Article
Automation & Control Systems
Rajni Aron, Ajith Abraham
Summary: This article reviews the background and latest technologies in scheduling in cloud computing, conducts a comprehensive survey of existing resource scheduling problems considering high-level taxonomy, and discusses the importance of meta-heuristic methods and artificial intelligence in resource scheduling methods in cloud computing.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
Lining Xing, Mingyang Zhang, Hao Li, Maoguo Gong, Jinghui Yang, Kesheng Wang
Summary: This paper emphasizes the importance of workflow scheduling in cloud computing and proposes a Local Search driven Periodic Scheduling (LSPS) method to optimize schedules. By utilizing task waiting time and designing a problem-specific local search strategy, LSPS shows advantages in alleviating the negative effects of dynamics and uncertainty and improving the quality of schedules.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Review
Computer Science, Information Systems
Dalia Abdulkareem Shafiq, N. Z. Jhanjhi, Azween Abdullah
Summary: Cloud Computing is a powerful model that offers various services and allows users to purchase required services based on their needs. Load Balancing is a common issue in cloud computing that affects application performance and adherence to service level agreements. This paper provides a comprehensive review of Load Balancing techniques and identifies research gaps for future studies.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Faisal Ahmad, Mohammad Shahid, Mahfooz Alam, Zubair Ashraf, Mohammad Sajid, Ketan Kotecha, Gaurav Dhiman
Summary: This study investigates the allocation problem of multiple workflows in a cloud environment and proposes a levelized multiple workflow allocation strategy with task merging. The experimental results show that this strategy outperforms its peers in most cases in terms of various performance parameters.
Article
Computer Science, Information Systems
P. Rajasekar, P. Santhiya
Summary: The deployment of IaaS clouds for compute-intensive scientific workflows has become a popular topic in recent years. This study proposes a Budget-based resource Provisioning and Scheduling (BPS) algorithm that can efficiently respond to cloud dynamics and reduce workflow execution time while staying within a specified budget. Experimental results show that the BPS algorithm achieves a budget completion rate of 94% and reduces makespan by 29% compared to state-of-the-art budget-aware algorithms.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Manoj Kumar, Suman
Summary: This paper presents a novel hybrid algorithm to address the scheduling issue in cloud computing. The algorithm combines the advantages of NAG and CSA, resulting in cost reduction, time-saving, and improved quality of service for users.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Artificial Intelligence
Shuo Qin, Dechang Pi, Zhongshi Shao
Summary: With the rapid development of cloud computing, scheduling complex scientific workflows on the cloud has become a challenging problem. This paper proposes a novel adaptive iterated local search framework (AILS) to meet budget constraints and demonstrates its effectiveness through comparison.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Theory & Methods
Guangyao Zhou, Wenhong Tian, Rajkumar Buyya
Summary: Cloud computing is a large-scale distributed computing system that dynamically provides elastic services to users. Resource scheduling in Cloud computing, which aims to minimize makespan, is usually NP-Hard. This paper proposes multi-search-routes-based algorithms, integrating LPT and BFD as basic search routes with the OneStep neighborhood search algorithm, to optimize scheduling schemes for homogeneous and heterogeneous resources. Theoretical derivations and extensive experiments demonstrate the superiority of the proposed algorithms in minimizing makespan problems.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Information Systems
Nupur Jangu, Zahid Raza
Summary: Corporations and enterprises often integrate fog computing with cloud computing in IoT-based systems to maximize resource utilization and improve service quality. This study proposes an efficient two-step scheduling algorithm to address the challenges of real-time processing in the IoT environment.
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Javid Ali Liakath, Pradeep Krishnadoss, Gobalakrishnan Natesan
Summary: Cloud computing offers on-demand, automatic resource delivery in a transparent manner, but incidental failures during task execution can bring down its performance. Intelligent task scheduling algorithms have been developed, but most neglect fault tolerance, which is crucial for better cloud performance. This research proposes a fault tolerance aware algorithm that effectively addresses failures and improves scheduling performance in the cloud.
