GEP optimization for load balancing of virtual machines (LBVM) in cloud computing
Cloud computing relies heavily on load balancing to distribute workloads evenly among servers, network connections, and drives. The cloud system has been assigned some load which can be underloaded, overloaded, or balanced depending on the cloud architecture and user requests. An important component...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917424000527 |
| _version_ | 1797259684936155136 |
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| author | G. Muneeswari Jhansi Bharathi Madavarapu R. Ramani C. Rajeshkumar C. John Clement Singh |
| author_facet | G. Muneeswari Jhansi Bharathi Madavarapu R. Ramani C. Rajeshkumar C. John Clement Singh |
| author_sort | G. Muneeswari |
| collection | DOAJ |
| description | Cloud computing relies heavily on load balancing to distribute workloads evenly among servers, network connections, and drives. The cloud system has been assigned some load which can be underloaded, overloaded, or balanced depending on the cloud architecture and user requests. An important component of task scheduling in clouds is the load balancing of workloads that may be dependent or independent of virtual machines (VMs). To overcome these drawbacks, a novel Load Balancing of Virtual Machine (LBVM) in Cloud Computing has been proposed in this paper. The input tasks from multiple users were collected in a single task collector and sent towards the load balancer, which contains the deep learning network called the Bi-LSTM technique. When the load is unbalanced, the VM migration will begin by sending the task details to the load balancer. The Bi-LSTM is optimized by a Genetic Expression Programming (GEP) optimizer and finally, it balances the input loads in VMs. The efficiency of the proposed LBVM has been determined using the existing techniques such as MVM, PLBVM, and VMIS in terms of evaluation metrics such as configuration latency, detection rate, accuracy etc. Experimental results shows that the proposed method reduces the Migration Time of 49%, 41.7%, and 17.8% than MVM, PLBVM, VMIS existing techniques respectively. |
| first_indexed | 2024-04-24T23:13:21Z |
| format | Article |
| id | doaj.art-07b583673c434ecca56ca642e9d039a5 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-04-24T23:13:21Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-07b583673c434ecca56ca642e9d039a52024-03-17T07:58:45ZengElsevierMeasurement: Sensors2665-91742024-06-0133101076GEP optimization for load balancing of virtual machines (LBVM) in cloud computingG. Muneeswari0Jhansi Bharathi Madavarapu1R. Ramani2C. Rajeshkumar3C. John Clement Singh4Department School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India; Corresponding author.Department of Information Technology, University of Cumberlands, 6178 College Station Drive, Williamsburg, KY, 40769, USADepartment of Computer Science and Engineering, P.S.R Engineering College, Sivakasi, Tamil Nadu, 626140, IndiaDepartment of Information Technology, Sri Krishna College of Technology, Coimbatore, 641042, Tamilnadu, IndiaElectronics and Communication Engineering, Kings Engineering College, Sriperumbudur, Chennai, 602117, Tamilnadu, IndiaCloud computing relies heavily on load balancing to distribute workloads evenly among servers, network connections, and drives. The cloud system has been assigned some load which can be underloaded, overloaded, or balanced depending on the cloud architecture and user requests. An important component of task scheduling in clouds is the load balancing of workloads that may be dependent or independent of virtual machines (VMs). To overcome these drawbacks, a novel Load Balancing of Virtual Machine (LBVM) in Cloud Computing has been proposed in this paper. The input tasks from multiple users were collected in a single task collector and sent towards the load balancer, which contains the deep learning network called the Bi-LSTM technique. When the load is unbalanced, the VM migration will begin by sending the task details to the load balancer. The Bi-LSTM is optimized by a Genetic Expression Programming (GEP) optimizer and finally, it balances the input loads in VMs. The efficiency of the proposed LBVM has been determined using the existing techniques such as MVM, PLBVM, and VMIS in terms of evaluation metrics such as configuration latency, detection rate, accuracy etc. Experimental results shows that the proposed method reduces the Migration Time of 49%, 41.7%, and 17.8% than MVM, PLBVM, VMIS existing techniques respectively.http://www.sciencedirect.com/science/article/pii/S2665917424000527Load balancingCloud computingVirtual machinesBi-LSTMGenetic expression programming |
| spellingShingle | G. Muneeswari Jhansi Bharathi Madavarapu R. Ramani C. Rajeshkumar C. John Clement Singh GEP optimization for load balancing of virtual machines (LBVM) in cloud computing Measurement: Sensors Load balancing Cloud computing Virtual machines Bi-LSTM Genetic expression programming |
| title | GEP optimization for load balancing of virtual machines (LBVM) in cloud computing |
| title_full | GEP optimization for load balancing of virtual machines (LBVM) in cloud computing |
| title_fullStr | GEP optimization for load balancing of virtual machines (LBVM) in cloud computing |
| title_full_unstemmed | GEP optimization for load balancing of virtual machines (LBVM) in cloud computing |
| title_short | GEP optimization for load balancing of virtual machines (LBVM) in cloud computing |
| title_sort | gep optimization for load balancing of virtual machines lbvm in cloud computing |
| topic | Load balancing Cloud computing Virtual machines Bi-LSTM Genetic expression programming |
| url | http://www.sciencedirect.com/science/article/pii/S2665917424000527 |
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