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All Journal International Journal of Electrical and Computer Engineering
Ammar Kamal Abasi
Universiti Sains Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Earlier stage for straggler detection and handling using combined CPU test and LATE methodology Anwar H. Katrawi; Rosni Abdullah; Mohammed Anbar; Ammar Kamal Abasi
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (650.355 KB) | DOI: 10.11591/ijece.v10i5.pp4910-4917

Abstract

Using MapReduce in Hadoop helps in lowering the execution time and power consumption for large scale data. However, there can be a delay in job processing in circumstances where tasks are assigned to bad or congested machines called "straggler tasks"; which increases the time, power consumptions and therefore increasing the costs and leading to a poor performance of computing systems. This research proposes a hybrid MapReduce framework referred to as the combinatory late-machine (CLM) framework. Implementation of this framework will facilitate early and timely detection and identification of stragglers thereby facilitating prompt appropriate and effective actions.
Text documents clustering using modified multi-verse optimizer Ammar Kamal Abasi; Ahamad Tajudin Khader; Mohammed Azmi Al-Betar; Syibrah Naim; Mohammed A. Awadallah; Osama Ahmad Alomari
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v10i6.pp6361-6369

Abstract

In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems. For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the final results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce significant results in comparison with three well-established methods.
Co-Authors Ahamad Tajudin Khader Anwar H. Katrawi Mohammed A. Awadallah Mohammed Anbar Mohammed Azmi Al-Betar Osama Ahmad Alomari Rosni Abdullah Syibrah Naim