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kubernetes-Powered Cardiovascular Monitoring: Enhancing Internet of Things Heart Rate Systems for Scalability and Efficiency

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INFORMATION 2025年第3期16卷

作者： Sucipto, Hans Indrawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Surantha, Nico Bina Nusantara Univ BINUS Grad Program Master Comp Sci Comp Sci Dept Jakarta 11480 Indonesia Bina Nusantara Univ Sch Comp Sci Comp Sci Dept Jakarta 11480 Indonesia Bina Nusantara Univ Bioinformat & Data Sci Res Ctr Jakarta 11480 Indonesia Tokyo City Univ Fac Engn Dept Elect Elect & Commun Engn Setagaya Ku Tokyo 1588557 Japan

Reliable system design is an important component to ensure data processing speed, service availability, and an improved user experience. Several studies have been conducted to provide data processing speeds for health monitors using clouds or edge devices. However, if the system design used cannot handle many requests, the reliability of the monitoring itself will be reduced. This study used the kubernetes approach for system design, leveraging its scalability and efficient resource management. The system was deployed in a local kubernetes environment using an Intel Xeon CPU E5-1620 with 8 GB RAM. This study compared two architectures: MQTT (traditional method) and MQTT-Kafka (proposed method). The proposed method shows a significant improvement, such as throughput results on the proposed method of 1587 packets/s rather than the traditional methods at 484 packets/s. The response time and latency are 95% more stable than the traditional method, and the performance of the proposed method also requires a larger resource of approximately 30% more than the traditional method. The performance of the proposed method requires the use of a large amount of RAM for a resource-limited environment, with the highest RAM usage at 5.63 Gb, while the traditional method requires 4.5 Gb for the highest RAM requirement.

关键词： internet of things microservices health monitoring kubernetes

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kubernetes application performance benchmarking on heterogeneous CPU architecture: An experimental review

HIGH-CONFIDENCE COMPUTING

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HIGH-CONFIDENCE COMPUTING 2025年第1期5卷

作者： Noor, Jannatun Faysal, Badsha Amin, Sheikh Tabassum, Bushra Khan, Tamim Raiyan Rahman, Tanvir United Int Univ Sch Data & Sci Dept Comp Sci & Engn Comp Sustainabil & Social Good C2SG Res Grp Dhaka 1212 Bangladesh

With the rapid advancement of cloud technologies, cloud services have enormously contributed to the cloud community for application development life-cycle. In this context, kubernetes has played a pivotal role as a cloud computing tool, enabling developers to adopt efficient and automated deployment strategies. Using kubernetes as an orchestration tool and a cloud computing system as a manager of the infrastructures, developers can boost the development and deployment process. With cloud providers such as GCP, AWS, Azure, and Oracle offering kubernetes services, the availability of both x86 and ARM platforms has become evident. However, while x86 currently dominates the market, ARM-based solutions have seen limited adoption, with only a few individuals actively working on ARM deployments. This study explores the efficiency and cost-effectiveness of implementing kubernetes on different CPU platforms. By comparing the performance of x86 and ARM platforms, this research seeks to ascertain whether transitioning to ARM presents a more advantageous option for kubernetes deployments. Through a comprehensive evaluation of scalability, cost, and overall performance, this study aims to shed light on the viability of leveraging ARM on different CPUs by providing valuable insights. (c) 2024 The Author(s). Published by Elsevier B.V. on behalf of Shandong University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： kubernetes K8s K3s Serverless computing Container orchestration Docker CPU architecture x86 ARM

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Analyzing Energy-Efficient and kubernetes-Based Autoscaling of Microservices Using Probabilistic Model Checking

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JOURNAL OF GRID COMPUTING 2025年第1期23卷 1-26页

作者： Agos Jawaddi, Siti Nuraishah Ismail, Azlan Sulaiman, Mohd Suffian Cardellini, Valeria Univ Teknol MARA UiTM Coll Comp Informat & Math Sch Comp Sci Shah Alam 40450 Selangor Malaysia Univ Teknol MARA UiTM Inst Big Data Analyt & Artificial Intelligence IBD Selangor Malaysia Univ Roma Tor Vergata Dept Civil Engn & Comp Sci Engn DICII Rome Italy

