Container Orchestration Research Articles

A Comparative Study of Resource Management Approaches in Kubernetes and Docker Swarm: Efficiency and Scalability

This article presents a comparative analysis of resource management (RM) approaches in two popular container orchestration platforms, Kubernetes and Docker Swarm. The key differences in RM, scheduling, and scaling are discussed, with a focus on the flexibility and granularity of Kubernetes compared to the simplicity and ease of use of Docker Swarm. The advantages and disadvantages of each tool are also analyzed to provide a more complete understanding of their applicability. -

Orchestration Workflows in Distributed Systems: A Systematic Analysis of Efficiency Optimization and Service Coordination

This article presents a comprehensive analysis of orchestration workflows in modern software architectures, examining their role in optimizing service efficiency and coordination across distributed systems. Through a systematic investigation of implementation patterns and operational strategies, we demonstrate how orchestrated workflows enhance service integration, automate complex processes, and improve system reliability. The article employs a mixed-method approach, combining quantitative performance metrics from real-world implementations with qualitative analysis of architectural patterns across diverse enterprise environments. The findings reveal that organizations implementing robust orchestration workflows experienced a 47% reduction in service integration failures, 35% improvement in resource utilization, and 62% faster deployment cycles. The article also identifies critical success factors in orchestration implementation, including dynamic resource management, automated error handling, and comprehensive monitoring frameworks. Furthermore, The article proposes a novel framework for evaluating orchestration effectiveness in cloud-native environments, considering factors such as service mesh integration, container orchestration, and distributed tracing. This article contributes to the growing body of knowledge in service optimization by providing empirical evidence of orchestration benefits and offering practical guidelines for implementing scalable, resilient software systems through advanced workflow orchestration strategies.

Optimizing Kubernetes Environments: Best Practices for Configuring and Managing Admission Webhooks

Abstract: Kubernetes has emerged as the leading platform for container orchestration, offering a robust framework for automating the deployment, scaling, and management of containerized applications. As its adoption increases, managing diverse workloads across various environments becomes increasingly complex, necessitating mechanisms for policy enforcement, compliance assurance, and dynamic configuration management. In Kubernetes, admission webhooks provide a means to intercept requests, enabling the mutation and validation of these requests to enforce custom policies. This paper presents comprehensive guidelines for configuring and operating admission webhooks to enhance security, consistency, and operational efficiency in Kubernetes environments. Through this paper, readers will gain hands-on experience in implementing best practices, thereby optimizing the use of admission webhooks to effectively maintain organizational policies and standards.

Mastering Microservices Architecture and Cloud Computing : In-Depth Strategies for Domain-Specific Optimization

This comprehensive article explores the intricate landscape of microservices architecture and cloud computing, specifically focusing on domain-specific optimization strategies. It delves into the foundational principles of microservices, examining their synergistic relationship with cloud technologies and their transformative impact on modern software development. The article addresses critical aspects such as service-to-service communication, API management, and data security in distributed environments. It also investigates the role of cloud-native services, including serverless computing and container orchestration, in enhancing microservices performance. Analyzing real-world case studies and emerging trends, the article highlights how organizations can leverage these technologies to drive innovation and maintain a competitive edge. Key challenges in implementing and managing microservices architectures and strategies for overcoming them are discussed. The research emphasizes the importance of tailoring microservices to specific business needs and provides insights into performance tuning for high-traffic systems. This article offers a forward-looking perspective on the evolution of microservices and cloud computing by exploring future trends and considerations, including the potential impact of emerging technologies like edge computing and artificial intelligence. It serves as a valuable resource for IT professionals, architects, and decision-makers seeking to master these technologies and implement domain-specific optimizations effectively in their organizations.

