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Journal articles on the topic 'Knative'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Prudhvi, Naayini, and Bura Chiranjeevi. "Optimizing AI Model Inference on Serverless Cloud Platforms: A Scalable Approach." International Journal of Current Science Research and Review 08, no. 05 (2025): 1927–35. https://doi.org/10.5281/zenodo.15323189.

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Abstract : The increasing prevalence of Artificial Intelligence (AI) and Machine Learning (ML) models across various industries has highlighted the critical need for efficient and scalable deployment strategies. Traditional deployment methods often struggle with adapting to fluctuating demands and maintaining cost-effectiveness. Serverless computing has emerged as a promising solution to address these challenges. This paper investigates the deployment of AI models within serverless architectures on Amazon Web Services (AWS), specifically focusing on AWS Lambda and Knative. The study analyzes t

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2

Adusumilli, Lakshmi Vara Prasad. "Serverless Kubernetes: The Evolution of Container Orchestration." European Journal of Computer Science and Information Technology 13, no. 30 (2025): 20–36. https://doi.org/10.37745/ejcsit.2013/vol13n302036.

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This article examines the convergence of serverless computing and Kubernetes orchestration, representing a significant advancement in cloud-native architecture. Serverless Kubernetes implementations address fundamental operational challenges of traditional container orchestration while preserving its powerful capabilities. It explores the technical foundations enabling this evolution, including Virtual Kubelet for node abstraction, KEDA for event-driven scaling, and Knative for serverless abstractions. It analyzes implementations from major cloud providers—AWS EKS on Fargate, Azure Container I

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Naayini, Prudhvi. "Scalable AI Model Deployment and Management on Serverless Cloud Architecture." International Journal of Electrical, Electronics and Computers 9, no. 1 (2024): 1–12. https://doi.org/10.22161/eec.91.1.

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Scalable deployment of deep learning models in the cloud faces challenges in balancing performance, cost, and manageability. This paper investigates serverless cloud architecture for AI model inference, focusing on AWS technologies such as AWS Lambda, API Gateway, and Kubernetes-based serverless extensions (e.g., AWS EKS with Knative). We first outline the limitations of traditional, server-based model hosting to motivate the serverless approach. Then, we present novel strategies for scalable model serving: an adaptive resource provisioning algorithm, intelligent model caching, and efficient m

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Li, Dazhi, Jiaang Duan, Yan Yao, et al. "SoDa: A Serverless-Oriented Deadline-Aware Workflow Scheduling Engine for IoT Applications in Edge Clouds." Wireless Communications and Mobile Computing 2022 (October 7, 2022): 1–20. http://dx.doi.org/10.1155/2022/7862911.

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As a coordination tool, workflow with a large number of interdependent tasks has increasingly become a new paradigm for orchestrating computationally intensive tasks in large-scale and complex Internet of Things (IoT) applications. Serverless computing has also recently been applied to real-world problems at the network edge as well, primarily aimed at event based IoT applications. However, the existing workflow scheduling algorithm based on the virtual machine resource model is inefficient in ensuring the QoS (Quality of Service) of users on the serverless platform. In this paper, we design a

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Pentaparthi, Sai Kalyan Reddy. "Dissecting Serverless Computing for AI-Driven Network Functions: Concepts, Challenges, and Opportunities." European Journal of Computer Science and Information Technology 13, no. 6 (2025): 1–12. https://doi.org/10.37745/ejcsit.2013/vol13n6112.

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Serverless computing represents a transformative paradigm in cloud architecture that is fundamentally changing how network functions are deployed and managed. This article examines the intersection of serverless computing and artificial intelligence in the context of network functions, highlighting how this convergence enables more efficient, scalable, and intelligent network operations. The serverless model abstracts infrastructure management while offering automatic scaling and consumption-based pricing, creating an ideal environment for deploying AI-driven network capabilities. The architec

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Anila, Gogineni. "Helm for Continuous Delivery of Serverless Applications on Kubernetes." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 9, no. 6 (2021): 1–11. https://doi.org/10.5281/zenodo.14880986.