Microservices are widely used to enable agility and scalability in modern software systems, while cloud computing offers cost-effective ways to provision computing resources on demand. However, ensuring the correctness of scaling decisions and their impact on energy consumption is a challenging problem that has not been sufficiently addressed in previous research. Thus, in this paper, we present an innovative approach for analyzing host energy consumption and energy violations influenced by microservice autoscaling policies using probabilistic model checking (PMC). We propose four variations of the Markov Decision Process (MDP) models that incorporate various scaling constraints inspired by kubernetes-based Horizontal Pod Autoscaler, and we encode these models using two different approaches, namely, bounded-by-action (BBA) and bounded-by-state (BBS). We use PMC to verify the scaling policies in terms of host energy consumption and energy violations, and we conduct sensitivity analysis to demonstrate the effectiveness of our models in generating energy-efficient scaling policies. Our results show that the latency and energy-based MDP model offers the most suitable policies for ensuring energy efficiency in microservice systems. Additionally, the number of pods and the scale-out action significantly affect energy consumption and violations. Sensitivity analysis also reveals that incorporating latency into scaling decisions is key to energy efficiency, while variations in the maximum pod threshold significantly influence energy consumption and violation. Our approach provides a formal method for ensuring the correctness of microservice autoscaling decisions in cloud environments at design time and can help reduce energy consumption and violations while ensuring service-level objectives are met.

关键词： Microservices Energy-driven microservice autoscaling Markov decision process Formal verification Probabilistic model checking kubernetes

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KTWIN: A serverless kubernetes-based Digital Twin platform

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COMPUTER NETWORKS 2025年 259卷

作者： Wermann, Alexandre Gustavo Wickboldt, Juliano Araujo Fed Univ Rio Grande do Sul UFRGS Inst Informat Av Bento Goncalvez 9500 BR-91509900 Porto Alegre RS Brazil

Digital Twins (DTs) systems are virtual representations of physical assets allowing organizations to gain insights and improve existing processes. In practice, DTs require proper modeling, coherent development and seamless deployment along cloud and edge landscapes relying on established patterns to reduce operational costs. In this work, we propose KTWIN a kubernetes-based Serverless Platform for Digital Twins. KTWIN was developed using the state-of-the-art open-source Cloud Native tools, allowing DT operators to easily define models through open standards and configure details of the underlying services and infrastructure. The experiments carried out with the developed prototype show that KTWIN can provide a higher level of abstraction to model and deploy a Digital Twin use case without compromising the solution scalability. The tests performed also show cost savings ranging between 60% and 80% compared to overprovisioned scenarios.

关键词： Digital Twin Serverless kubernetes Event-Driven Architecture Internet of Things

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Investigating the Influence of Data Architecture on the Performance of kubernetes-Based Applications

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2025年第1期41卷 25-42页

作者： Lu, An Liao, Chun-feng Natl Chengchi Univ Dept Management Informat Syst Taipei 116 Taiwan Natl Chengchi Univ Dept Comp Sci Taipei 116 Taiwan Natl Chengchi Univ Program Digital Content & Technol Taipei 116 Taiwan

kubernetes has gained significant importance and popularity as a leading container orchestration platform, facilitating the deployment and management of distributed applications. However, despite its widespread adoption, the exploration of data architecture in the context of kubernetes-based applications remains limited. The design and configuration of data architecture of a kubernetes cluster can profoundly impact the overall performance and scalability of applications. This paper aims to investigate the influence of data architecture on the performance of kubernetes-based applications. By analyzing various aspects of data architecture we aim to understand their impact on crucial performance metrics through a series of experiments and performance evaluations. Specifically, we examine different data architecture configurations and their effects on application performance. The outcomes of this research contribute to enhancing the understanding of the relationship between data architecture and the performance of kubernetes-based applications, empowering developers and system administrators to make informed decisions when optimizing and designing data architecture for their applications in a kubernetes environment.

关键词： container container orchestration kubernetes storage data architecture cloud native systems

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On the Stability of the kubernetes Horizontal Autoscaler Control Loop

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IEEE ACCESS 2025年 13卷 7160-7166页

