Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing
Task scheduling is one of the core issues in cloud computing. Tasks are heterogeneous, and they have intensive computational requirements. Tasks need to be scheduled on Virtual Machines (VMs), which are resources in a cloud environment. Due to the immensity of search space for possible mappings of t...
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| Format: | Article |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/14/6244 |
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| author | Rohail Gulbaz Abdul Basit Siddiqui Nadeem Anjum Abdullah Alhumaidi Alotaibi Turke Althobaiti Naeem Ramzan |
| author_facet | Rohail Gulbaz Abdul Basit Siddiqui Nadeem Anjum Abdullah Alhumaidi Alotaibi Turke Althobaiti Naeem Ramzan |
| author_sort | Rohail Gulbaz |
| collection | DOAJ |
| description | Task scheduling is one of the core issues in cloud computing. Tasks are heterogeneous, and they have intensive computational requirements. Tasks need to be scheduled on Virtual Machines (VMs), which are resources in a cloud environment. Due to the immensity of search space for possible mappings of tasks to VMs, meta-heuristics are introduced for task scheduling. In scheduling makespan and load balancing, Quality of Service (QoS) parameters are crucial. This research contributes a novel load balancing scheduler, namely Balancer Genetic Algorithm (BGA), which is presented to improve makespan and load balancing. Insufficient load balancing can cause an overhead of utilization of resources, as some of the resources remain idle. BGA inculcates a load balancing mechanism, where the actual load in terms of million instructions assigned to VMs is considered. A need to opt for multi-objective optimization for improvement in load balancing and makespan is also emphasized. Skewed, normal and uniform distributions of workload and different batch sizes are used in experimentation. BGA has exhibited significant improvement compared with various state-of-the-art approaches for makespan, throughput and load balancing. |
| first_indexed | 2024-03-10T09:47:02Z |
| format | Article |
| id | doaj.art-84e510eddb684420b405c7af64dd39c6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:47:02Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-84e510eddb684420b405c7af64dd39c62023-11-22T03:06:29ZengMDPI AGApplied Sciences2076-34172021-07-011114624410.3390/app11146244Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud ComputingRohail Gulbaz0Abdul Basit Siddiqui1Nadeem Anjum2Abdullah Alhumaidi Alotaibi3Turke Althobaiti4Naeem Ramzan5Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad 44000, PakistanDepartment of Computer Science, Capital University of Science and Technology, Islamabad 44000, PakistanDepartment of Computer Science, Capital University of Science and Technology, Islamabad 44000, PakistanDepartment of Science and Technology, College of Ranyah, Taif University, Taif 11099, Saudi ArabiaFaculty of Science, Northern Border University, Arar 1321, Saudi ArabiaSchool of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UKTask scheduling is one of the core issues in cloud computing. Tasks are heterogeneous, and they have intensive computational requirements. Tasks need to be scheduled on Virtual Machines (VMs), which are resources in a cloud environment. Due to the immensity of search space for possible mappings of tasks to VMs, meta-heuristics are introduced for task scheduling. In scheduling makespan and load balancing, Quality of Service (QoS) parameters are crucial. This research contributes a novel load balancing scheduler, namely Balancer Genetic Algorithm (BGA), which is presented to improve makespan and load balancing. Insufficient load balancing can cause an overhead of utilization of resources, as some of the resources remain idle. BGA inculcates a load balancing mechanism, where the actual load in terms of million instructions assigned to VMs is considered. A need to opt for multi-objective optimization for improvement in load balancing and makespan is also emphasized. Skewed, normal and uniform distributions of workload and different batch sizes are used in experimentation. BGA has exhibited significant improvement compared with various state-of-the-art approaches for makespan, throughput and load balancing.https://www.mdpi.com/2076-3417/11/14/6244task schedulingcloud computingVirtual Machinesload balancingoptimization |
| spellingShingle | Rohail Gulbaz Abdul Basit Siddiqui Nadeem Anjum Abdullah Alhumaidi Alotaibi Turke Althobaiti Naeem Ramzan Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing Applied Sciences task scheduling cloud computing Virtual Machines load balancing optimization |
| title | Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing |
| title_full | Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing |
| title_fullStr | Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing |
| title_full_unstemmed | Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing |
| title_short | Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing |
| title_sort | balancer genetic algorithm a novel task scheduling optimization approach in cloud computing |
| topic | task scheduling cloud computing Virtual Machines load balancing optimization |
| url | https://www.mdpi.com/2076-3417/11/14/6244 |
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