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3. Task Scheduling in Cloud Environment–Techniques, Applications, and Tools: A Systematic Literature Review

Author

Olanrewaju L. Abraham, Md Asri Bin Ngadi, Johan Bin Mohamad Sharif, and Mohd Kufaisal Mohd Sidik

Subjects
Cloud computing, task scheduling, heuristic technique, meta-heuristic technique, hybrid technique, makespan, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Cloud computing has become a revolutionary model for providing computational resources and services via the internet. As the volume of tasks and the dynamic nature of cloud resources increase, several critical challenges emerge, including load balancing, resource utilization, task allocation, and system performance. Ineffective scheduling leads to issues such as resource imbalance (either overuse or underuse) resulting in service degradation or resource wastage. The primary goal of this study is to review and analyze the challenges related to task allocation among limited cloud resources, focusing on factors like resource utilization, reliability, makespan time, cost, energy consumption, availability, response time, and other key performance metrics. The paper offers a systematic literature review of task scheduling in cloud computing, introducing a novel classification taxonomy and a comparative review of various techniques. This taxonomy categorizes metaheuristic scheduling techniques based on scheduling algorithms, problem nature, task types, primary scheduling objectives, task-resource mapping, scheduling constraints, and testing environments. This study provides a thorough assessment, classification, and analysis of different scheduling systems, discussing their advantages and limitations. It also outlines future research directions to support current researchers and practitioners.

Published
2024
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4. Improving Wheat Leaf Disease Classification: Evaluating Augmentation Strategies and CNN-Based Models With Limited Dataset

Author

Syed Taha Yeasin Ramadan, Tanjim Sakib, Fahmid Al Farid, Md. Shofiqul Islam, Junaidi Bin Abdullah, Md Roman Bhuiyan, Sarina Mansor, and Hezerul Bin Abdul Karim

Subjects
Wheat leaf disease, deep learning, augmentation techniques, CycleGAN, SMOTE, SMOTETomek, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Global food security is seriously threatened by wheat leaf disease, which makes effective and precise disease detection and classification techniques necessary. For efficient disease control and the best possible crop health, timely identification and precise classification are essential. However, the limited availability of datasets for wheat leaf diseases hinders the development of effective and robust classification models. This research emphasizes the importance of precise wheat leaf disease diagnosis for global food security. The existing methods face challenges with limited data and computational demands. The research explores the potential of deep learning for automated disease detection, considering these challenges. CycleGAN proved to be the most effective among various augmentation techniques, enhancing the performance of classifiers DenseNet121, ResNet50V2, DenseNet169, Xception, ResNet152V2, and MobileNetV2. ADASYN also significantly improved classification accuracy, with MobileNetV2 consistently outperforming across different augmentation methods. This technique excels in overcoming challenges posed by limited datasets and class imbalances. Using CycleGAN for data augmentation notably enhanced classifier performance, addressing the scarcity of real-world samples. Evaluation through confusion matrix analysis revealed a minimal number of misclassified images—possibly as low as 0 to 3 images over the test dataset. The exceptional 100% accuracy achieved by the MobileNetV2 model on both CycleGAN and ADASYN augmented datasets highlights the potential of these techniques to unlock new levels of accuracy in wheat disease classification. This augmentation technique fine-tuned the classifier, reducing errors and highlighting the crucial role of CycleGAN in enhancing the accuracy and precision of wheat disease classification models. The proposed method establishes CycleGAN’s effectiveness in augmenting wheat leaf disease classification and recognizes ADASYN’s potential. The developed technique shows promise for automated disease detection in agriculture, enhancing global food security. Future research may optimize computational efficiency and explore integrating emerging technologies such as edge computing.

Published
2024
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