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  1. 1
    Efficient multi-cloud storage using online dynamic replication and placement algorithms for online social networks

    Efficient multi-cloud storage using online dynamic replication and placement algorithms for online social networks by Aldailamy, Ali Y., Muhammed, Abdullah, Abdul Hamid, Nor Asilah Wati, Latip, Rohaya, Ismail, Waidah

    Published 2024
    “…The DTS algorithm establishes the replication and placement of objects using deterministic time slots, while the RTS algorithm is based on randomized time slots. …”
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  2. 2
    Online Dynamic Replication and Placement Algorithms for Cost Optimization of Online Social Networks in Two-tier Multi-cloud

    Online Dynamic Replication and Placement Algorithms for Cost Optimization of Online Social Networks in Two-tier Multi-cloud by Ali Y. Aldailamy, Abdullah Muhammed, Rohaya Latip, Nor Asilah Wati Abdul Hamid, Waidah Ismail

    Published 2024
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  3. 3
    An adaptive data replication and placement for efficient data storage of online social networks using two-tier multi-cloud environment

    An adaptive data replication and placement for efficient data storage of online social networks using two-tier multi-cloud environment by Al-Dailamy, Ali Y., Muhammed, Abdullah, Latip, Rohaya, Abdul Hamid, Nor Asila Wati, Esmail, Waidah

    Published 2022
    “…The second algorithm is Dynamic Exponential Time (DET) which determines the data object replication and placement based on exponential time periods. …”
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  4. 4
    Efficient Multi-Cloud Storage Using Online Dynamic Replication and Placement Algorithms for Online Social Networks

    Efficient Multi-Cloud Storage Using Online Dynamic Replication and Placement Algorithms for Online Social Networks by Ali Y. Aldailamy, Abdullah Muhammed, Nor Asilah Wati Abdul Hamid, Rohaya Lati, Waidah Ismail

    Published 2024
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  5. 5
    Online dynamic replication and placement algorithms for cost optimization of online social networks in two-tier multi-cloud

    Online dynamic replication and placement algorithms for cost optimization of online social networks in two-tier multi-cloud by Aldailamy, Ali Y., Muhammed, Abdullah, Latip, Rohaya, Hamid, Nor Asilah Wati Abdul, Ismail, Waidah

    Published 2024
    “…The second algorithm is Dynamic Exponential Time (DET) which determines the data object replication and placement based on exponential time periods. …”
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  6. 6
    Re-engineering grid-based quorum replication into binary vote assignment on cloud: A scalable approach for strong consistency in cloud databases

    Re-engineering grid-based quorum replication into binary vote assignment on cloud: A scalable approach for strong consistency in cloud databases by Ainul Azila Che Fauzi, Ainul Azila, Noor Ashafiqa, Noor Ashafiqa, Asiah Mat, Asiah, Syerina Azlin Md Nasir, Syerina Azlin, A. Noraziah, Noraziah

    Published 2025
    “…This study proposes the Binary Vote Assignment in Cloud (BVAC), a cloud-native replication algorithm re-engineered from the Binary Vote Assignment on Grid Quorum (BVAGQ). …”
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  7. 7
    Performance evaluation of real-time multiprocessor scheduling algorithms

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…The CPU profiler of JavaTM VisualVM measures the number of invocations of scheduling event handlers (procedures) in each algorithm as well as the total time spent in all invocations of this handler. …”
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  8. 8
    Cloud Worm Detection and Response Technique By Integrating The Enhanced Genetic Algorithm An Threat Level

    Cloud Worm Detection and Response Technique By Integrating The Enhanced Genetic Algorithm An Threat Level by Hasan Mahmoud Sha'ban Kanaker

    Published 2024
    thesis::doctoral thesis
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  9. 9
    A Toolkit for Simulation of Desktop Grid Environment

    A Toolkit for Simulation of Desktop Grid Environment by FOROUSHAN, PAYAM CHINI

    Published 2014
    “…In this type of environment it is nearly impossible to prove the effectiveness of a scheduling algorithm. Hence the main objective of this study is to develop a desktop grid simulator toolkit for measuring and modeling scheduler algorithm performance. …”
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  10. 10
    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT

    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT by Abdullah, Amran

    Published 2006
    “…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
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  11. 11
    A framework for implementing asynchronous replication scheme in utility-based computing environment

    A framework for implementing asynchronous replication scheme in utility-based computing environment by Wan Nor Shuhadah, W.N., Zhou, B.B., Zomaya, A.Y., Abawajy, J.H.

    Published 2016
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  12. 12
    Improving Class Timetabling using Genetic Algorithm

    Improving Class Timetabling using Genetic Algorithm by Qutishat, Ahmed Mohammed Ali

    Published 2006
    “…This paper reports the power fill techniques using GA in scheduling. Class timetabling problem is one of the applications in scheduling. …”
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  13. 13
    Examination timetabling using genetic algorithm case study: KUiTTHO

    Examination timetabling using genetic algorithm case study: KUiTTHO by Mohd Salikon, Mohd Zaki

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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  14. 14
    A Framework for Asynchronous Replication Scheme for Utility-based Computing Environment

    A Framework for Asynchronous Replication Scheme for Utility-based Computing Environment by Wan Nor Shuhadah, Wan Nik, Zhou, Bing Bing, Zomaya, Albert Y

    Published 2015
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  15. 15
    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO

    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO by Mohd. Zaki, Mohd. Salikon

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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  16. 16
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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  17. 17
    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing

    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

    Published 2011
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  18. 18
    Batch mode heuristic approaches for efficient task scheduling in grid computing system

    Batch mode heuristic approaches for efficient task scheduling in grid computing system by Maipan-Uku, Jamilu Yahaya

    Published 2016
    “…Many algorithms have been implemented to solve the grid scheduling problem. …”
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  19. 19
    Classroom finder system with student availability, space and time constraint

    Classroom finder system with student availability, space and time constraint by Loh, Yong Bin

    Published 2024
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  20. 20
    Smart student timetable planner

    Smart student timetable planner by Wong, Xin Tong

    Published 2025
    “…Course data is managed in CSV format, parsed into JSON for fast processing, while sessionStorage and localStorage handle user data within active sessions. A Genetic Algorithm forms the core scheduling engine, generating optimized timetables that respect both hard constraints, such as avoiding clashes, and soft constraints, such as personal preferences.The final output of this project is a functional web-based timetable planner that successfully enhances scheduling efficiency, reduces the likelihood of errors, and improves the overall academic planning experience. …”
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