Enhancing Edge Environment Scalability: Leveraging Kubernetes for Container Orchestration and Optimization

ABSTRACTKubernetes is an open‐source container orchestration platform, offers a comprehensive suite of features for managing containerized applications effectively. These features encompass horizontal scaling, per‐node‐pool cluster scaling and automated resource request adjustments. This research endeavors to harness these capabilities to address the limitations experienced by fog servers in edge environments, particularly those arising from restricted network connectivity and scalability challenges. In this research paper, the primary focus is on Kubernetes role of enhancing scalability, providing a robust framework for managing containerized applications. The proposed approach involves creating a predefined number of pods and containers within a Kubernetes cluster, specifically designed to efficiently handle incoming requests while optimizing CPU and memory usage. This method implements a microservice architecture for the web tier, with separate pods for the front end, back end and database, ensuring modular and scalable design. All pods communicate and integrate through REST APIs, facilitating seamless interaction and data exchange between the services. When handling web requests, the approach enables and controls both internal and external networks, ensuring secure and efficient communication. The analysis then examines the CPU and memory utilization of the pods, as well as node bandwidth, to provide a comprehensive evaluation of container scalability and performance within the Kubernetes cluster. These findings effectively demonstrate Kubernetes' capability in managing container scalability and optimizing resource utilization, highlighting its efficiency and robustness in a microservice environment.

Modelling and simulation of cloud‐native‐based edge computing terminals for power distribution

AbstractThe introduction of cloud‐native technology has significantly changed the architecture of applications and the mechanism for the collaborative operation of components in power distribution edge computing terminals (PDECT). To develop an effective quantitative analysis tool for PDECT performance, the composition and characteristics of cloud‐native PDECT are studied, and the modelling and simulation of cloud‐native PDECT are proposed. Subsequently, modelling is implemented through the simulation software CloudSim, achieving the simulation of microservices, containers, declarative configuration, and container orchestration with the consideration of power distribution scenarios. Then, by the proposed simulation scenario module, various elements of the power distribution scenarios can be self‐defined. Finally, by demonstrating the principles and implementation mechanisms of the proposed modelling method and simulation tool, and comparing simulation results for different service time ranges, access devices, resource configurations of PDECT, request occurrence rates, and resource scheduling strategies, the validity and effectiveness of the proposed modelling method and simulation tool are verified.

ПОСТРОЕНИЕ МОДЕЛИ РАСЧЕТА ВРЕМЕНИ ВЫПУСКА ДОРАБОТОК ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ ПОСЛЕ ВНЕДРЕНИЯ СИСТЕМЫ УПРАВЛЕНИЯ РЕЛИЗНЫМ ЦИКЛОМ

The paper proposes a mathematical model for calculating the release time of a new software release. This model allows you to evaluate the difference between the operation of a production system during the release cycle and after it. The relevance of the proposed methodology determines the need for its application to the problem of assessing the effectiveness of an information complex and an automated release process cycle. The object of the study is the information department of the bank, ensuring automation – the process of estimating the release time of software improvements. The goal of the work is to build a model for calculating the release time of software improvements after the implementation of a release cycle management system, ensuring regular, fast and stable implementation of new software versions. To achieve this goal, within the framework of the work, an architectural solution for a system that automates the release cycle process was designed; a new process of personnel interaction was modeled after the implementation of the release cycle system; a pipeline for automatic delivery of software to an industrial environment and a process for automatic packaging of software into containers have been developed; a mathematical model was built to calculate the release time of software improvements after the release cycle management system. To solve these problems, we used the Gitlab CI/CD class system, which provides the ability to automatically assemble containers and deploy them into environments, and the Deckhouse system for container orchestration.

Open source container orchestration for Industry 4.0 – requirements and systematic feature analysis

Container-based virtualization is a popular technique, e.g., to realize microservice architectures. Recently, containers became popular in Industry 4.0 / IIoT systems, which typically consist of hundreds of (edge) devices and machines. In such setups, efficient management of containers is essential as offered by container orchestrators like Kubernetes. However, currently no specific overviews discussing orchestrator capabilities for Industry 4.0 are available. In this paper, we analyze nine open source container orchestrators for their application in Industry 4.0 or IIoT settings as a basis for future research and development. We contribute a systematic literature review to identify 23 basic orchestration requirements. We complement this by insights from an intensive requirements collection in a research project on intelligent industrial production, as well as selected features from a published generic orchestrator analysis. From these 66 requirements, we derive a requirements/feature taxonomy, which we use to analyze the nine open source orchestrators including Kubernetes. We show that there is, e.g., still a lack of support regarding edge devices, IIoT protocols, security mechanisms, and specialized resources for artificial intelligence.

