A VIRTUAL DATA CENTER COMPARISON OF DIFFERENT FIREWALLS' PERFORMANCE
DOI:
https://doi.org/10.36755/jac.v1i1.46Keywords:
Communication Connection, Data center , Firewall Virtual machineAbstract
Whether virtual or real, every data centre depends on its network, and the firewall is a crucial component of that network for secure communication. Different types of firewalls, such as software firewalls, physical firewalls, virtual appliance firewalls, and kernel-integrated firewalls, can secure data centre connections. When choosing a firewall, there are many things to take into account, especially in a virtualized data centre where each firewall behaves differently depending on the situation. Reduced costs, effective administration extensibility, greater resource usage, scalability, and energy resilience are just a few benefits that virtualized data centres are supposed to produce. In this study, the use of firewalls is examined in relation to virtualized data centres. The effectiveness of several kinds of firewalls, including virtual firewalls, physical firewalls, and software .The performance of various types of firewalls, such as software firewalls, physical firewalls, virtual appliance firewalls, and kernel-integrated firewalls, is being analysed. Virtual data centre firewall implementation and performance comparisons explain how to design and which firewall type provides the best performance. In all conditions, it was shown that kernel-integrated firewalls worked properly. Virtual machine IP addresses and networks can vary, and the kernel-based firewall can dynamically update its rules to keep pace with such changes.
Downloads
References
[1] Haapala, H., Nurkka, P., Kaustell, K., Mattila, T., & Suutarinen, J. (2006). Usability as a challenge in agricultural engineering. Suomen Maataloustieteellisen Seuran Tiedote 21, 1–7. DOI: https://doi.org/10.33354/smst.76058
[2] Goyal, S., Morita, P., Lewis, G. F., Yu, C., Seto, E., & Cafazzo, J. A. (2016). The systematic design of a behavioural mobile health application for the self-management of type 2 diabetes. Canadian journal of diabetes, 40(1), 95-104. DOI: https://doi.org/10.1016/j.jcjd.2015.06.007
[3] Gawade, S., Raikar, K., & Chopade, S. (2017). Usability evaluation of agricultural websites. Paper presented at the 4th International Conference on “Computing for Sustainable Global Development”(INDIACom-2017), Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi, 136-141.
[4] Garcia, E., Martin, C., Garcia, A., Harrison, R., & Flood, D. (2011). Systematic analysis of mobile diabetes management applications on different platforms. Paper presented at the Symposium of the Austrian HCI and Usability Engineering Group, 7058, 379-396. DOI: https://doi.org/10.1007/978-3-642-25364-5_27
[5] Gao, C., Zhou, L., Liu, Z., Wang, H., & Bowers, B. (2017). Mobile application for diabetes self-management in China: Do they fit for older adults? International journal of medical informatics, 101, 68-74. DOI: https://doi.org/10.1016/j.ijmedinf.2017.02.005
[6] Costopoulou, C., Ntaliani, M., & Karetsos, S. (2016). Studying mobile apps for agriculture. Journal of Mobile Computing & Application 3(6), 44-49.
[7] Alam, T., Ullah, A., & Benaida, M. (2022). Deep reinforcement learning approach for computation offloading in blockchain-enabled communications systems. Journal of Ambient Intelligence and Humanized Computing, 1-14.
[8] Ouhame, S., Hadi, Y., & Ullah, A. (2021). An efficient forecasting approach for resource utilization in cloud data center using CNN-LSTM model. Neural Computing and Applications, 33(16), 10043-10055.
[9] Sebai, D., & Shah, A. U. (2022). Semantic-oriented learning-based image compression by Only-Train-Once quantized autoencoders. Signal, Image and Video Processing, 1-9.
[10] Alam, T., Ullah, A., & Benaida, M. (2022). Deep reinforcement learning approach for computation offloading in blockchain-enabled communications systems. Journal of Ambient Intelligence and Humanized Computing, 1-14. DOI: https://doi.org/10.1007/s12652-021-03663-2
[11] Ullah, A., & Nawi, N. M. (2021). An improved in tasks allocation system for virtual machines in cloud computing using HBAC algorithm. Journal of Ambient Intelligence and Humanized Computing, 1-14. DOI: https://doi.org/10.1007/s12652-021-03496-z
[12] Ouhame, S., Hadi, Y., & Ullah, A. (2021). An efficient forecasting approach for resource utilization in cloud data center using CNN-LSTM model. Neural Computing and Applications, 33(16), 10043-10055.
[13] Bari, M. F., Boutaba, R., Esteves, R., Granville, L. Z., Podlesny, M., Rabbani, M. G., ... & Zhani, M. F. (2012). Data center network virtualization: A survey. IEEE communications surveys & tutorials, 15(2), 909-928. DOI: https://doi.org/10.1109/SURV.2012.090512.00043
[14] Beck, K. F., & Hämäläinen, J. (2022). Mapping the field of international comparative research in school social work. International Social Work, 65(2), 203-223. DOI: https://doi.org/10.1177/0020872819897742
[15] Beerbaum, D. O. (2021). Applying Agile Methodology to regulatory compliance projects in the financial industry: A case study research. Available at SSRN (4)26, 3834205. DOI: https://doi.org/10.2139/ssrn.3834205
[16] Bell, E., & Bryman, A. (2007). The ethics of management research: an exploratory content analysis. British journal of management, 18(1), 63-77. DOI: https://doi.org/10.1111/j.1467-8551.2006.00487.x
[17] Bodei, C., Degano, P., Galletta, L., Focardi, R., Tempesta, M., & Veronese, L. (2018). Language-independent synthesis of firewall policies. In 2018 Ieee European Symposium On Security And Privacy (Euros&P), 24th to 26th April,London,UK, (pp:1791-5587). DOI: https://doi.org/10.1109/EuroSP.2018.00015
[18] Caiazzi, T., Scazzariello, M., Alberro, L., Ariemma, L., Castro, A., Grampin, E., & Di Battista, G. (2022). Sibyl: a Framework for Evaluating the Implementation of Routing Protocols in Fat-Trees. In NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium Conference, 25th to 29th April, Budapest, Hungry,(pp:217811). DOI: https://doi.org/10.1109/NOMS54207.2022.9789876
[19] Ouhame, S., Hadi, Y., & Ullah, A. (2021). An efficient forecasting approach for resource utilization in cloud data center using CNN-LSTM model. Neural Computing and Applications, 33(16), 10043-10055.
[20] Ouhame, S., Hadi, Y., & Ullah, A. (2021). An efficient forecasting approach for resource utilization in cloud data center using CNN-LSTM model. Neural Computing and Applications, 33(16), 10043-10055.
[21] Ouhame, S., Hadi, Y., & Ullah, A. (2021). An efficient forecasting approach for resource utilization in cloud data center using CNN-LSTM model. Neural Computing and Applications, 33(16), 10043-10055. DOI: https://doi.org/10.1007/s00521-021-05770-9
[22] Sebai, D., & Shah, A. U. (2022). Semantic-oriented learning-based image compression by Only-Train-Once quantized autoencoders. Signal, Image and Video Processing, 1-9. DOI: https://doi.org/10.1007/s11760-022-02231-1
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Hanane Aznaoui, Canan Batur Şahin (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.