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Chanintorn Jittawiriyanukoon
Graduate School of Advanced Technology Management, Assumption University.

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Proposed agorithm for regression-based prediction with bulk noise Chanintorn Jittawiriyanukoon
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 1: January 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i1.pp543-550

Abstract

The noise has incited an original data due to a network with an inferior SNR. In case of the bulk noise, the insightful content within the data is substantially squeezed.  A cost-effective method will challenge to quarantine the insights, so that information can be utilized more resourcefully.  To achieve this aim, it is essential to iron the bulk noise content out, and then calculate the analytics of the clean data. As noise is bulk so some statistical methodologies such as averaging or randomizing are employed. A prediction using the regression-based model with bulk noise for the experiment in practice is introduced. The decomposition approach to separate the insights is exploited. The proposed algorithm achieves a (local) solution at each computing step and selects the best solution in view of global impacts. The correlation coefficient, average error, absolute error and mean squared error are used to constitute the prediction. Results from MOA simulation will be compared to actual data in the succeeding time. The prediction with bulk noise using the proposed algorithm outperforms.
Cloud computing based load balancing algorithm for erlang concurrent traffic Chanintorn Jittawiriyanukoon
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 2: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i2.pp1109-1116

Abstract

The distribution of scheduler from user inquiries in the clouds is complex. In keeping up with the cloud computing environment and the inquirers, the clouds meet with some problematic load balancing complications as an improving load balancing tool induces the rigorous efficiency of the cloud based website’s user access. Overloaded or underloaded conditions originate processing catastrophe regarding the prolonged execution time, bandwidth hog, malfunction, and etc. Besides, to manipulate Erlang concurrent tasks is another skyward situation. Hence, the load balancing is obliged to exhaust all mentioned conditions. The proposed load balancing algorithm for Erlang concurrent tasks (those are and could also be autonomous and unstable.) on VMware workstations is introduced.  There are several load patterns within the clouds corresponding to CPU’s load (utilization), memory load (queue size), link capacity load (bandwidth), and so on. The proposed load balancing is to spot underloaded and overloaded conditions then stabilizes the weight amidst computing nodes. There are countless load balancing approaches in the cloud environment to examine performance parameters. A short outline of corresponding performance metrics in the review and their findings are presented. To investigate the fit efficiency of the proposed algorithm, the simulation is applied then results based on the proposed method are compared to the existing ones. The outcomes settle the weight balancing, outperform others when executing Erlang traffic, and are catered in the context.
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