Phd thesis grid computing
Deposited on Mar in Research - Coventry. Last modified on Mar Visit the Curve home page to find out more or /write-an-essay-about-my-dream-school.html the collections.
Research - Coventry Version: The fact that sequential algorithms do not scale with multicore systems phd thesis grid computing benefit from parallelism remains /a-separate-peace-notes.html major challenge to scheduling in the Grid. As multicore systems become phd thesis grid more pervasive in our computing lives, phd thesis grid computing reliance on such systems for passive parallelism does not offer the best option in harnessing the benefits of their multiprocessors for Grid scheduling.
An explicit means of grid computing parallelism for Grid scheduling is required. The Group-based Parallel Multi-scheduler for Grid introduced in this work is aimed at effectively exploiting the benefits of grid computing systems for Grid job scheduling by splitting jobs and machines into paired groups and independently multi-scheduling jobs in parallel from the groups.
The Priority method splits jobs into four phd thesis grid computing groups based /decorative-lined-paper-to-print.html job attributes and uses two methods SimTog and EvenDist methods to source machines.
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Then the scheduling is carried out using the MinMin algorithm within the /contents-page-dissertation-year.html group pairs. The Priority method was implemented and compared with the MinMin scheduling algorithm phd thesis grid computing grouping named ordinary MinMin in this research.
The analysis of results compared against the ordinary MinMin shows substantial improvement in speedup and gains in scheduling efficiency. The two methods used the same dating paper online research grouping methods as used with the Priority method, but were able to vary the number of groups and equally exploited different means of grouping jobs to ensure equitability grid computing jobs in phd thesis grid computing.
PhD thesis abstract - Dietrich, Carl
Computing MinMin Grid scheduling algorithm was then executed independently within the discrete group pairs. The conclusion is reached computing grouping jobs and machines before scheduling improves the phd thesis grid computing efficiency significantly.
Computational grids Computer systemsParallel scheduling Computer scheduling. Some materials have been removed phd thesis grid computing thesis this thesis due to computing party. Pages where material has been removed are clearly marked in the electronic version. The unabridged version of the thesis can be viewed at the Lanchester Library, Coventry University.
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