PEER-TO-PEER NETWORKING AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Pradeep Krishnadoss, Vijayakumar Kedalu Poornachary, Parkavi Krishnamoorthy, Leninisha Shanmugam
Summary: Well organized datacentres with interconnected servers are the foundation of cloud computing infrastructure. User requests are sent to these servers through an interface and services are provided on-demand. Task scheduling in the cloud is a challenging NP hard problem that affects cloud performance and user satisfaction. Researchers have proposed a meta-heuristic algorithm based on seagull optimization to optimize task scheduling in the heterogeneous cloud environment.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Computer Science, Hardware & Architecture
Mohana Bakshi, Chandreyee Chowdhury, Ujjwal Maulik
Summary: IoT devices can gather, store, and process more data, requiring scalability. Edge computing allows processing functions to be moved closer to where data is gathered. Job scheduling at the edges is important for ensuring near real-time response, but existing techniques do not consider job dependency, conflict, and heterogeneous edge infrastructure. This paper proposes an optimal job scheduling approach based on the cuckoo search algorithm, achieving high resource utilization even with conflicts and dependencies.
JOURNAL OF SUPERCOMPUTING
(2023)
Review
Computer Science, Information Systems
Zahra Jalali Khalil Abadi, Najme Mansouri, Mahshid Khalouie
Summary: Despite its advantages, cloud computing can be a poor choice due to slow response times, leading to the need for fog computing. Scheduling tasks in a fog environment presents a major challenge, as IoT clients require timely execution, lower-cost services, and secure operations. This paper reviews scheduling algorithms proposed by various researchers in fog environments, compares simulation tools for developers, and discusses open issues and future research directions.
COMPUTER SCIENCE REVIEW
(2023)
Article
Computer Science, Information Systems
Andre Pires, Jose Simao, Luis Veiga
Summary: Cloud Computing has been successful in providing ample resources for scalable applications, but there is a growing need for lower latency services and affordable bandwidth at the internet's edge. As a result, new computing paradigms like Edge Computing and Fog Computing are emerging. Caravela is a Docker container orchestrator that leverages volunteer edge resources from users to create an Edge Cloud.
JOURNAL OF GRID COMPUTING
(2021)
Article
Computer Science, Information Systems
Ali Belgacem, Kadda Beghdad-Bey, Hassina Nacer
Summary: Cloud computing is a popular paradigm for leasing IT services over the Internet, which requires dynamic allocation and release of resources to ensure service quality. A new dynamic resource allocation model is proposed along with the MOSOS algorithm to minimize completion time and cost for improved cloud performance.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Review
Computer Science, Artificial Intelligence
Nadim Rana, Muhammad Shafie Abd Latiff, Shafi'i Muhammad Abdulhamid, Haruna Chiroma
NEURAL COMPUTING & APPLICATIONS
(2020)
Article
Computer Science, Information Systems
Nureni Ayofe Azeez, Sanjay Misra, Ihotu Agbo Margaret, Luis Fernandez-Sanz, Shafi'i Muhammad Abdulhamid
Summary: Phishing is a significant issue in cyberspace, with challenges such as low detection rates and slow access times for existing anti-phishing solutions. However, a new automated white-list approach has been proposed, showing high accuracy in detecting phishing attacks, especially with lower-level datasets. This approach outperforms similar benchmarks in accuracy and efficiency, demonstrating robust detection performance in comparison to other techniques.
COMPUTERS & SECURITY
(2021)
Article
Engineering, Multidisciplinary
Nadim Rana, Muhammad Shafie Abd Latiff, Shafi'i Muhammad Abdulhamid, Sanjay Misra
Summary: The study proposed a hybrid algorithm (M-WODE) based on evolutionary algorithm and whale optimization algorithm for solving virtual machine scheduling problems. Experimental results show that the algorithm outperformed previous algorithms in most cases in terms of makespan and cost trade-offs.
