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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Vydané v:2025 4th International Conference on Computational Modelling, Simulation and Optimization (ICCMSO) s. 388 - 393
Hlavní autori: Narwal, Abhikriti, Dhingra, Sunita
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 20.06.2025
Predmet:
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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Shrnutí: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.
DOI:10.1109/ICCMSO67468.2025.00074