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Distributed Processing System, 2022, 3(1); doi: 10.38007/DPS.2022.030107.

Distributed Computing Test Model and Framework Based on Mapreduce

Author(s)

Aditinya Kumarun

Corresponding Author:
Aditinya Kumarun
Affiliation(s)

Amman Arab University, Jordan

Abstract

With the rapid development of network and related technologies and the continuous expansion of application fields, distributed systems have become the main choice for building network applications. This paper aims at the research and application of the distributed computing test model and framework based on MapReduce. This paper firstly proposes a performance testing method based on decision tree in cloud environment. The method is based on the decision tree model, and uses the test resources in the cloud environment to describe the test case generation, test script execution and performance bottleneck location in the performance test process in detail; by dividing and selecting the test case set, it can effectively reduce number of performance tests. Secondly, in view of the insufficient scalability of the performance test loop under the traditional test platform, this paper builds a cloud computing-based distribution system based on the open source cloud computing platform CloudStack, the performance testing tool LoadRunner, the Web lightweight development framework SSH, and the JavaScript library JQuery. The prototype system of the system performance test platform realizes the automatic configuration and dynamic expansion of the performance test environment in the cloud environment. The test platform mainly includes functions such as test task submission, virtual machine image matching, test environment automatic configuration, test task distribution and scheduling, cloud platform resource scheduling, and test result return. Experiments show that the algorithm in this paper effectively reduces the execution time by about 20%, and improves the system resource utilization by about 20%.

Keywords

Mapreduce Algorithm, Distributed Computing, Test Model, Test Framework

Cite This Paper

Aditinya Kumarun. Distributed Computing Test Model and Framework Based on Mapreduce. Distributed Processing System (2022), Vol. 3, Issue 1: 54-61. https://doi.org/10.38007/DPS.2022.030107.

References

[1] Ketu S, Mishra P K, Agarwal S. Performance Analysis of Distributed Computing Frameworks for Big Data Analytics: Hadoop Vs Spark. Computacion y Sistemas, 2020, 24(2):669–686. https://doi.org/10.13053/cys-24-2-3401

[2] Li F, Chen J, Wang Z. Wireless MapReduce Distributed Computing. IEEE Transactions on Information Theory, 2019, 65(10):610 https://doi.org/10.1109/TIT.2019.2924621

[3] Yan Q, Wigger M, Yang S, et al. A Fundamental Storage-Communication Tradeoff for Distributed Computing With Straggling Nodes. IEEE Transactions on Communications, 2020, PP(99):1-1. https://doi.org/10.1109/ISIT.2019.8849615

[4] Queiroz W, Capretz M, Dantas M. A MapReduce Approach for Traffic Matrix Estimation in SDN. IEEE Access, 2020, PP(99):1-1.

[5] Kavitha C, Lakshmi R S, Devi J A, et al. Evaluation of worker quality in crowdsourcing system on Hadoop platform. International Journal of Reasoning-based Intelligent Systems, 2019, 11(2):181. https://doi.org/10.1504/IJRIS.2019.099856

[6] Meraou M A, Al-Kandari N M, Raqab M Z. Univariate and Bivariate Compound Models Based on Random Sum of Variates with Application to the Insurance Losses Data. Journal of Statistical Theory and Practice, 2022, 16(4):1-30.

[7] Zhou L, Gai X, Lu Y, et al. Research and Application of Intelligent Learning System for Power Grid All-Element Simulation Based on Microservice. Journal of Physics: Conference Series, 2021, 1802(4):042103 (8pp).

[8] Ha H, Kwak Y. Prediction model for discomfort luminance levels of head‐mounted displays. Color Research And Application, 2022, 47(4):1035-1041. https://doi.org/10.1002/col.22783

[9] Bartling M, Resch B, Reichenbacher T, et al. Adapting mobile map application designs to map use context: a review and call for action on potential future research themes. Cartography and Geographic Information Science, 2022, 49(3):237-251.

[10] Oh S, Kwak Y. A hue and warm‐cool model for warm‐cool based correlated color temperature calculation. Color Research And Application, 2022, 47(4):953-965. https://doi.org/10.1002/col.22764

[11] Veluchamy M, Subramani B. Cuckoo search optimization‐based image color and detail enhancement for contrast distorted images. Color Research And Application, 2022, 47(4):1005-1022.

[12] Oscar Sánchez, Min A, Mendonca A, et al. Development and application of novel BiFC probes for cell sorting based on epigenetic modification. Cytometry Part A, 2022, 101(4):339-350.

[13] Torii R, Yacoub M. CT-based fractional flow reserve: development and expanded application.. Global cardiology science & practice, 2021, 2021(3):e202120.

[14] Zjavka L. Power quality statistical predictions based on differential, deep and probabilistic learning using off‐grid and meteo data in 24‐hour horizon. International Journal of Energy Research, 2022, 46(8):10182-10196. https://doi.org/10.1002/er.7431

[15] Leibowitz S G, Pennino M J, Beyene M T. Parsing Weather Variability and Wildfire Effects on the Post‐Fire Changes in Daily Stream Flows: A Quantile‐Based Statistical Approach and Its Application. Water R

[16] Holsteen K, Hittle M, Barad M, et al. Development and Internal Validation of a Multivariable Prediction Model for Individual Episodic Migraine Attacks Based on Daily Trigger Exposures.. References, 2020, 60(10):2364-2379.

[17] Dutta A K, Mandal J J, Bandyopadhyay D. Application of Quintic Displacement Function in Static Analysis of Deep Beams on Elastic Foundation. Architecture, Structures and Construction, 2022, 2(2):257-267.

[18] Fernandes C I, Veiga P M, Adro F. The impact of innovation management on the performance of NPOs: Applying the Tidd and Bessant model (2009). Nonprofit Management and Leadership, 2022, 32(4):577-601. https://doi.org/10.1002/nml.21501

[19] Billger M, Amborg E, Zboinska M A, et al. Colored skins and vibrant hybrids: Manipulating visual perceptions of depth and form in double‐curved architectural surfaces through informed use of color, transparency and light. Color Research And Application, 2022, 47(4):1042-1064. https://doi.org/10.1002/col.22784