Significance of Load Balancing with Task Scheduling in Cloud Environment

The term "cloud computing" involves working with a group of virtualized PC assets. A cloud can perform a range of functions comprising quick provisioning of physical or virtual machines, supporting repetitive, self-recovering, and highly adaptable programming models, and allowing tasks to...

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Published in:2025 4th International Conference on Computational Modelling, Simulation and Optimization (ICCMSO) pp. 388 - 393
Main Authors: Narwal, Abhikriti, Dhingra, Sunita
Format: Conference Proceeding
Language:English
Published: IEEE 20.06.2025
Subjects:
Cloud computing
Computational modeling
Costs
Distributed Computing
Load Balancing
Load management
Load modeling
Resource management
Resource Utilization
Scheduling
Scheduling algorithms
Task scheduling
Time factors
Virtual machines
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Abstract The term "cloud computing" involves working with a group of virtualized PC assets. A cloud can perform a range of functions comprising quick provisioning of physical or virtual machines, supporting repetitive, self-recovering, and highly adaptable programming models, and allowing tasks to recover from structure equipment/programming failures and rebalance distributions. The concept is to advance work area registration to a support-based stage by leveraging worker groups and huge database at datacentres. Task scheduling with load balancing has become interesting research in the space of cloud computing. By spreading work load across multiple nodes in the cloud system, a superior load balancing algorithm will improve performance and resource usage. The paper compares and contrasts enhanced multi-objective scheduling algorithm (EMOSA), the credit-based scheduling algorithm (CBSA), and the Enhanced Multi-Objective Scheduling algorithm with Load Balancing (EMOSA_LB) to find out the significance of load balancing with scheduling algorithms. Honey Bee based load balancing with scheduling technique ensures improved resource utilization with minimal response time, processing cost and processing time.
AbstractList The term "cloud computing" involves working with a group of virtualized PC assets. A cloud can perform a range of functions comprising quick provisioning of physical or virtual machines, supporting repetitive, self-recovering, and highly adaptable programming models, and allowing tasks to recover from structure equipment/programming failures and rebalance distributions. The concept is to advance work area registration to a support-based stage by leveraging worker groups and huge database at datacentres. Task scheduling with load balancing has become interesting research in the space of cloud computing. By spreading work load across multiple nodes in the cloud system, a superior load balancing algorithm will improve performance and resource usage. The paper compares and contrasts enhanced multi-objective scheduling algorithm (EMOSA), the credit-based scheduling algorithm (CBSA), and the Enhanced Multi-Objective Scheduling algorithm with Load Balancing (EMOSA_LB) to find out the significance of load balancing with scheduling algorithms. Honey Bee based load balancing with scheduling technique ensures improved resource utilization with minimal response time, processing cost and processing time.
Author Narwal, Abhikriti
Dhingra, Sunita
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  givenname: Sunita
  surname: Dhingra
  fullname: Dhingra, Sunita
  email: sunitadhingramdu@rediff.com
  organization: University Institute of Engineering and Technology, Maharshi Dayanand University,Department of Computer Science and Engineering,Rohtak,Haryana,INDIA
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Snippet The term "cloud computing" involves working with a group of virtualized PC assets. A cloud can perform a range of functions comprising quick provisioning of...
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StartPage 388
SubjectTerms Cloud computing
Computational modeling
Costs
Distributed Computing
Load Balancing
Load management
Load modeling
Resource management
Resource Utilization
Scheduling
Scheduling algorithms
Task scheduling
Time factors
Virtual machines
Title Significance of Load Balancing with Task Scheduling in Cloud Environment
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