Papers by Pavan Srikanth Patchamatla
International Journal of Innovative Research in Computer and Communication Engineering (ISSN: 2320-9801), 2019
The increasing adoption of cloud computing has led to the widespread use of virtualization techno... more The increasing adoption of cloud computing has led to the widespread use of virtualization technologies, with Docker containers and Virtual Machines (VMs) emerging as dominant solutions. This study presents a comparative analysis of Docker and VMs in cloud environments, focusing on performance, security, scalability, and deployment efficiency. Empirical benchmarks indicate that Docker outperforms VMs in CPU, memory, and disk I/O performance, primarily due to its lightweight architecture and direct host resource access. However, VMs provide stronger security isolation, making them more suitable for compliance-heavy applications. While Docker scales more efficiently, enabling faster horizontal scaling with Kubernetes, VMs offer better vertical scaling, supporting resource-intensive workloads. Security vulnerabilities in Docker include container escape attacks and kernel exploits, whereas VMs rely on hypervisorbased isolation for enhanced security. Emerging trends such as hybrid cloud models, where Docker runs inside VMs, are gaining traction to balance performance and security requirements. Additionally, advancements in Kata Containers, rootless Docker, and AWS Firecracker are shaping the future of secure containerization. Future research should focus on optimizing energy efficiency, improving security mechanisms, and developing AI-driven orchestration techniques for both Docker and VMs. This study contributes to the ongoing discourse on cloud virtualization, offering insights into when to use Docker, VMs, or a hybrid model based on workload requirements.
International Journal of Innovative Research in Computer and Communication Engineering (ISSN: 2320-9801), 2022
Docker has revolutionized application deployment by providing lightweight, scalable, and efficien... more Docker has revolutionized application deployment by providing lightweight, scalable, and efficient containerization, but its shared kernel architecture introduces challenges in performance and security management. This study explores performance optimization techniques for Docker-based workloads, emphasizing resource management, orchestration, and hybrid deployment models. Using experimental benchmarks and case studies, the research evaluates the effectiveness of tools like cgroups, namespace isolation, Kubernetes, and security integrations such as ZAP and OWASP Dependency Check. Results demonstrate that resource isolation and orchestration optimizations significantly reduce CPU and memory contention, improving workload predictability and scalability. Kubernetes' horizontal autoscaling enhances responsiveness under high-traffic conditions, though proactive scaling strategies such as prescaling pods further minimize latency. Hybrid architectures, including Docker within VMs and microVM solutions like Kata Containers, offer strong isolation without excessive performance penalties, making them ideal for high-security applications. However, challenges in container networking and the overhead of security tools highlight the need for adaptive resource allocation and workload-specific optimizations. Future research directions include leveraging AIdriven resource management, Zero Trust security architectures, and confidential computing to address the growing complexity of containerized environments. This study contributes actionable insights for developers, DevOps engineers, and researchers seeking to enhance the performance, scalability, and security of Docker deployments.
International Journal Of Multidisciplinary Research In Science, Engineering and Technology (IJMRSET), 2020
This paper aims to explore the integration of serverless architectures with Kubernetes and OpenSt... more This paper aims to explore the integration of serverless architectures with Kubernetes and OpenStack to optimize AI workflows. The study evaluates the architecture's scalability, performance, resource utilization, cost efficiency, and energy consumption while addressing challenges such as cold start latency and GPU resource management.The research involves an experimental setup consisting of OpenStack for IaaS, Kubernetes for container orchestration, and serverless frameworks like Knative and OpenFaaS for function execution. AI workflows, including data preprocessing, model training, and real-time inference, were implemented. Performance metrics such as latency, throughput, energy efficiency, and cost were analyzed using tools like Prometheus, Grafana, and TensorFlow Profiler. Comparisons were drawn between serverless and containerized workflows across diverse workload scenarios. The integration demonstrated significant benefits for AI workflows, particularly in real-time inference tasks, with serverless architectures exhibiting better scalability and cost efficiency. Containerized workflows achieved superior GPU utilization and cost performance for batch processing tasks. Serverless workflows reduced energy consumption by up to 25% during idle periods but were impacted by cold start latencies and resource contention during peak workloads. The findings emphasize the transformative potential of serverless-Kubernetes-OpenStack integration, particularly for scalable and energy-efficient AI workflows. However, trade-offs in performance and architectural complexity were noted. The study contributes by optimizing GPU utilization, reducing energy consumption, and supporting hybrid workloads, addressing key gaps in prior research. Recommendations include strategies for latency mitigation, resource orchestration, and workload placement.
CogNenux, 2025
The adoption of Continuous Integration and Continuous Deployment (CI/CD) tools has transformed th... more The adoption of Continuous Integration and Continuous Deployment (CI/CD) tools has transformed the landscape of machine learning (ML) workflows, enabling automation, scalability, and efficiency. This study evaluates the comparative performance of three prominent open-source CI/CD tools\u2014Jenkins, GitHub Actions, and Bitbucket Pipelines\u2014in addressing the unique demands of ML tasks, including hyperparameter tuning, model training, and deployment. Through a systematic analysis, the research explores key parameters such as scalability, usability, and security integration, providing actionable insights into their suitability for diverse organizational contexts. Jenkins, with its extensive customization options, demonstrates flexibility but is hindered by a steep learning curve. GitHub Actions excels in usability and accessibility for smaller teams but requires enhancements to handle large-scale workflows. Bitbucket Pipelines, with Kubernetes integration, emerges as a robust option for resource-intensive tasks, though its documentation and advanced features need refinement. The study highlights critical gaps in existing tools, such as limited scalability for distributed workloads and insufficient integration of advanced security mechanisms like TLS automation. Recommendations for tool selection and future enhancements are provided, emphasizing adaptive pipelines, federated learning workflows, and energy-efficient orchestration. This work contributes to the optimization of CI/CD tools for ML operations, offering a structured framework and practical guidance for practitioners and researchers aiming to deploy secure, scalable, and efficient ML pipelines.
International Journal of Advanced Research in Education and Technology (IJARETY), 2018
The integration of Kubernetes with OpenStack offers a promising approach for deploying scalable a... more The integration of Kubernetes with OpenStack offers a promising approach for deploying scalable and efficient AI workflows in multi tenant cloud environments. This study evaluates the performance, scalability, and security implications of this integration, focusing on bare metal, virtual machines (VMs), and containers as resource provisioning methods. The objectives include designing a unified framework combining Kubernetes' orchestration capabilities with OpenStack's infrastructure provisioning, assessing resource optimization strategies, and proposing best practices for AI workload management. The methodology involves benchmarking CPU, memory, disk I/O, and network performance using tools such as PXZ, Sysbench, and Netperf. The study also examines the scalability and security of multi tenant deployments, leveraging Kubernetes namespaces, GPU sharing, and Neutron overlays. Key metrics include latency, throughput, and cost effectiveness. The results highlight that bare metal environments provide the highest raw performance, while containers achieve a balance between efficiency and scalability. VMs offer robust isolation but incur significant overheads. Scalability analysis demonstrates containers' superior resource utilization and cost effectiveness, whereas bare metal systems struggle with underutilization in multi tenant setups. The discussion addresses challenges such as network overheads and resource contention, proposing solutions like optimizing Kubernetes Neutron configurations and adopting advanced GPU scheduling. This research contributes to the optimization of cloud
Uploads
Papers by Pavan Srikanth Patchamatla