Abstract

In article by Mahmood [1], the results for a genetic algorithm (GA), adaptive genetic algorithm (AGA), and greedy algorithm were not correctly reported in Section 5 due to a programming error[...]

Highlights

  • Computer Science Department, University of Bahrain, P.O

  • The authors have repeated the experiments with extensive simulations and analysis using the help of an assistant

  • Since the core focus of the paper is on the performance of an adaptive genetic algorithm and greedy algorithm, the central claims and conclusions have not been affected, but the tables and figures have been updated with correct values, and necessary changes to the text have been made, as explained below

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Summary

Scheduling in Cloud Computing Using an Adaptive

Computer Engineering Department, University of Bahrain, P.O. Box 32038, Sakhir, Bahrain. In article by Mahmood [1], the results for a genetic algorithm (GA), adaptive genetic algorithm (AGA), and greedy algorithm were not correctly reported in Section 5 due to a programming error. The authors have repeated the experiments with extensive simulations and analysis using the help of an assistant Bahlool), who has been added as an author. Since the core focus of the paper is on the performance of an adaptive genetic algorithm and greedy algorithm, the central claims and conclusions have not been affected, but the tables and figures have been updated with correct values, and necessary changes to the text have been made, as explained below. Results for 150 and 250 tasks have been added for a better understanding of the trends for each combination

Average Cost
Number of tasks
Number of Tasks
Run time comparison
In last paragraph of AGA
DAG with
Allperformed authors
Findings
Cloud Computing
Full Text
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