ENGINEERING OPTIMIZATION
(2022)
Review
Engineering, Multidisciplinary
Haruna Chiroma, Shafi'i M. Abdulhamid, Ibrahim A. T. Hashem, Kayode S. Adewole, Absalom E. Ezugwu, Saidu Abubakar, Liyana Shuib
Summary: The Internet of Vehicles (IoV) is a developing technology attracting attention from both industry and academia, with the potential for hundreds of millions of connected vehicles by 2035. However, surveys on leveraging deep learning in IoV within the context of big data analytics are currently scarce. This study presents a survey exploring the theoretical perspective of the role of deep learning in IoV within the context of big data analytics, highlighting research opportunities that cut across deep learning, IoV, and big data analytics.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Javed Ali, M. Aldhaifallah, Kottakkaran Sooppy Nisar, A. A. Aljabr, M. Tanveer
Summary: Support vector machines (SVMs) have been successfully used in classification and regression problems. Twin SVM (TWSVM) reduces the complexity of SVM, while least squares twin SVM (LSTSVM) is useful for solving multiclass classification problems with less computational cost and good generalization performance. A new regularization based method called multiclass regularized least squares twin support vector machine (MRLSTSVM) is proposed in this work to improve generalization performance in multiclass classification problems.
Review
Psychology, Multidisciplinary
Syed Hamid Hussain Madni, Javed Ali, Hafiz Ali Husnain, Maidul Hasan Masum, Saad Mustafa, Junaid Shuja, Mohammed Maray, Samira Hosseini
Summary: This research investigates the factors influencing the adoption of IoT in E-Learning in higher educational institutes in developing countries. A proposed adoption model categorizes the influencing factors into four groups: individual, organizational, environmental, and technological. The significant factors identified include privacy, infrastructure readiness, financial constraints, ease of use, support of faculty, interaction, attitude, and network and data security.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Green & Sustainable Science & Technology
Javed Ali, Syed Hamid Hussain Madni, Mohd Shamim Ilyas Jahangeer, Muhammad Abdullah Ahmed Danish
Summary: This study investigates factors influencing the adoption of Internet of Things (IoT) in e-learning systems of higher education institutions (HEIs) in Saudi Arabia. The results suggest that usability, accessibility, technical support, and individual proficiencies significantly contribute to the rate of IoT incorporation. Financial obstacles, self-efficacy, interactive capability, online surveillance, automated attendance tracking, training programs, network and data safeguarding measures, and relevant tools also have a significant impact on IoT adoption.
Review
Multidisciplinary Sciences
Emmanuel Gbenga Dada, Stephen Bassi Joseph, David Opeoluwa Oyewola, Alaba Ayotunde Fadele, Haruna Chiroma, Shafi'i Muhammad Abdulhamid
Summary: This paper presents the recent progress, variants, and applications of the Grey Wolf Optimization (GWO) algorithm, highlighting the potential for development of more robust variants. The review aims to stimulate researchers in advancing the effectiveness of GWO in solving complex optimization problems.
GAZI UNIVERSITY JOURNAL OF SCIENCE
(2022)
Review
Computer Science, Information Systems
Gul Sahar, Kamalrulnizam Bin Abu Bakar, Fatima Tul Zuhra, Sabit Rahim, Tehmina Bibi, Syed Hamid Hussain Madni
Summary: This paper reviews the existing energy-efficient data redundancy reduction schemes in Wireless Sensor Networks (WSNs), categorizing the concept into three levels: node, cluster head, and sink. It also highlights current key issues and challenges, as well as suggesting future research directions for reducing data redundancy.
Article
Computer Science, Artificial Intelligence
Ibrahim Bello, Haruna Chiroma, Usman A. Abdullahi, Abdulsalam Ya'u Gital, Fatsuma Jauro, Abdullah Khan, Julius O. Okesola, Shafi'i M. Abdulhamid
Summary: Recently, there has been a growing interest in using intelligent algorithms, particularly deep learning algorithms, for ransomware attack detection. However, there is a lack of comprehensive literature review on the applications of intelligent algorithms in detecting ransomware attacks, indicating a potential direction for future research.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Menzi Skhosana, Absalom E. Ezugwu, Nadim Rana, Shafi'i M. Abdulhamid
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT VI
(2020)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Olutosin Taiwo, Absalom E. Ezugwu, Nadim Rana, Shafi'i M. Abdulhamid
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT VI
(2020)
Article
Computer Science, Artificial Intelligence
Fatsuma Jauro, Haruna Chiroma, Abdulsalam Y. Gital, Mubarak Almutairi, Shafi'i M. Abdulhamid, Jemal H. Abawajy
APPLIED SOFT COMPUTING
(2020)
