2021_Optimization Of Replication Strategy Based On Monte Carlo Bat Algorithm In Cloud Computing Environment
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| country | Malaysia |
| date | 2021-08-29 |
| format | General Document |
| id | 16261 |
| institution | UniSZA |
| originalfilename | 16261_8ed266ba7c017bb.pdf |
| person | Aws Ismail Sami Abu Eid |
| recordtype | oai_dc |
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| spelling | 16261 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16261 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 General Document Malaysia Library Staff (Top Management) Library Staff (Management) Library Staff (Support) Terengganu Faculty of Informatics & Computing English application/pdf 1.5 Server storage Scanned document Universiti Sultan Zainal Abidin UniSZA Private Access UNIVERSITI SULTAN ZAINAL ABIDIN SAMBox 2.3.4; modified using iTextSharp™ 5.5.10 ©2000-2016 iText Group NV (AGPL-version) Copyright©PWB2025 234 Cloud computing 2021-08-29 16261_8ed266ba7c017bb.pdf Aws Ismail Sami Abu Eid 2021_Optimization Of Replication Strategy Based On Monte Carlo Bat Algorithm In Cloud Computing Environment In distributed systems such as grid and cloud, effective data management has become a critical requirement. In achieving effective data management, the data replication technique is a common solution to address metrics such as availability, bandwidth consumption, resource consumption, response time, and storage usage. However, reducing response time and resource consumption like computation and cloud storage became major challenges within the cloud environment for data replication. Effective data management may be improved by reducing the time consumption and reducing resources in searching data replicas location in a cloud environment. Consequently, the performance can be increased, and therefore, the cost of updating a new replica may be reduced. Choosing data replica locations on the unknown dynamic cloud environment is hardness or NP-hardness problem (Non-deterministic Polynomial-time). For this reason, the search for a new meta-heuristic algorithm is still a useful endeavour. This thesis presents the design and implementation of a new meta-heuristic algorithm based on Markov Chain Monte Carlo MCMC and BAT Algorithm, called Monte Carlo BAT Optimization (MCBO), for generating a random start location for the replication route and determining the most effective path.MCBO is the first approach that uses BAT as the backbone engine to introduce Markov Chain Monte Carlo replication techniques and to optimize the dynamic data replication on a cloud environment for the reduction of time and resources in unknown areas. The experimental results supported by non-parametric statistical analysis demonstrate that the MCBO algorithm gives competitive performance over its counterparts, MCBO has achieved 58 % of time reduction, which indicates reduce time and resource consumption to distribute replica on a cloud environment, benchmark with results 48% for GA (Genetic Algorithm) and 46% PSO (Particle Swarm Optimisation). This finding contributes to the optimization data replication technique by minimizing the time and resource consumption in the cloud environment. Till now, there is no prior work that incorporates MCMC and BAT algorithms to minimize time and resource consumption in the cloud environment. This research contributes to the field of dynamic replication strategies as it provides a new optimization model called MCBO. MCBO enhanced data replica distribution by reducing time and resource consumptions and providing the most optimum path to update data replica. In the cloud computing environment, the experimental results indicate a considerable improvement in replication strategy. Dissertations, Academic Cloud Computing Data Replication Monte Carlo Simulations Thesis |
| spellingShingle | 2021_Optimization Of Replication Strategy Based On Monte Carlo Bat Algorithm In Cloud Computing Environment |
| state | Terengganu |
| subject | Cloud computing Dissertations, Academic |
| summary | In distributed systems such as grid and cloud, effective data management has become a critical requirement. In achieving effective data management, the data replication technique is a common solution to address metrics such as availability, bandwidth consumption, resource consumption, response time, and storage usage. However, reducing response time and resource consumption like computation and cloud storage became major challenges within the cloud environment for data replication. Effective data management may be improved by reducing the time consumption and reducing resources in searching data replicas location in a cloud environment. Consequently, the performance can be increased, and therefore, the cost of updating a new replica may be reduced. Choosing data replica locations on the unknown dynamic cloud environment is hardness or NP-hardness problem (Non-deterministic Polynomial-time). For this reason, the search for a new meta-heuristic algorithm is still a useful endeavour. This thesis presents the design and implementation of a new meta-heuristic algorithm based on Markov Chain Monte Carlo MCMC and BAT Algorithm, called Monte Carlo BAT Optimization (MCBO), for generating a random start location for the replication route and determining the most effective path.MCBO is the first approach that uses BAT as the backbone engine to introduce Markov Chain Monte Carlo replication techniques and to optimize the dynamic data replication on a cloud environment for the reduction of time and resources in unknown areas. The experimental results supported by non-parametric statistical analysis demonstrate that the MCBO algorithm gives competitive performance over its counterparts, MCBO has achieved 58 % of time reduction, which indicates reduce time and resource consumption to distribute replica on a cloud environment, benchmark with results 48% for GA (Genetic Algorithm) and 46% PSO (Particle Swarm Optimisation). This finding contributes to the optimization data replication technique by minimizing the time and resource consumption in the cloud environment. Till now, there is no prior work that incorporates MCMC and BAT algorithms to minimize time and resource consumption in the cloud environment. This research contributes to the field of dynamic replication strategies as it provides a new optimization model called MCBO. MCBO enhanced data replica distribution by reducing time and resource consumptions and providing the most optimum path to update data replica. In the cloud computing environment, the experimental results indicate a considerable improvement in replication strategy. |
| title | 2021_Optimization Of Replication Strategy Based On Monte Carlo Bat Algorithm In Cloud Computing Environment |
| title_full | 2021_Optimization Of Replication Strategy Based On Monte Carlo Bat Algorithm In Cloud Computing Environment |
| title_fullStr | 2021_Optimization Of Replication Strategy Based On Monte Carlo Bat Algorithm In Cloud Computing Environment |
| title_full_unstemmed | 2021_Optimization Of Replication Strategy Based On Monte Carlo Bat Algorithm In Cloud Computing Environment |
| title_short | 2021_Optimization Of Replication Strategy Based On Monte Carlo Bat Algorithm In Cloud Computing Environment |
| title_sort | 2021_optimization of replication strategy based on monte carlo bat algorithm in cloud computing environment |