A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments

By Suraj Pandey, Linlin Wu, Siddeswara Mayura Guru, Rajkumar Buyya. 2010 24th IEEE International Conference on Advanced Information Networking and Applications, 2010

953 citations recorded by the source corpus.

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A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges (2016, methodology): “[85] proposed PSO based RSA to schedule the resources to the workloads by considering data transmission and computation cost.”
The Internet of Things, Fog and Cloud Continuum: Integration and Challenges (2018, background): “Common objectives in application scheduling are minimization of the execution time [54] and minimization of costs [9], as cloud computing services charge on a pay-per-use basis.”
Deadline Based Resource Provisioningand Scheduling Algorithm for Scientific Workflows on Clouds (2014, neutral): “Despite the fact that their heuristic is able to handle heterogeneous resources, just as Pandey et al. [15] , it assumes an initial set of VMs is available beforehand and hence lacks in utilizing the elasticity of IaaS clouds.”
Cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds (2012, background): “Other approaches [18], [19] use metaheuristics that usually run for a long time before producing good results, which makes them less useful in the scenarios we consider in this paper.”
Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches (2015, methodology): “PSO [Pandey et al. 2010] Cost Considers both computation cost and data transmission cost.”