作者： Serracanta, Berta Lukacs, Andor Rodriguez-Natal, Alberto Cabellos, Albert Retvari, Gabor Univ Politecn Cataluna Dept Comp Architecture Barcelona 08034 Spain Babes Bolyai Univ Fac Math & Comp Sci Cluj napoca 400084 Romania Cisco Madrid 28108 Spain Budapest Univ Technol & Econ Dept Telecommun & Artificial Intelligence H-1111 Budapest Hungary

kubernetes is a widely used platform for deploying and managing containerized applications due to its efficient elastic capabilities. The Horizontal Pod Autoscaler (HPA) in kubernetes independently adjusts the number of pods for each service, yet these services often operate in an interconnected manner. This study aims to understand the effects of autoscaling events on a graph of interconnected services. To achieve this, we apply control theory to model the HPA's behavior. We analyze the stability of this model, perform numerical simulations, and deploy a real testbed to evaluate the performance. Our findings demonstrate that the control theory-based model accurately predicts the HPA's behavior, ensuring system stability with CPU utilization meeting desired thresholds and no traffic loss after a transitional period. The model provides insights into optimizing resource scheduling and improving application performance in kubernetes environments. Additionally, we extend our model to the whole service graph to understand how individual scaling decisions influence the complex graphs of cloud applications.

关键词： Microservice architectures Load modeling Computational modeling Analytical models Resource management Measurement Mathematical models Asymptotic stability Stability criteria Solid modeling Cloud autoscaling control theory Horizontal Pod Autoscaler kubernetes microservices architecture numerical simulations system stability

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Energy-aware Scheduling Algorithm for Microservices in kubernetes Clouds

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JOURNAL OF GRID COMPUTING 2025年第1期23卷 1-22页

作者： Rao, Wei Li, Hongjian Chongqing Univ Posts & Telecommun Dept Comp Sci & Technol Chongqing 400065 Peoples R China

More and more applications are organized in the form of meshed microservices which can be deployed on the popular container orchestration platform kubernetes. kubernetes offers automated management, high availability, elastic scaling, and cross-cloud compatibility for complex meshed microservices applications. Although kubernetes is a powerful tool for managing containers, its default scheduling algorithm and existing studies on container scheduling are mainly designed for monolithic applications. They fail to consider the varying resource consumption of different microservices, as well as the CPU consumption caused by the heartbeat mechanism of these microservices, leading to energy waste and inefficiencies. Hence, we propose an energy-aware scheduling algorithm based on Service Level Agreement (SLA) to reduce energy consumption of microservices deployed in kubernetes. The proposed algorithm divides the communication frequency for the overall Pods by the network traffic between Pods and prioritizes the resource consumption of Pods based on the resource consumption of microservices running in the Pods. Additionally, an improved Sparrow Search Algorithm (ISSA) is designed and applied to pack the Pods by the communication frequency and the resource consumption priority of Pods, to achieve the goal of ensuring SLA and reducing energy consumption. The experimental results show that the energy consumption of kubernetes clusters in a cloud environment is reduced by at least 5% compared with the latest container scheduling algorithms.

关键词： kubernetes Service level agreement Energy consumption Container scheduling

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Dynamic Load Balancing in kubernetes Environments With kubernetes Scheduling Extension (KSE)

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2025年第3期37卷

作者： Neto, Pedro Moritz de Carvalho Castro, Marcio Siqueira, Frank Fed Univ Santa Catarina UFSC Grad Program Comp Sci PPGCC Florianopolis Brazil

kubernetes is a flexible and reliable container orchestrator that has been employed to maintain massive cloud infrastructures worldwide. The task of allocating "Pods" (deployable units of computing that have one or more containers) to cluster nodes in kubernetes is done by the Kube-Scheduler module, which determines which nodes are valid placements for each Pod according to constraints and available resources. Since it only acts at Pod creation, it does not take any action when the load of the system becomes uneven. In imbalanced scenarios, overloaded nodes can compromise the performance and availability of services hosted by them, whereas underloaded nodes may be a waste of financial resources, especially when using public clouds. In this paper, we propose an extension to the kubernetes scheduler, called kubernetes Scheduling Extension (KSE), which allows users to implement dynamic load-balancing algorithms that can migrate Pods between nodes at runtime. We also provide the implementation of two well-known load-balancing algorithms (KSE-GreedyLB and KSE-RefineLB) in KSE, which can balance the load of system nodes using CPU and memory consumption metrics. We carried out several experiments to assess the effectiveness of KSE-GreedyLB and KSE-RefineLB and compared their results with Kube-Scheduler. Overall, we evaluated 32 different scenarios using synthetic and realistic applications. Our results showed that KSE-RefineLB achieves better results than Kube-Scheduler when the workload is highly imbalanced while keeping similar performance when the load imbalance is low.

