COMPARISON OF DIFFERENT FIREWALLS PERFORMANCE IN A VIRTUAL FOR CLOUD DATA CENTER
DOI:
https://doi.org/10.36755/jac.v1i1.49Keywords:
Data centre Performance, firewall , Appliance virtualized data , Firewal machineAbstract
Every data centre, whether virtual or physical, relies on its network, and the firewall is an essential part of that network for safe communication. Data centre connection can be protected by a variety of firewall types, including software firewalls, physical firewalls, virtual appliance firewalls, and kernel-integrated firewalls. There are several factors to consider when selecting a firewall, especially in a virtualized data centre, where each firewall works differently in different situations. Virtualized data centres are intended to yield lower budgets, efficient management extensibility, better utilization of available resources, scalability, and energy resilience, among several other advantages. Virtualized data centres are the topic of this study, which examines the application of firewalls. 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. With its distributed firewall functionality, virtual machines can travel across hypervisors with no disruption to their security settings, if their policies remain the same. A kernel-based, distributed firewall is the best way to protect against viruses.
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[1] Afamugat, N. (2022). Building an ethical hacking environment.Metropolia 2(24),1-53.
[2] Agbenyegah, F. K., & Asante, M. (2017). Impact of firewall on network performance. International Journal of Scientific & Technology Research, 6(3), 32-38.
[3] Ahmadin, M. (2022). Social Research Methods: Qualitative and Quantitative Approaches. Jurnal Kajian Sosial dan Budaya: Tebar Science, 6(1), 104-113.
[4] Akhtar, D. M. I. (2016). Research design. Research Design 18(1),1-17. DOI: https://doi.org/10.2139/ssrn.2862445
[5] 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
[6] Aldribi, A., Traoré, I., Moa, B., & Nwamuo, O. (2020). Hypervisor-based cloud intrusion detection through online multivariate statistical change tracking. Computers & Security, 88(3), 101646. DOI: https://doi.org/10.1016/j.cose.2019.101646
[7] Alfayyadh, B., Ponting, J., Alzomai, M., & Jøsang, A. (2010). Vulnerabilities in personal firewalls caused by poor security usability. In 2010 IEEE International Conference on Information Theory and Information Security, 17th to 19th December,Beijing, China,(pp:1-50). DOI: https://doi.org/10.1109/ICITIS.2010.5689490
[8] Alhasan, A. J., & Surantha, N. (2021). Evaluation of Data Center Network Security based on Next-Generation Firewall. International Journal of Advanced Computer Science and Applications, 12(9),518-525. DOI: https://doi.org/10.14569/IJACSA.2021.0120958
[9] Amin, H. J. (2021). Effect of Entrepreneurial Marketing Dimensions on Small and Medium Enterprises Performance in Nasarawa State. Economics and Business Quarterly Reviews, 4(2),1-14. DOI: https://doi.org/10.31014/aior.1992.04.02.356
[10] Anwar, R. W., Abdullah, T., & Pastore, F. (2021). Firewall Best Practices for Securing Smart Healthcare Environment: A Review. Applied Sciences, 11(19), 9183. DOI: https://doi.org/10.3390/app11199183
[11] Balaji, K., Sai Kiran, P., & Sunil Kumar, M. (2022). Power aware virtual machine placement in IaaS cloud using discrete firefly algorithm. Applied Nanoscience,73(3) 1-9. DOI: https://doi.org/10.1007/s13204-021-02337-x
[12] 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
[13] 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
[14] 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
[15] 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
[16] 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
[17] 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
[18] 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.
[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. DOI: https://doi.org/10.1007/s00521-021-05770-9
[21] 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
[22] 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
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