5g-Enabled DevOps: Revolutionizing Integrated Communication And Networking Technologies For Faster Software Delivery

Authors

  • Prudhvi Singirikonda Independent Researcher

DOI:

https://doi.org/10.36676/jrps.v15.i1.1540

Keywords:

5G Technology, IoT, DevOps, CI/CD, Software Delivery Acceleration

Abstract

This paper discusses how integrating 5G and IoT technologies can improve the DevOps process regarding communication and networking to foster efficient software delivery. The strengths of 5G are expected to provide interfaces for IoT devices and ensure high-speed, low-latency networks that will bring responses to the existing DevOps challenges, including longer durations of continuous integration and deployment. Exploiting the benefits provided by 5G connectivity and IoT for real-time data processing, one can get more robust, functional, and adaptive DevOps settings. The results suggest that integrating 5G and IoT into DevOps will not only increase the speed of software delivery but also improve overall system performance and open doors to novel solutions for heightened, built-in agile software development and deployment in complex and highly distributed systems. Therefore, this paper aims to demonstrate the possibility of innovation enabled by using 5G and IoT to improve the efficiency and resilience of the DevOps processes, making it possible for technologies and business requirements to advance rapidly in the digital age.

References

Alliance, N. G. M. N. (2016). Perspectives on vertical industries and implications for 5G. White Paper, Jun. https://ngmn.org/wp-content/uploads/Publications/2016/160922_NGMN_Perspectives_on_Vertical_Industries_and_Implications_for_5G.pdf

Mallreddy, S. R., & Vasa, Y. (2023). Predictive Maintenance In Cloud Computing And Devops: Ml Models For Anticipating And Preventing System Failures. NVEO-NATURAL VOLATILES & ESSENTIAL OILS Journal| NVEO, 10(1), 213-219. DOI: https://doi.org/10.53555/nveo.v10i1.5751

Mallreddy, S. R., & Vasa, Y. (2023). Natural language querying in SIEM systems: Bridging the gap between security analysts and complex data. NATURAL LANGUAGE QUERYING IN SIEM SYSTEMS: BRIDGING THE GAP BETWEEN SECURITY ANALYSTS AND COMPLEX DATA, 10(1), 205–212. https://doi.org/10.53555/nveo.v10i1.5750 DOI: https://doi.org/10.53555/nveo.v10i1.5750

Vasa, Y., Mallreddy, S. R., & Jami, V. S. (2022). AUTOMATED MACHINE LEARNING FRAMEWORK USING LARGE LANGUAGE MODELS FOR FINANCIAL SECURITY IN CLOUD OBSERVABILITY. International Journal of Research and Analytical Reviews , 9(3), 183–190.

Vasa, Y., Singirikonda, P., & Mallreddy, S. R. (2023). AI Advancements in Finance: How Machine Learning is Revolutionizing Cyber Defense. International Journal of Innovative Research in Science, Engineering and Technology, 12(6), 9051–9060.

Vasa, Y., & Singirikonda, P. (2022). Proactive Cyber Threat Hunting With AI: Predictive And Preventive Strategies. International Journal of Computer Science and Mechatronics, 8(3), 30–36.

Vasa, Y., Mallreddy, S. R., & Jaini, S. (2023). AI And Deep Learning Synergy: Enhancing Real-Time Observability And Fraud Detection In Cloud Environments, 6(4), 36–42. https://doi.org/ 10.13140/RG.2.2.12176.83206

Katikireddi, P. M., Singirikonda, P., & Vasa, Y. (2021). Revolutionizing DEVOPS with Quantum Computing: Accelerating CI/CD pipelines through Advanced Computational Techniques. Innovative Research Thoughts, 7(2), 97–103. https://doi.org/10.36676/irt.v7.i2.1482 DOI: https://doi.org/10.36676/irt.v7.i2.1482

Vasa, Y., Cheemakurthi, S. K. M., & Kilaru, N. B. (2022). Deep Learning Models For Fraud Detection In Modernized Banking Systems Cloud Computing Paradigm. International Journal of Advances in Engineering and Management, 4(6), 2774–2783. https://doi.org/10.35629/5252-040627742783

Vasa, Y., Kilaru, N. B., & Gunnam, V. (2023). Automated Threat Hunting In Finance Next Gen Strategies For Unrivaled Cyber Defense. International Journal of Advances in Engineering and Management, 5(11). https://doi.org/10.35629/5252-0511461470

