Evaluating SimGrid and CloudSim Plus for Hybrid Cloud Scientific Workflows
Resumo
Hybrid cloud, integrating public and private clouds, presents a promising environment for scientific applications by combining scalability with cost efficiency. However, the complexity of these environments requires tools to support infrastructure planning and optimization prior to actual deployment. This study evaluates two simulators: SimGrid and CloudSim Plus. The focus is on assessing their suitability for simulating the execution of scientific applications within hybrid cloud environments; particularly regarding scalability. Scientific workflows modeled as directed acyclic graph, were used to evaluate a hybrid cloud infrastructure, and both simulators were assessed on workload representation, cloud infrastructure, and scheduling strategies. The results suggest that SimGrid offers greater flexibility in network modeling, while CloudSim Plus excels in resource allocation policy simulation. This comparative analysis aims to help researchers select the most appropriate tool for simulating and optimizing scientific applications and cloud scheduling strategies. Future research should explore the integration of emerging technologies, such as containers and microservices, within these simulators.
Palavras-chave:
cloud computing, scientific workflow, hybrid cloud
Referências
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Casanova, H.; Legrand, A.; Quinson, M. SimGrid: A Generic Framework for Large-Scale Distributed Experiments. In: TENTH International Conference on Computer Modeling and Simulation (uksim 2008). 2008. P. 126–131. DOI: 10.1109/UKSIM.2008.28.
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Mell, P. M.; Grance, T. SP 800-145. The NIST Definition of Cloud Computing. Gaithersburg, MD, USA, 2011.
Sotomayor, B. et al. Virtual Infrastructure Management in Private and Hybrid Clouds. IEEE Internet Computing, v. 13, n. 5, p. 14–22, 2009. ISSN 1089-7801. DOI: 10.1109/MIC.2009.119. Available from: [link]. Visited on: 18 Mar. 2024.
Mansouri, Y.; Prokhorenko, V.; Babar, M. A. An automated implementation of hybrid cloud for performance evaluation of distributed databases. Journal of Network and Computer Applications, v. 167, p. 102740, 2020. ISSN 1084-8045. DOI: 10.1016/j.jnca.2020.102740. Available from: [link].
Maarouf, A.; Marzouk, A.; Haqiq, A. Comparative Study of Simulators for Cloud Computing. In: 2015 INTERNATIONAL CONFERENCE ON CLOUD TECHNOLOGIES AND APPLICATIONS (CLOUDTECH). 2015 International Conference on Cloud Technologies and Applications (CloudTech). Marrakech, Morocco: IEEE, 2015. P. 1–8. ISBN 978-1-4673-8149-9. DOI: 10.1109/CloudTech.2015.7336989. Available from: [link]. Visited on: 22 Feb. 2024.
Khan, S. U.; Ullah, N. Challenges in the Adoption of Hybrid Cloud: An Exploratory Study Using Systematic Literature Review. The Journal of Engineering, v. 2016, n. 5, p. 107–118, 2016. ISSN 2051-3305, 2051-3305. DOI: 10.1049/joe.2016.0089. Available from: [link]. Visited on: 24 May 2024.
Buyya, R. et al. Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility. Future Generation Computer Systems, v. 25, n. 6, p. 599–616, 2009. ISSN 0167739X. DOI: 10.1016/j.future.2008.12.001. Available from: [link]. Visited on: 31 Dec. 2023.
Lata, S.; Singh, D. Cloud simulation tools: A survey. In: AIP PUBLISHING, 1. AIP Conference Proceedings. 2022. v. 2555.
Shahid, M. A. et al. A Systematic Survey of Simulation Tools for Cloud and Mobile Cloud Computing Paradigm. Journal of Independent Studies and Research Computing, 2022. Available from: [link].
Casanova, H.; Legrand, A.; Quinson, M. SimGrid: A Generic Framework for Large-Scale Distributed Experiments. In: TENTH International Conference on Computer Modeling and Simulation (uksim 2008). 2008. P. 126–131. DOI: 10.1109/UKSIM.2008.28.
Silva Filho, M. C. et al. CloudSim Plus: A Cloud Computing Simulation Framework Pursuing Software Engineering Principles for Improved Modularity, Extensibility and Correctness. In: 2017 IFIP/IEEE SYMPOSIUM ON INTEGRATED NETWORK AND SERVICE MANAGEMENT (IM). 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). Lisbon: IEEE, 2017. P. 400–406. ISBN 978-3-901882-89-0. DOI: 10.23919/INM.2017.7987304. Available from: [link]. Visited on: 31 Dec. 2023.
Silva Filho, M. C.; Rodrigues, J. J. P. C. Human Readable Scenario Specification for Automated Creation of Simulations on CloudSim. In: Hsu, R. C.-H.; Wang, S. (Eds.). Internet of Vehicles – Technologies and Services. Cham: Springer International Publishing, 2014. P. 345–356. ISBN 978-3-319-11167-4.
Dabek, F. et al. Vivaldi: A decentralized network coordinate system. ACM SIGCOMM Computer Communication Review, ACM New York, NY, USA, v. 34, n. 4, p. 15–26, 2004.
Jacob, J. C. et al. Montage: An astronomical image mosaicking toolkit. Astrophysics Source Code Library, ascl–1010, 2010.
Mansouri, N.; Ghafari, R.; Zade, B. M. H. Cloud Computing Simulators: A Comprehensive Review. Simulation Modelling Practice and Theory, v. 104, p. 102144, 2020. ISSN 1569190X. DOI: 10.1016/j.simpat.2020.102144. Available from: [link]. Visited on: 31 Dec. 2023.
Fakhfakh, F.; Kacem, H. H.; Kacem, A. H. Simulation Tools for Cloud Computing: A Survey and Comparative Study. In: 2017 IEEE/ACIS 16TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS). 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS). Wuhan, China: IEEE, 2017. P. 221–226. ISBN 978-1-5090-5507-4. DOI: 10.1109/ICIS.2017.7959997. Available from: [link]. Visited on: 5 Feb. 2024.
Publicado
27/11/2024
Como Citar
SOARES, João Antonio; AMARAL, Rafael Burlamaqui; CAVALHEIRO, Gerson.
Evaluating SimGrid and CloudSim Plus for Hybrid Cloud Scientific Workflows. In: ESCOLA REGIONAL DE REDES DE COMPUTADORES (ERRC), 21. , 2024, Rio Grande/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 25-29.
DOI: https://doi.org/10.5753/errc.2024.4590.
