, the demand on computing is sharply increasing. Due to a large number of computing tasks, the scheduling algorithm is an important part of cluster computing and has a great influence on the quality of claster service. In cluster computing, some large tasks may occupy too many resources and some small tasks may wait for a long time based on First-In-First-Out (FIFO) scheduling algorithm. This paper provides an overview of an improved scheduling algorithm that shortens the execution time of tasks and increases the resource utilization.

The Development of a Skinnerian Based Expert System for Use in a Computer Managed Instructional Feedback System

1997 ◽  
Vol 25 (3) ◽  
pp. 293-299
Author(s):  
Steven R. Terrell
Keyword(s):  
Expert System ◽  
Software Package ◽  
Qualitative Data ◽  
Instructional System ◽  
Feedback System ◽  
Instructional Software ◽  
System Software ◽  
Weekly Basis ◽  
Instructional Feedback ◽  
Specific Feedback

This article describes efforts to include an expert system software package, based on the theories of B. F. Skinner, into a computer managed instructional system. The purpose of the system is to provide meaningful feedback to students concerning their achievement status on a weekly basis. The process for the development of the system is discussed as well as results in light of the collection and analysis of both quantitative and qualitative data. Conclusions are drawn that suggest that behaviorist techniques can be incorporated into instructional software at a level higher than the traditional task specific feedback.

An Efficient Live Process Migration Approach for High Performance Cluster Computing Systems

2011 ◽  
pp. 362-373 ◽  
Author(s):  
Ehsan Mousavi Khaneghah ◽  
Najmeh Osouli Nezhad ◽  
Seyedeh Leili Mirtaheri ◽  
Mohsen Sharifi ◽  
Ashakan Shirpour
Keyword(s):  
High Performance ◽  
Cluster Computing ◽  
Process Migration ◽  
Computing Systems
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