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

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

Abstract

Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the `execution time'. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We compare the cost savings when using PSO and existing `Best Resource Selection' (BRS) algorithm. Our results show that PSO can achieve: (a) as much as 3 times cost savings as compared to BRS, and (b) good distribution of workload onto resources.

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Metadata

Authors
Pandey, S., Wu, L., Guru, S. M., & Buyya, R.
Venue
2010 24th IEEE International Conference on Advanced Information Networking and Applications
Publisher
IEEE
Published
2010
Pages
400-407
OpenAlex
W1970275772
Cited by
880
References
0
FWCI
27.66
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments. https://app.cochat.ai/papers/10.1109/aina.2010.31
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Pandey, S., Wu, L., Guru, S. M., & Buyya, R. (2010). A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments. 2010 24th IEEE International Conference on Advanced Information Networking and Applications. https://doi.org/10.1109/aina.2010.31

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.