Distributed Environment Modeling using Path Compression Algorithm

Authors

  • Çiğdem BAKIR YILDIZ TEKNİK ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜ, BİLGİSAYAR MÜHENDİSLİĞİ ANABİLİM DALI 0000-0001-8482-2412
  • Veli HAKKOYMAZ YILDIZ TEKNİK ÜNİVERSİTESİ, ELEKTRİK-ELEKTRONİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ 0000-0002-3245-4440

DOI:

https://doi.org/10.18100/ijamec.797074

Keywords:

distribıted environment, object access, object move, path compression

Abstract

In distributed environment, some operations related to objects are performed. For example, objects can be accessed or they can be moved. In our study, events related to objects (object-access, object-move) were created as independent events. In this study, the distributed environment simulation was performed and the effectiveness and success of the path compression algorithm, which we proposed as a result of the experimental study, was demonstrated. The purpose of this study is to show the effectiveness and benefits of the path compression algorithm. Path compression algorithm is an efficient algorithm whose runtime is linear. With the path compression, the long node chain that is formed while data objects are passing between the source node and the destination is broken, so that the objects are retrieved fast and the cost of access is reduced. This result is shown with experimental study by modeling the distributed environment. It is shown comparative the results of the distributed environment simulation according to the various Access/Move (%) rates using binomial distribution. When we use the path compression, the maximum length and mean length of the chain decreases. Thus, with the path compression algorithm, the long node chain created by the objects is broken, the cost of accessing the objects is reduced, and fast access to the objects is ensured. In short, with our study, fast access to data is ensured in a distributed environment.

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Published

31-12-2020

Issue

Section

Research Articles

How to Cite

[1]
“Distributed Environment Modeling using Path Compression Algorithm”, J. Appl. Methods Electron. Comput., vol. 8, no. 4, pp. 226–231, Dec. 2020, doi: 10.18100/ijamec.797074.

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