关键词： kubernetes load balancing optimization orchestrator virtualization container

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GAIKube: Generative AI-Based Proactive kubernetes Container Orchestration Framework for Heterogeneous Edge Computing

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2025年第2期11卷 933-945页

作者： Ali, Babar Golec, Muhammed Murugesan, Subramaniam Subramanian Wu, Huaming Gill, Sukhpal Singh Cuadrado, Felix Uhlig, Steve Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Tianjin Univ Ctr Appl Math Tianjin 300072 Peoples R China Univ Politecn Madrid Sch Telecommun Engn Madrid 28040 Spain

Containerized edge computing emerged as a preferred platform for latency-sensitive applications requiring informed and efficient decision-making accounting for the end user and edge service providers' interests simultaneously. Edge decision engines exploit pipelined knowledge streams to enhance performance and often fall short by employing inferior resource predictors subjected to limited available training data. These shortcomings flow through the pipelines and adversely impact other modules, including schedulers leading to such decisions costing delays, user-experienced accuracy, Service Level Agreements (SLA) violations, and server faults. To address limited data, substandard CPU usage predictions, and container orchestration considering delay accuracy and SLA violations, we propose a threefold GAIKube framework offering Generative AI (GAI)-enabled proactive container orchestration for a heterogeneous edge computing paradigm. Addressing data limitation, GAIKube employs DoppelGANger (DGAN) to augment time series CPU usage data for a computationally heterogeneous edge cluster. In the second place, GAIKube leverages Google TimesFM for its long horizon predictions, 4.84 Root Mean Squared Error (RMSE) and 3.10 Mean Absolute Error (MAE) against veterans Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) on concatenated DGAN and original dataset. Considering TimesFM quality predictions utilizing the DGAN extended dataset, GAIKube pipelines CPU usage predictions of edge servers to a proposed dynamic container orchestrator. GAIKube orchestrator produces container scheduling, migration, dynamic vertical scaling, and hosted application model-switching to balance contrasting SLA violations, cost, and accuracy objectives avoiding server faults. Google kubernetes Engine (GKE) based real testbed experiments show that the GAIKube orchestrator offers 3.43% SLA violations and 3.80% user-experienced accuracy loss with zero server faults at 1.46 CPU cores expense in comp

关键词： Edge computing generative AI generative AI container migration container migration vertical scaling vertical scaling kubernetes kubernetes service level agreement service level agreement

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Adaptive horizontal scaling in kubernetes clusters with ANN-based load forecasting

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第3期28卷 1-14页

作者： da Silva, Lucileide M. D. Alves, Pedro V. A. Silva, Sergio N. Fernandes, Marcelo A. C. Fed Univ Rio Grande Norte UFRN InovAI Lab nPITI IMD BR-59078970 Natal RN Brazil Univ Fed Rio Grande do Norte Leading Adv Technol Ctr Excellence LANCE nPITI IMD BR-59078970 Natal RN Brazil Fed Inst Educ Sci & Technol Rio Grande Norte IFRN BR-59200000 Santa Cruz RN Brazil Univ Fed Rio Grande do Norte Dept Comp & Automat Engn BR-59078900 Natal RN Brazil

In modern cloud environments, efficient management of computational resources is a critical challenge due to the growing demand for scalable and high-performance applications. Horizontal scaling in kubernetes (K8s) clusters is essential for dynamically adjusting resources to match workload demands. However, their reactive nature often limits traditional autoscaling methods like K8s Horizontal Pod Autoscaler (HPA), leading to inefficiencies under variable loads. To overcome these limitations, more advanced and adaptive scaling approaches are needed. Thus, this study introduces an adaptive approach to horizontal scaling in K8s clusters using Artificial Neural Networks (ANNs) for load forecasting, referred to as ANN-HS. The proposed method aims to enhance the efficiency of resource consumption and optimize replica allocation compared to the standard HPA. By leveraging pre-trained regression models, ANN-HS dynamically adjusts resources to meet varying demands, ensuring adherence to latency requirements and improving overall system performance. Experimental results demonstrate that ANN-HS outperforms traditional HPA methods, offering a scalable and flexible solution for managing microservices in cloud environments. This approach provides a robust framework for optimizing horizontal scaling in kubernetes, contributing to the advancement of intelligent resource management in cluster computing. Experimental results show that ANN-HS significantly improves resource utilization compared to kubernetes' HPA. Specifically, ANN-HS reduces CPU consumption by approximately 50% while maintaining Service Level Agreement (SLA) compliance with an average violation rate of less than 10%. Additionally, ANN-HS reduces the number of replicas needed by 66.67%, optimizing resource allocation under varying load conditions.

关键词： Horizontal scaling kubernetes Machine learning Artificial neural networks HPA

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