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This paper focuses on exploring the utilization of Helm in enabling coantinuous delivery of serverless applications that run on the Kubernetes. Helm helps in covering a package manager for Kubernetes that makes the packaging of serverless architecture more solid with the template Helm charts. By using these charts, developers are able to consolidate and orchestrate deployment pipelines over different environments. The problem addressed in the study is related to real-world issues of serverless applications, focusing on how serverless technologies have to work with Kubernetes, issues with multi

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A., Shevchenko, and Puzyrov S. "DEVELOPMENT OF THE HARDWARE AND SOFTWARE PLATFORM FOR MODERN IOT SOLUTIONS BASED ON FOG COMPUTING USING CLOUD-NATIVE TECHNOLOGIES." Computer systems and network 2, no. 1 (2017): 102–12. http://dx.doi.org/10.23939/csn2020.01.102.

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The concept of digital transformation is very relevant at the moment due to the epidemiological situation and the transition of the world to the digital environment. IoT is one of the main drivers of digital transformation. The Internet of Things (IoT) is an extension of the Internet, which consists of sensors, controllers, and other various devices, the so-called "things," that communicate with each other over the network. In this paper, the development of hardware and software for the organization of fog and edge computing was divided into three levels: hardware, orchestration, application.

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Hong, Sara, Yeeun Kim, Jaehyun Nam, and Seongmin Kim. "On the Analysis of Inter-Relationship between Auto-Scaling Policy and QoS of FaaS Workloads." Sensors 24, no. 12 (2024): 3774. http://dx.doi.org/10.3390/s24123774.

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A recent development in cloud computing has introduced serverless technology, enabling the convenient and flexible management of cloud-native applications. Typically, the Function-as-a- Service (FaaS) solutions rely on serverless backend solutions, such as Kubernetes (K8s) and Knative, to leverage the advantages of resource management for underlying containerized contexts, including auto-scaling and pod scheduling. To take the advantages, recent cloud service providers also deploy self-hosted serverless services by facilitating their on-premise hosted FaaS platforms rather than relying on comm

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9

Pinnapareddy, Nishanth Reddy. "Serverless Computing & Function-as-a-Service (FaaS) Optimization." American Journal of Engineering and Technology 07, no. 04 (2025): 65–88. https://doi.org/10.37547/tajet/volume07issue04-09.

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Function-as-a-Service (FaaS) in cloud computing is a critical optimization problem that needs to be tackled, including cold start latency, resource inefficiency, state management, and more. While FaaS provides obvious scalability and lower cost benefits, the lack of availability of resources and the problem of cold starts to prevent it from being used for high-performance applications. Pre-warming, snapshotting, and on-demand instantiation with lightweight runtimes, such as WebAssembly, are other ways to minimize cold start delays. It also benchmarks major FaaS platforms (AWS Lambda, Google Cl

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Pinnapareddy, Nishanth Reddy. "Serverless Computing & Function-as-a-Service (FaaS) Optimization." American Journal of Engineering and Technology 05, no. 05 (2023): 17–41. https://doi.org/10.37547/tajet/volume05issue05-09a.

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Function-as-a-Service (FaaS) in cloud computing is a critical optimization problem that needs to be tackled, including cold start latency, resource inefficiency, state management, and more. While FaaS provides obvious scalability and lower cost benefits, the lack of availability of resources and the problem of cold starts to prevent it from being used for high-performance applications. Pre-warming, snapshotting, and on-demand instantiation with lightweight runtimes, such as WebAssembly, are other ways to minimize cold start delays. It also benchmarks major FaaS platforms (AWS Lambda, Google Cl

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11

Гіоргізова-Гай, В. Ш. "ФУНКЦІЯ ЯК СЕРВІС У ПРИВАТНІЙ ХМАРІ ПІДПРИЄМСТВА". Таврійський науковий вісник. Серія: Технічні науки, № 2 (29 травня 2025): 57–68. https://doi.org/10.32782/tnv-tech.2025.2.6.

