Multi-Objective Scientific-Workflow Scheduling With Data Movement Awareness in Cloud
Due to serving several purposes simultaneously, running scientific workflows on dynamic environments such as cloud computing, has become multi-objective scheduling. Among these purposes, Cost and Makespan are probably the most two primitive objectives. Another critical factor in a large-scale scient...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8924641/ |
| _version_ | 1818444281577209856 |
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| author | Peerasak Wangsom Kittichai Lavangnananda Pascal Bouvry |
| author_facet | Peerasak Wangsom Kittichai Lavangnananda Pascal Bouvry |
| author_sort | Peerasak Wangsom |
| collection | DOAJ |
| description | Due to serving several purposes simultaneously, running scientific workflows on dynamic environments such as cloud computing, has become multi-objective scheduling. Among these purposes, Cost and Makespan are probably the most two primitive objectives. Another critical factor in a large-scale scientific workflow is tremendous amount of data during execution. Therefore, this work also includes Data Movement as an additional objective as it has a major impact on network utilization and energy consumption in network equipment in cloud data center. In considering these three objectives, this work proposes a framework for scheduling solutions which combines a new nodes clustering technique in Directed Acyclic Graph (DAG) model known as Multilevel Dependent Node Clustering (MDNC) and the multi-objective optimization, Extreme Nondominated Sorting Genetic Algorithm-III (E-NSGA-III). E-NSGA-III is the recent extension of Nondominated Sorting Genetic Algorithm (NSGA-III). Five well-known scientific workflows, CyberShake, Epigenomics, LIGO, Montage, and SIPHT are selected as testbeds, while the commonly known Hypervolume is chosen as the performance metric. In this work, MDNC is also experimented with both NSGA-III. Comparison among three approaches, E-NSGA-III alone, E-NSGA-III with Peer-to-Peer clustering and E-NSGA-III with MDNC are carried out. The superiority of the proposed framework among them and its limitation are discussed. |
| first_indexed | 2024-12-14T19:13:27Z |
| format | Article |
| id | doaj.art-6fb3f097408041aeab14f5da4f04a006 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T19:13:27Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6fb3f097408041aeab14f5da4f04a0062022-12-21T22:50:40ZengIEEEIEEE Access2169-35362019-01-01717706317708110.1109/ACCESS.2019.29579988924641Multi-Objective Scientific-Workflow Scheduling With Data Movement Awareness in CloudPeerasak Wangsom0https://orcid.org/0000-0002-3916-8973Kittichai Lavangnananda1https://orcid.org/0000-0002-9227-4839Pascal Bouvry2https://orcid.org/0000-0003-4473-8659Data Science and Engineering Laboratory, School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, ThailandData Science and Engineering Laboratory, School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, ThailandFSTC-CSC, SnT, University of Luxembourg, Luxembourg, LuxembourgDue to serving several purposes simultaneously, running scientific workflows on dynamic environments such as cloud computing, has become multi-objective scheduling. Among these purposes, Cost and Makespan are probably the most two primitive objectives. Another critical factor in a large-scale scientific workflow is tremendous amount of data during execution. Therefore, this work also includes Data Movement as an additional objective as it has a major impact on network utilization and energy consumption in network equipment in cloud data center. In considering these three objectives, this work proposes a framework for scheduling solutions which combines a new nodes clustering technique in Directed Acyclic Graph (DAG) model known as Multilevel Dependent Node Clustering (MDNC) and the multi-objective optimization, Extreme Nondominated Sorting Genetic Algorithm-III (E-NSGA-III). E-NSGA-III is the recent extension of Nondominated Sorting Genetic Algorithm (NSGA-III). Five well-known scientific workflows, CyberShake, Epigenomics, LIGO, Montage, and SIPHT are selected as testbeds, while the commonly known Hypervolume is chosen as the performance metric. In this work, MDNC is also experimented with both NSGA-III. Comparison among three approaches, E-NSGA-III alone, E-NSGA-III with Peer-to-Peer clustering and E-NSGA-III with MDNC are carried out. The superiority of the proposed framework among them and its limitation are discussed.https://ieeexplore.ieee.org/document/8924641/Cloud computingcostdata movementdirected acyclic graph (DAG)extreme nondominated sorting genetic algorithm-III (E-NSGA-III)makespan |
| spellingShingle | Peerasak Wangsom Kittichai Lavangnananda Pascal Bouvry Multi-Objective Scientific-Workflow Scheduling With Data Movement Awareness in Cloud IEEE Access Cloud computing cost data movement directed acyclic graph (DAG) extreme nondominated sorting genetic algorithm-III (E-NSGA-III) makespan |
| title | Multi-Objective Scientific-Workflow Scheduling With Data Movement Awareness in Cloud |
| title_full | Multi-Objective Scientific-Workflow Scheduling With Data Movement Awareness in Cloud |
| title_fullStr | Multi-Objective Scientific-Workflow Scheduling With Data Movement Awareness in Cloud |
| title_full_unstemmed | Multi-Objective Scientific-Workflow Scheduling With Data Movement Awareness in Cloud |
| title_short | Multi-Objective Scientific-Workflow Scheduling With Data Movement Awareness in Cloud |
| title_sort | multi objective scientific workflow scheduling with data movement awareness in cloud |
| topic | Cloud computing cost data movement directed acyclic graph (DAG) extreme nondominated sorting genetic algorithm-III (E-NSGA-III) makespan |
| url | https://ieeexplore.ieee.org/document/8924641/ |
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