Vasa, Y., & Mallreddy, S. R. (2022). Biotechnological Approaches To Software Health: Applying Bioinformatics And Machine Learning To Predict And Mitigate System Failures. Natural Volatiles & Essential Oils, 9(1), 13645–13652. https://doi.org/https://doi.org/10.53555/nveo.v9i2.5764

Mallreddy, S. R., & Vasa, Y. (2022). Autonomous Systems In Software Engineering: Reducing Human Error In Continuous Deployment Through Robotics And AI. NVEO - Natural Volatiles & Essential Oils, 9(1), 13653–13660. https://doi.org/https://doi.org/10.53555/nveo.v11i01.5765

Vasa, Y., Jaini, S., & Singirikonda, P. (2021). Design Scalable Data Pipelines For Ai Applications. NVEO - Natural Volatiles & Essential Oils, 8(1), 215–221. https://doi.org/https://doi.org/10.53555/nveo.v8i1.5772 DOI: https://doi.org/10.53555/nveo.v8i1.5772

Singirikonda, P., Jaini, S., & Vasa, Y. (2021). Develop Solutions To Detect And Mitigate Data Quality Issues In ML Models. NVEO - Natural Volatiles & Essential Oils, 8(4), 16968–16973. https://doi.org/https://doi.org/10.53555/nveo.v8i4.5771 DOI: https://doi.org/10.53555/nveo.v8i4.5771

Vasa, Y. (2021). Develop Explainable AI (XAI) Solutions For Data Engineers. NVEO - Natural Volatiles & Essential Oils, 8(3), 425–432. https://doi.org/https://doi.org/10.53555/nveo.v8i3.5769 DOI: https://doi.org/10.53555/nveo.v8i3.5769

Sukender Reddy Mallreddy. (2023). ENHANCING CLOUD DATA PRIVACY THROUGH FEDERATED LEARNING: A DECENTRALIZED APPROACH TO AI MODEL TRAINING. IJRDO -Journal of Computer Science Engineering, 9(8), 15-22. DOI: https://doi.org/10.53555/cse.v9i8.6131

Mallreddy, S.R., Nunnaguppala, L.S.C., & Padamati, J.R. (2022). Ensuring Data Privacy with CRM AI: Investigating Customer Data Handling and Privacy Regulations. ResMilitaris. Vol.12(6). 3789-3799

Nunnagupala, L. S. C. ., Mallreddy, S. R., & Padamati, J. R. . (2022). Achieving PCI Compliance with CRM Systems. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 13(1), 529–535. DOI: https://doi.org/10.61841/turcomat.v13i1.14689

Jangampeta, S., Mallreddy, S.R., & Padamati, J.R. (2021). Anomaly Detection for Data Security in SIEM: Identifying Malicious Activity in Security Logs and User Sessions. 10(12), 295-298

Jangampeta, S., Mallreddy, S. R., & Padamati, J. R. (2021). Data Security: Safeguarding the Digital Lifeline in an Era of Growing Threats. International Journal for Innovative Engineering and Management Research, 10(4), 630-632.

Sukender Reddy Mallreddy(2020).Cloud Data Security: Identifying Challenges and Implementing Solutions.JournalforEducators,TeachersandTrainers,Vol.11(1).96 -102.

Nunnaguppala, L. S. C. , Sayyaparaju, K. K., & Padamati, J. R.. (2021). "Securing The Cloud: Automating Threat Detection with SIEM, Artificial Intelligence & Machine Learning", International Journal For Advanced Research In Science & Technology, Vol 11 No 3, 385-392

Padamati, J., Nunnaguppala, L., & Sayyaparaju, K. . (2021). "Evolving Beyond Patching: A Framework for Continuous Vulnerability Management", Journal for Educators, Teachers and Trainers, 12(2), 185-193.

Nunnaguppala, L. S. C. . (2021). "Leveraging AI In Cloud SIEM And SOAR: Real-World Applications For Enhancing SOC And IRT Effectiveness", International Journal for Innovative Engineering and Management Research,10(08), 376-393

Sayyaparaju, K. K., Nunnaguppala, L. S. C. , & Padamati, J. R.. (2021). "Building SecureAI/ML Pipelines: Cloud Data Engineeringfor Compliance and Vulnerability Management", International Journal for Innovative Engineering and Management Research,10(10), 330-340

Downloads

Published

30-03-2024

How to Cite

Prudhvi Singirikonda. (2024). 5g-Enabled DevOps: Revolutionizing Integrated Communication And Networking Technologies For Faster Software Delivery. International Journal for Research Publication and Seminar, 15(1), 233–241. https://doi.org/10.36676/jrps.v15.i1.1540