Article
Computer Science, Information Systems
Bara Abusalah, Derek Schatzlein, Julian James Stephen, Masoud Saeida Ardekani, Patrick Eugster
Summary: This article proposes the paradigm of dependable resources, which provides generic fault tolerance mechanisms by offering fault tolerance support at the level of resource management systems. Through the demonstration of Guardian, the benefits of this concept are shown, improving completion time for big data processing frameworks in the presence of failures while maintaining low overhead.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Mazen Farid, Rohaya Latip, Masnida Hussin, Nor Asilah Wati Abdul Hamid
Summary: The FITSW algorithm proposes a method to increase the reliability of workflows by duplicating sub-tasks, using an intermediate data decision-making mechanism, and deadline partitioning method to achieve dynamic task scheduling and improve efficiency. Experimental results show that FITSW not only improves success rate and task completion rate, but also reduces completion time compared to systems with intrusion tolerant features.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Information Systems
Muhammad Mudassar, Yanlong Zhai, Liao Lejian
Summary: Edge computing is a technology that pushes cloud computing capabilities to the edge of the network, improving the service quality for latency-oriented IoT applications. This article proposes a fault-tolerance methodology based on checkpointing and replication for edge computing, effectively increasing system reliability and task availability.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Chemistry, Analytical
Abid Ali, Muhammad Munawar Iqbal, Harun Jamil, Faiza Qayyum, Sohail Jabbar, Omar Cheikhrouhou, Mohammed Baz, Faisal Jamil
Summary: This paper discusses the issues related to energy optimization and time management on mobile devices, proposing a novel task scheduling algorithm that quickly adapts to cloud computing tasks and energy and time computation on mobile devices through an energy-efficient dynamic decision-based method.
Review
Computer Science, Information Systems
Mukosi Abraham Mukwevho, Turgay Celik
Summary: This paper provides a comprehensive survey of fault tolerance methods for cloud computing, including ReActive Methods (RAMs), PRoactive Methods (PRMs), and ReSilient Methods (RSMs). Machine Learning and Artificial Intelligence have played a significant role in optimizing recovery time in RSM domain. Current issues and challenges in cloud fault tolerance are also discussed to identify potential areas for future research.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2021)
Article
Computer Science, Information Systems
Frederico Cerveira, Raul Barbosa, Henrique Madeira, Filipe Araujo
Summary: Virtualized servers are widely used in cloud computing environments to host online applications and provide elastic computing resources. However, the presence of soft errors in large-scale servers can lead to various failure modes, with hang failures being the most common. A recovery mechanism using online testing is developed to address these hang failures and ensure server uptime.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Article
Computer Science, Information Systems
Priti Kumari, Parmeet Kaur
Summary: Cloud computing has transformed the delivery model of information technology from product to service, but its performance is hindered by scale-related vulnerabilities, making fault tolerance a critical requirement for achieving high performance. This comprehensive overview of fault tolerance in cloud computing presents solutions and identifies future research directions.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Hardware & Architecture
Yu Wu, Duo Liu, Xianzhang Chen, Jinting Ren, Renping Liu, Yujuan Tan, Ziling Zhang
Summary: The proposed MobileRE hybrid fault tolerance strategy combines erasure codes and replicas for a mobile distributed system, dynamically adjusting based on real-time network status to ensure data reliability and reduce system reliability cost rate.
JOURNAL OF SYSTEMS ARCHITECTURE
(2021)
Article
Computer Science, Software Engineering
Salma M. A. Attallah, Magda B. Fayek, Salwa M. Nassar, Elsayed E. Hemayed
Summary: The paper proposes a proactive fault tolerance algorithm with load balancing for achieving high reliability and availability of cloud computing infrastructure. The method tolerates virtual machine CPU faults and tracks CPU utilization changes in real time, making decisions to migrate faulty virtual machines or adjust loads to ensure system stability.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2021)
Article
Computer Science, Hardware & Architecture
Chao Qiu, Zheyuan Chen, Xiaoxu Ren, Ziming Dai, Cheng Zhang, Xiaofei Wang
Summary: With the advancements of 6G technology, there is growing interest in 6G immersive services, which utilize wearable devices to provide high-quality virtual experiences for users. The active involvement of service users and providers has led to the rapid proliferation of wearable devices. Emerging technologies like cloud computing and edge computing have further facilitated the development of the 6G immersive service market. However, the widespread use of wearable devices poses challenges in resource provisioning and meeting diverse requirements. To address these challenges, an AI-driven approach called Almers-6G is proposed, focusing on large and small regions for resource allocation and matching using immersive learning and blockchain-based mechanisms.
