2020_A New Resource-Aware Approach To Improve Schedule Workflows In Cloud Computing Environment
| Format: | General Document |
|---|
| _version_ | 1860798146825682944 |
|---|---|
| building | INTELEK Repository |
| collection | Online Access |
| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 |
| copyright | Copyright©PWB2025 |
| country | Malaysia |
| date | 2020-02-02 |
| format | General Document |
| id | 16174 |
| institution | UniSZA |
| originalfilename | 16174_5270a417ff8c89f.pdf |
| person | Tawfiq Ahmad Alarawashdeh |
| recordtype | oai_dc |
| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16174 |
| sourcemedia | Server storage Scanned document |
| spelling | 16174 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16174 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 2020-02-02 Cloud computing Cloud computing 215 16174_5270a417ff8c89f.pdf Tawfiq Ahmad Alarawashdeh Workflow Scheduling Resource Management 2020_A New Resource-Aware Approach To Improve Schedule Workflows In Cloud Computing Environment Cloud computing has emerged as an efficient environment to execute scientific workflows. In a cloud computing, users can rent Virtual Machines (VMs) to execute their computational tasks. Additionally, users are charged based on a number of resources they rent using pay-per-use cost model. In such case, determining the right number of resources to rent is a challenging task. Over-renting increases the execution cost, where, under-renting results in increasing the execution time. To address this problem, this work focuses on maximization the utilization of resources. By improving the utilization of the resource, this study aims to improve the execution time and cost, since the utilization of the resources influences the execution time and cost. This research considers two variations concerning this problem that can be denoted as single workflow scheduling and multiple workflows scheduling. In single workflow scheduling problem, the input is considered to be single workflow with a set of available resources. Whereby in multiple workflows scheduling problem, the input is assumed to be multiple workflow submitted by several users with a set of available resources. The single workflow scheduling problem is addressed by proposing the Level-Based Clustering (LBC) algorithm. By considering each level of tasks as a single object (cluster), this algorithm aims to establish a relationship between the execution requirement for each cluster, and the number of resources that must be used to execute the entire workflow. To address the multiple workflow scheduling problem, establishing a fair division of the resources between the users (input workflows) is considered as part of the objective function. A modified version of this algorithm termed as LBC-Multiple (LBCM) is presented. In the LBCM algorithm, a number of resources assigned to each workflow depends on the computational requirement for these workflows. This is established by a time-slot mechanism that determines the largest acceptable execution time for each workflow level tasks. The LBC algorithm performance is compared against three well-known algorithms from the literature, and the result shows that the LBC algorithm achieves 50%, 25%, 50% on average improvement in term of cost, makespan and the number of resources used, respectively. In addition, in most situations, the LBCM achieves 20% on average improvement compared to the LBC algorithm. The proposed algorithms take into consideration of the structure of the workflows and the computation requirement of the tasks during the distribution of the resources. The LBCM extends the LBC algorithm by virtual connecting the input workflows. The results show that on average the LBC algorithm reduces the number of used resources by 50%, and this underlines the efficacy of the LBC algorithm in terms of resource utilization. In addition, in most cases, the LBCM algorithm achieves 20% improvement compared the sequential execution strategy, and this contributes toward improving the utilization of the resources. Dissertations, Academic Thesis |
| spellingShingle | 2020_A New Resource-Aware Approach To Improve Schedule Workflows In Cloud Computing Environment |
| state | Terengganu |
| subject | Cloud computing Dissertations, Academic |
| summary | Cloud computing has emerged as an efficient environment to execute scientific workflows. In a cloud computing, users can rent Virtual Machines (VMs) to execute their computational tasks. Additionally, users are charged based on a number of resources they rent using pay-per-use cost model. In such case, determining the right number of resources to rent is a challenging task. Over-renting increases the execution cost, where, under-renting results in increasing the execution time. To address this problem, this work focuses on maximization the utilization of resources. By improving the utilization of the resource, this study aims to improve the execution time and cost, since the utilization of the resources influences the execution time and cost. This research considers two variations concerning this problem that can be denoted as single workflow scheduling and multiple workflows scheduling. In single workflow scheduling problem, the input is considered to be single workflow with a set of available resources. Whereby in multiple workflows scheduling problem, the input is assumed to be multiple workflow submitted by several users with a set of available resources. The single workflow scheduling problem is addressed by proposing the Level-Based Clustering (LBC) algorithm. By considering each level of tasks as a single object (cluster), this algorithm aims to establish a relationship between the execution requirement for each cluster, and the number of resources that must be used to execute the entire workflow. To address the multiple workflow scheduling problem, establishing a fair division of the resources between the users (input workflows) is considered as part of the objective function. A modified version of this algorithm termed as LBC-Multiple (LBCM) is presented. In the LBCM algorithm, a number of resources assigned to each workflow depends on the computational requirement for these workflows. This is established by a time-slot mechanism that determines the largest acceptable execution time for each workflow level tasks. The LBC algorithm performance is compared against three well-known algorithms from the literature, and the result shows that the LBC algorithm achieves 50%, 25%, 50% on average improvement in term of cost, makespan and the number of resources used, respectively. In addition, in most situations, the LBCM achieves 20% on average improvement compared to the LBC algorithm. The proposed algorithms take into consideration of the structure of the workflows and the computation requirement of the tasks during the distribution of the resources. The LBCM extends the LBC algorithm by virtual connecting the input workflows. The results show that on average the LBC algorithm reduces the number of used resources by 50%, and this underlines the efficacy of the LBC algorithm in terms of resource utilization. In addition, in most cases, the LBCM algorithm achieves 20% improvement compared the sequential execution strategy, and this contributes toward improving the utilization of the resources. |
| title | 2020_A New Resource-Aware Approach To Improve Schedule Workflows In Cloud Computing Environment |
| title_full | 2020_A New Resource-Aware Approach To Improve Schedule Workflows In Cloud Computing Environment |
| title_fullStr | 2020_A New Resource-Aware Approach To Improve Schedule Workflows In Cloud Computing Environment |
| title_full_unstemmed | 2020_A New Resource-Aware Approach To Improve Schedule Workflows In Cloud Computing Environment |
| title_short | 2020_A New Resource-Aware Approach To Improve Schedule Workflows In Cloud Computing Environment |
| title_sort | 2020_a new resource-aware approach to improve schedule workflows in cloud computing environment |