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У статті проводиться аналіз переваг та обмежень технології FaaS, тенденцій розвитку хмарних технологій, що сприяють її застосуванню у власній хмарній інфраструктурі організацій, та додаткових переваг, які вона може в ній надати. Також проводиться порівняння найбільш поширених FaaS фрейворків з відкритим вихідним кодом. Технологія FaaS модель хмарних обчислень, що пропонує платформу для виконання функціонально завершених фрагментів коду, які ініціюються подіями. Платформа забезпечує автоматичне масштабування та керування обчислювальними ресурсами, абстрагує низькорівневі інфраструктурні рішення

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12

Werthmann, R., and R. Hoppe. "Ein neues einfaches Titanat: KNaTiO3." Zeitschrift f�r anorganische und allgemeine Chemie 523, no. 4 (1985): 54–62. http://dx.doi.org/10.1002/zaac.19855230408.

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13

Hitzbleck, Rolf-D., Petra Vogt, and Horst Sabrowsky. "Notizen: Erste Interalkalimetalltelluride: NaLiTe, KLiTe, KNaTe / First Inter Alkali Metal Tellurides: NaLiTe, KLiTe, KNaTe." Zeitschrift für Naturforschung B 44, no. 12 (1989): 1602–4. http://dx.doi.org/10.1515/znb-1989-1224.

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The dark grey compounds NaLiTe, KLiTe and KNaTe have been prepared and characterized by X-ray powder diffraction. NaLiTe and KNaTe are isotypic and crystallize in the orthorhombic space group Pnma with Z = 4 formula units in the unit cell. The lattice constants are a = 774.0(1), b = 462.4(2) and c = 840.6(1) pm for NaLiTe and a = 852.1(1), b = 513.4(2) and c = 926.5(1) pm for KNaTe. KLiTe crystallizes in the tetragonal space group P4/nmm with a = 483.9(2) and c = 771.7(1) pm and Z = 2.

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14

Babu, Prabijna S. S., and Prakash D. Vaidya. "Sorption-enhanced steam methane reforming over Ni/Al2O3/KNaTiO3 bifunctional material." Journal of the Indian Chemical Society 99, no. 5 (2022): 100430. http://dx.doi.org/10.1016/j.jics.2022.100430.

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15

Fan, Wenqi, Tianyu Zhang, Nicholas M. Musyoka, et al. "Fabrication of structurally improved KNaTiO3 pellets derived from cheap rutile sand for high-temperature CO2 capture." Fuel 354 (December 2023): 129322. http://dx.doi.org/10.1016/j.fuel.2023.129322.

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16

Zheng, Qianwen, Liang Huang, Ziyi Zhong, Benoit Louis, and Qiang Wang. "Development of KNaTiO3 as a novel high-temperature CO2 capturing material with fast sorption rate and high reversible sorption capacity." Chemical Engineering Journal 380 (January 2020): 122444. http://dx.doi.org/10.1016/j.cej.2019.122444.

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17

Fan, Wenqi, Wanlin Gao, Liang Huang, et al. "Scalable synthesis of KNaTiO3-based high-temperature CO2 capture material from high titanium slag: CO2 uptake, kinetics, regenerability and mechanism study." Journal of CO2 Utilization 49 (July 2021): 101578. http://dx.doi.org/10.1016/j.jcou.2021.101578.

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18

Wei, Xingda, Fangming Lu, Zhuobin Huang, Rong Chen, Mingyu Wu, and Haibo Chen. "Towards Serialization/Deserialization-free State Transfer in Serverless Workflows." ACM Transactions on Computer Systems, March 25, 2025. https://doi.org/10.1145/3725986.

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Serialization and deserialization dominate the state transfer time of serverless workflows, leading to substantial performance penalties when executing various serverless workflow applications. We identify the key reason for serialization and deserialization as a lack of ability to efficiently access the (remote) memory of another function. To this end, we propose RMMap , an OS primitive for remote memory map, which allows a serverless function to directly access the memory of another function, even if it is located remotely. RMMap is the first to completely eliminate serialization and deseria

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19

Tran, Minh-Ngoc, and YoungHan Kim. "Concurrent service auto-scaling for Knative resource quota-based serverless system." Future Generation Computer Systems, June 2024. http://dx.doi.org/10.1016/j.future.2024.06.019.

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20

Tran, Minh-Ngoc, and YoungHan Kim. "Optimized resource usage with hybrid auto-scaling system for knative serverless edge computing." Future Generation Computer Systems, November 2023. http://dx.doi.org/10.1016/j.future.2023.11.010.

