CluMSy: Cluster management system

Date of Publication

2004

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Computer Science

Subject Categories

Computer Sciences

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Elaine Jane Chua

Defense Panel Member

Elaine Jane Chua

Caslon Chua

Rhia Trogo

Abstract/Summary

Today, supercomputers are generally being replaced by network of standalone computing nodes that works together to perform specific tasks due to the large gap between their cost-benefit ratio. The capabilities of a cluster of workstations (COWs) over a single computing node make it possible to achieve high-performance parallel computing. Cluster Management Systems (CMS) aid users in monitoring and managing cluster of workstations. These systems keep track of the resources, manage the processes and monitor the utilization of each node in the cluster. Though, these cluster management systems requires users to adapt to an unfamiliar environment and majority of the cluster management systems at present deal solely with monitoring and limited process management. To address this issue, the proponents implemented a system called Cluster Management System (CluMSy) that provides both monitoring and management of files, processes, accounts and packages but are cluster-enabled utilities based on the classical UNIX commands that survived amidst the GUI revolution. The Cluster Connection Manager (CCM) provides a simple parallel programming environment that handles the parallel execution of these classical commands. Aside from monitoring and management functions, these cluster-enabled utilities also assist users in preparation and termination of parallel program executions.

Abstract Format

html

Note

With: Technical manual, programmer's guide, user's manual.

Language

English

Format

Print

Accession Number

TU13686

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

1 v. (various leaves) : ill. (some col.) ; 28 cm.

Keywords

Cluster analysis--Computer programs

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