IEEE WIRELESS COMMUNICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Mani Alaei, Reihaneh Khorsand, Mohammadreza Ramezanpour
Summary: The research aims to develop an adaptive fault detection strategy based on the Improved Differential Evolution algorithm in cloud computing to minimize energy consumption, makespan, total cost, and tolerate faults while scheduling scientific workflows. The proposed method utilizes an adaptive network-based fuzzy inference system prediction model to proactively control resource load fluctuation and applies a reactive fault tolerance technique for processor failures. Experimental results showed significant improvements in scheduling performance, fault tolerance, makespan, energy consumption, task fault ratio, and total cost compared to existing techniques.
APPLIED SOFT COMPUTING
(2021)
Review
Computer Science, Hardware & Architecture
Ahmad Salah AlAhmad, Hasan Kahtan, Yehia Ibrahim Alzoubi, Omar Ali, Ashraf Jaradat
Summary: Mobile cloud computing (MCC) is a popular technology, but it currently faces critical security issues including authentication, privacy, and trust. Existing MCC models lack comprehensive protection for data, resources, and communication channels. Further research and design solutions are needed from model developers and practitioners.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Ahyoung Lee, Jui Mhatre, Rupak Kumar Das, Min Hong
Summary: Healthcare is an essential part of life, and the advancement of technology is driving the rapid development of the healthcare industry. Cloud-based healthcare systems have the potential to provide reliable and remote interactions between patients and healthcare professionals, but they face challenges such as network availability, latency, battery life, and resource availability. To address these challenges, a hybrid mobile cloud computing architecture is proposed, and the performance of heuristic and dynamic machine learning algorithms for task scheduling and load balancing are evaluated and compared.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Review
Computer Science, Information Systems
Muhammad Asim Shahid, Noman Islam, Muhammad Mansoor Alam, M. S. Mazliham, Shahrulniza Musa
Summary: This research article provides a detailed survey of emerging fault tolerance methods for Cloud Computing, categorizing them into Reactive Methods, Proactive Methods, and Resilient Methods. Each category focuses on different approaches to deal with system faults, with Resilient Methods aiming to reduce recovery time from malfunctions by utilizing Machine Learning and Artificial Intelligence.
COMPUTER SCIENCE REVIEW
(2021)
Review
Computer Science, Hardware & Architecture
Shreshth Tuli, Fatemeh Mirhakimi, Samodha Pallewatta, Syed Zawad, Giuliano Casale, Bahman Javadi, Feng Yan, Rajkumar Buyya, Nicholas R. Jennings
Summary: In recent years, there has been a shift in computing paradigms towards decentralized systems like IoT, Edge, Fog, Cloud, and Serverless. This shift has been powered by the adoption of AI-driven autonomous systems for managing distributed computing resources. This survey explores the evolution of data-driven AI methods and their impact on computing systems, focusing on resource management and QoS optimization. It also discusses future research directions and the potential of AI-driven computing systems.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2023)
Article
Engineering, Biomedical
Majid Sepahvand, Fardin Abdali-Mohammadi
Summary: Biometric recognition systems can utilize physiological signals like electrocardiogram (ECG) for identification, and a personal biometric recognition system has been designed to estimate these medical variables.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Majid Sepahvand, Fardin Abdali-Mohammadi
Summary: The manuscript introduces a novel method for human motion detection based on inertial sensors and a new ensemble learning approach through genetic programing. The method utilizes spatial information of human motion for feature extraction and enhances classifier optimization through genetic programing.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Biology
Majid Sepahvand, Fardin Abdali-Mohammadi
Summary: This paper proposes a method based on knowledge distillation to overcome the dissociation between MI classification and MD classification in histopathological images of breast cancer. The experimental results show that the proposed method achieves high accuracy rates at different magnification factors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Cybernetics
Ahmad Absetan, Abdolhossein Fathi
Summary: This paper proposes a new method for evaluating the quality of retargeted images. By training a convolutional neural network on different image retargeting methods, the method utilizes deep learning to identify important regions and extract measures for assessing geometric changes, block line bending, and information loss. The proposed method demonstrates excellent performance and reliability compared to existing methods, as shown by tests on known databases.
