Dynamic scheduling strategies for cloud-based load balancing in parallel and distributed systems

Actual load balancing in parallel and distributed systems ruins a serious task due to workloads’ dynamic nature and resource availability. Existing scheduling procedures continually fail to regulate real-time alterations, leading to suboptimal performance and resource underutilization. Our study val...

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Vydané v:Journal of cloud computing : advances, systems and applications Ročník 14; číslo 1; s. 33 - 25
Hlavný autor: Albalawi, Nasser S.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2025
Springer Nature B.V
SpringerOpen
Predmet:
Cloud computing
Computer Communication Networks
Computer networks
Computer Science
Computer System Implementation
Computer Systems Organization and Communication Networks
Distributed processing
Dynamic loads
Dynamic scheduling strategies
Efficiency
Energy consumption
Genetic algorithms
Information Systems Applications (incl.Internet)
Least Recently Used (LRU)
Least Response Time (LRT)
Load balancing
Optimization
Parallel and distributed systemsf
Performance evaluation
Real time
Regression analysis
Resource management
Resource scheduling
Response time
Round-Robin Allocation with Sunflower Whale Optimization (RRA-SWO)
Scheduling
Search algorithms
Software
Software Engineering/Programming and Operating Systems
Special Purpose and Application-Based Systems
Task scheduling
Workloads
Least Response Time (LRT)
Dynamic scheduling strategies
Parallel and distributed systemsf
Load balancing
Round-Robin Allocation with Sunflower Whale Optimization (RRA-SWO)
Least Recently Used (LRU)
ISSN:2192-113X, 2192-113X
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Popis
Shrnutí:Actual load balancing in parallel and distributed systems ruins a serious task due to workloads’ dynamic nature and resource availability. Existing scheduling procedures continually fail to regulate real-time alterations, leading to suboptimal performance and resource underutilization. Our study validates dynamic and effective load distribution by combining novel systems and optimization techniques to handle these issues. We utilize a comprehensive dynamic scheduling approach in this work to provide efficient load balancing in distributed and parallel systems. In this example, we start by using Round-Robin Allocation with Sunflower Whale Optimization (RRA-SWO) to perform an allocation procedure. The allocation step is followed by the Hybrid Ant Genetic Algorithm (HAGA), which is used to schedule tasks in parallel. The Least Response Time (LRT) technique for the Load Monitoring procedures will be developed once the job scheduling is complete. The Harmony Search Algorithm with Linear Regression (LR-HSA) is then used to do Distributed Computing-based Load Prediction and Adjustment. Alongside ongoing observation, this is carried out. Finally, we use the Least Recently Used (LRU) technique to do dynamic load balancing. Performance evaluations are using CloudSim and NetBeans 12.3, metrics like Packet Delivery Ratio at 98 (%), Average Response Time at 65 (s), Task Success Rate at 95 (%), Memory Utilization Rate at 80 (%), and Throughput at 97 (%) are all analyzed to validate our strategy.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2192-113X
2192-113X
DOI:10.1186/s13677-025-00757-6