Search Results - (( simulation optimization method algorithm ) OR ( using computational cloud algorithm ))
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1
Resource scheduling algorithm with load balancing for cloud service provisioning
Published 2019“…Cloud computing uses scheduling and load balancing for virtualized file sharing in cloud infrastructure. …”
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2
Hybrid Cat Swarm Optimization and Simulated Annealing for Dynamic Task Scheduling on Cloud Computing Environment
Published 2018“…In this study, a Cloud Scalable Multi-Objective Cat Swarm Optimization-based Simulated Annealing algorithm is proposed. …”
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3
Hybrid Henry Gas-Harris Hawks comprehensive-opposition algorithm for task scheduling in cloud computing
Published 2025“…Users can use online data computing services and computational resources from a distance in cloud computing environments. …”
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4
Task scheduling in cloud computing using hybrid genetic algorithm and bald eagle search (GA-BES)
Published 2022“…Task scheduling and resource allocation are essential aspects of cloud computing. This Study proposes task scheduling in cloud computing using a hybrid genetic algorithm, and bald eagle search proposed to solve the task scheduling problem. …”
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5
Multi-objective scientific workflow scheduling algorithm in multi-cloud environment for satisfying QoS requirements
Published 2022“…All the proposed algorithms are simulated using the popular cloud simulator, Workflowsim 1.0. …”
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Task scheduling in cloud computing environment using hybrid genetic algorithm and artificial bee colony
Published 2022“…During the methodology chapter, a comprehensive investigation has been done to ascertain the proposed method that can be adopted such as algorithms involved, project flow, and simulation. …”
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7
Optimising cloud computing performance with an enhanced dynamic load balancing algorithm for superior task allocation
Published 2024“…Evaluations using CloudSim simulations demonstrate that the EDLB algorithm achieves substantial average improvements over benchmark algorithm and the-state-of-the-art algorithm, including a 59.46% reduction in total makespan, a 12.70% reduction in average makespan, a 22.46% reduction in execution time, and a 3.10% increase in resource utilisation. …”
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8
Scheduling scientific workflow using multiobjective algorithm with fuzzy resource utilization in multi-cloud environment
Published 2020“…The provision of resources and services for scientific workflow applications using a multi-cloud architecture and a pay-per-use rule has recently gained popularity within the cloud computing research domain. …”
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HSO: A hybrid swarm optimization algorithm for reducing energy consumption in the cloudlets
Published 2018“…To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. …”
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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Simulated kalman filter (SKF) based image template matching for distance measurement by using stereo vision system
Published 2018“…SKF is compared with conventional algorithms for image template matching which are performance index value (PIM) and correlation by using DC components of image (TMC) and by using power of images (TMP) methods. …”
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12
Deep reinforcement learning based resource allocation strategy in cloud-edge computing system
Published 2024“…In this work, the research focus on the simulation testing of the MAL-DRL algorithm against classical Random Allocation (RA) and singe agent DRL methods. …”
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Deep reinforcement learning based resource allocation strategy in cloud-edge computing system
Published 2024“…In this work, the research focus on the simulation testing of the MAL-DRL algorithm against classical Random Allocation (RA) and singe agent DRL methods. …”
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Independent task scheduling algorithms in fog environments from users’ and service providers’ perspectives: a systematic review
Published 2025“…To address these issues, a substantial amount of work has been done to determine optimal methods to improve QoS using task scheduling algorithms. …”
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Multi-objective optimization for integrated task scheduling and load balancing in fog computing for IoT applications
Published 2025“…By using a task scheduling and offloading method, the FOCS algorithm arranges data according to size and sends it to the appropriate fog nodes. …”
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16
Task scheduling in cloud computing environment using hybrid genetic algorithm and bat algorithm
Published 2022“…Furthermore, CloudSim simulator will be used to evaluate the performance of this algorithm. …”
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17
System program management environment in cloud computing using hybrid Genetic Algorithm and Moth Flame Optimization (GA-MFO)
Published 2022“…In this project, Genetic Algorithms (GA) is combine Moth Flame Optimization (MFO) to improve the cloud computing environment. …”
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18
Task scheduling in cloud computing environment using hybrid of genetic algorithm and naked mole rat algorithm (GA-NMRA)
Published 2022“…Task scheduling and resource allocation are essential aspects of cloud computing. This research proposes task scheduling in cloud computing using a hybrid genetic algorithm and naked mole rat algorithm to solve the task scheduling problem. …”
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19
Performance-aware cost-effective brokering and load balancing algorithms for data center in large scale cloud computing
Published 2015“…It helps organization to reduce computing infrastructure cost. Incloud computing concept, cloud users can use computing resources according to their needs and requirements. …”
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20
Modified workflow scheduling using hybrid PSO-GA algorithm in cloud computing
Published 2019“…The goal of the Hybrid GA-PSO algorithm is to reduce the makespan and cost and balance the load of dependent tasks in cloud computing environments over the heterogonous resources. …”
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