CYBERNETICS AND SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Farzaneh Meshkat, Fardin Abdali-Mohammadi
Summary: This paper presents a system for the recognition of handwritten Farsi characters extracted from an inertial pen. The system combines advances in MEMS, deep learning techniques, and powerful processing methods to improve character recognition accuracy.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2022)
Article
Mathematics
Maytham N. Meqdad, Fardin Abdali-Mohammadi, Seifedine Kadry
Summary: This paper proposes a method to fuse electrocardiogram signals using time-frequency transform and structural learning, and optimize the features through genetic programming. The proposed method achieves a high accuracy of 97.60% in diagnosing arrhythmias on the Chapman dataset.
Article
Computer Science, Artificial Intelligence
Kaveh Moradkhani, Abdolhossein Fathi
Summary: This paper presents a deep learning-based approach for detecting surface water by training and optimizing three robust deep architectures and combining their results. By employing a deep stacked ensemble model, the proposed technique achieves more accurate segmentation masks of water areas and outperforms state-of-the-art results in a water body detection dataset.
APPLIED SOFT COMPUTING
(2022)
Article
Engineering, Electrical & Electronic
Sara Hosseini, Abdolhossein Fathi
Summary: This paper proposes a new system based on deep learning models for detecting the status of passengers and driver's seat belts. Experimental results show that the system achieves high accuracy in windshield detection, as well as detecting passenger and driver's seat belt rule violations.
SIGNAL IMAGE AND VIDEO PROCESSING
(2023)
Article
Biology
Majid Sepahvand, Fardin Abdali-Mohammadi
Summary: A lightweight learning model based on knowledge distillation is developed to classify breast cancer histopathological images in the BreakHis dataset. The student model, trained using two teacher models based on VGG and ResNext, achieved a recognition rate of 97.09% with significantly fewer parameters, reduced GPU memory usage, and higher compression rate compared to the teacher model. The results showed that the student model produced acceptable outputs in classifying the images of breast cancer in BreakHis.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Mehran Dalvand, Abdolhossein Fathi, Arezoo Kamran
Summary: Interactive image segmentation is a method that uses user input to accurately segment objects from the background. Current techniques are sensitive to the location and number of seed points, requiring users to repeat the process multiple times. This paper proposes a parallel fusion model using majority voting technique, which is more reliable and requires less user interaction. Evaluation and comparison with state-of-the-art methods demonstrate the efficiency of the proposed model.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Biochemical Research Methods
Shima Shafiee, Abdolhossein Fathi, Ghazaleh Taherzadeh
Summary: Peptide-binding proteins play important roles in various applications. SPPPred is a novel ensemble machine learning-based approach that can predict protein-peptide binding residues with consistent and comparable performance.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Computer Science, Information Systems
Malihe Mardanpour, Majid Sepahvand, Fardin Abdali-Mohammadi, Mahya Nikouei, Homeyra Sarabi
Summary: This paper proposes a knowledge distillation (KD) paradigm to reduce the computational cost of high accuracy activity classification using multi inertial sensors. By mapping tri-axial signals into single axis signals, the proposed method achieves 92.90% accuracy on embedded devices, outperforming other state-of-the-art KD approaches.
INFORMATION SCIENCES
(2023)
Article
Remote Sensing
Samaneh Molavi Vardanjani, Abdolhossein Fathi, Kaveh Moradkhani
Summary: The increasing development of imaging technology has made aerial image analysis one of the most widely used fields in image processing. In this study, a new hybrid deep learning model named GRSNet is proposed to automatically segment buildings in high-resolution satellite images. GRSNet extends the UNet framework by including attention gates, residual units, and deep supervision, and achieves superior performance compared to other state-of-the-art building segmentation methods.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2022)
Article
Environmental Sciences
Bahare Andayeshgar, Fardin Abdali-Mohammadi, Majid Sepahvand, Alireza Daneshkhah, Afshin Almasi, Nader Salari
Summary: This study aims to improve the accuracy of arrhythmia diagnosis by utilizing a novel graph convolutional network combined with mutual information indices extracted from 12 ECG leads, showing a significant advancement in detecting and classifying different types of rhythms.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Article
Computer Science, Information Systems
Maytham N. Meqdad, Fardin Abdali-Mohammadi, Seifedine Kadry
Summary: This article proposes a new interpretable meta structural learning algorithm for the detection of arrhythmia in electrocardiogram signals. By collaborating between models and transferring knowledge, the algorithm maintains generalization when dealing with unseen samples. To improve interpretability, CNN models are encoded as evolutionary trees using genetic programming algorithms. Experimental results show that the proposed model achieves high accuracy and performs competitively compared to other models based on big deep models.