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21

Naayini, Prudhvi, and Chiranjeevi Bura. "Optimizing AI Model Inference on Serverless Cloud Platforms: A Scalable Approach." International Journal of Current Science Research and Review 08, no. 05 (2025). https://doi.org/10.47191/ijcsrr/v8-i5-02.

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The increasing prevalence of Artificial Intelligence (AI) and Machine Learning (ML) models across various industries has highlighted the critical need for efficient and scalable deployment strategies. Traditional deployment methods often struggle with adapting to fluctuating demands and maintaining cost-effectiveness. Serverless computing has emerged as a promising solution to address these challenges. This paper investigates the deployment of AI models within serverless architectures on Amazon Web Services (AWS), specifically focusing on AWS Lambda and Knative. The study analyzes the limitati

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22

Salcedo-Navarro, Andoni, Raúl Peña-Ortiz, Jose M. Claver, Miguel Garcia-Pineda, and Juan Gutiérrez-Aguado. "Towards GPU-enabled serverless cloud edge platforms for accelerating HEVC video coding." Cluster Computing 28, no. 1 (2024). http://dx.doi.org/10.1007/s10586-024-04692-0.

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AbstractMultimedia streaming has become integral to modern living, reshaping entertainment consumption, information access, and global engagement. The ascent of cloud computing, particularly serverless architectures, plays a pivotal role in this transformation, offering dynamic resource allocation, parallel execution, and automatic scaling-especially beneficial in HTTP Adaptive Streaming (HAS) applications. This study presents an event-driven serverless cloud edge platform with graphics processing units (GPUs), managed by Knative, tailored for video encoding. Two implementations of the High Ef

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23

Patchamatla, Pavan Srikanth, and Isaiah Oluasegun Owolabi. "Integrating Serverless Computing and Kubernetes in OpenStack for Dynamic AI Workflow Optimization." International Journal Of Multidisciplinary Research In Science, Engineering and Technology 01, no. 12 (2020). https://doi.org/10.15680/ijmrset.2020.0312021.

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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-

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""Evaluation of the Scalability and Performance of the Open Source Serverless Computing Platforms"." JST: Smart Systems and Devices 35, no. 1 (2025): 1–9. https://doi.org/10.51316/jst.179.ssad.2025.35.1.1.

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The most powerful cloud computing service model currently being developed is the Serverless computing model. This model brings scalability and cost optimization in the process of deploying applications on cloud infrastructure. The application will be divided into functions that run a specific logic and those functions will be deployed as independent units on the Serverless computing platform. One of the strongest points of deploying applications running on the Serverless computing platform is its scalability and good processing performance. Scalability is demonstrated through the process of re

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Moina-Rivera, Wilmer, Miguel Garcia-Pineda, Jose M. Claver, and Juan Gutiérrez-Aguado. "Event-Driven Serverless Pipelines for Video Coding and Quality Metrics." Journal of Grid Computing 21, no. 2 (2023). http://dx.doi.org/10.1007/s10723-023-09647-0.

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AbstractNowadays, the majority of Internet traffic is multimedia content. Video streaming services are in high demand by end users and use HTTP Adaptive Streaming (HAS) as transmission protocol. HAS splits the video into non-overlapping chunks and each video chunk can be encoded independently using different representations. Therefore, these encode tasks can be parallelized and Cloud computing can be used for this. However, in the most extended solutions, the infrastructure must be configured and provisioned in advance. Recently, serverless platforms have made posible to deploy functions that

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26

WERTHMANN, R., and R. HOPPE. "ChemInform Abstract: A NEW SIMPLE TITANATE: SODIUM POTASSIUM TITANATE (KNATIO3)." Chemischer Informationsdienst 16, no. 35 (1985). http://dx.doi.org/10.1002/chin.198535037.

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HITZBLECK, R. D., P. VOGT, and H. SABROWSKY. "ChemInform Abstract: The First Inter Alkali-Metal Tellurides, NaLiTe, KLiTe, KNaTe,." ChemInform 21, no. 12 (1990). http://dx.doi.org/10.1002/chin.199012025.

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