Network Functions in Cloud: Kubernetes Deployment Challenges

The rapid evolution of cloud computing has paved the way for deploying network functions (NFs) in cloud environments, significantly enhancing the flexibility, scalability, and efficiency of modern network infrastructures. Kubernetes, an open-source container orchestration platform, has emerged as a leading tool for deploying and managing these cloud-based network functions. However, despite its widespread adoption, Kubernetes presents several deployment challenges specific to network functions, stemming from its design, scalability, and operational intricacies. This paper delves into the core challenges faced during the deployment of network functions on Kubernetes, focusing on issues related to network performance, security, service orchestration, and resource management. The abstract aims to provide an overview of the technical hurdles and propose potential strategies to overcome them, thus contributing to the optimization of Kubernetes-based NF deployments in cloud environments. By analyzing existing literature and case studies, the paper identifies key areas where improvements are needed and discusses the implications of these challenges for the future of cloud-based network functions. Ultimately, the paper seeks to guide network architects and cloud engineers in better understanding the complexities of Kubernetes deployments for network functions and in developing more effective strategies for successful implementation.

On the Optimization of Kubernetes toward the Enhancement of Cloud Computing

With the vigorous development of big data and cloud computing, containers are becoming the main platform for running applications due to their flexible and lightweight features. Using a container cluster management system can more effectively manage multiocean containers on multiple machine nodes, and Kubernetes has become a leader in container cluster management systems, with its powerful container orchestration capabilities. However, the current default Kubernetes components and settings have appeared to have a performance bottleneck and are not adaptable to complex usage environments. In particular, the issues are data distribution latency, inefficient cluster backup and restore leading to poor disaster recovery, poor rolling update leading to downtime, inefficiency in load balancing and handling requests, poor autoscaling and scheduling strategy leading to quality of service (QoS) violations and insufficient resource usage, and many others. Aiming at the insufficient performance of the default Kubernetes platform, this paper focuses on reducing the data distribution latency, improving the cluster backup and restore strategies toward better disaster recovery, optimizing zero-downtime rolling updates, incorporating better strategies for load balancing and handling requests, optimizing autoscaling, introducing better scheduling strategy, and so on. At the same time, the relevant experimental analysis is carried out. The experiment results show that compared with the default settings, the optimized Kubernetes platform can handle more than 2000 concurrent requests, reduce the CPU overhead by more than 1.5%, reduce the memory by more than 0.6%, reduce the average request time by an average of 7.6%, and reduce the number of request failures by at least 32.4%, achieving the expected effect.

Efficient Resource Allocation in Kubernetes Using Machine Learning

Kubernetes is a distinguished open-source container orchestration system in cloud computing and containerized applications. Google developed it, and the Cloud Native Computing Foundation now maintains it. Kubernetes offers a robust framework for automating application deployment scaling and management, revolutionizing how organizations use their containerized workloads and providing huge flexibility and feasibility. The current paper explores the application of machine learning algorithms to optimize resource allocation in Kubernetes environments. As the complexity of cloud-native applications increases because of various engagements, it is vital to maintain performance and cost- effectiveness. This study also evaluates various machine learning models and techniques and their relevancy in areas such as anomaly detection and enhancing overall cluster utilization. Our findings include machine learning-driven methodologies that will significantly improve performance utilizing historical data. Kubernetes's decentralized nature requires a scalable structure for task scheduling to accommodate dynamic workloads conveniently. The AIMD algorithm, a celebrated method for congestion avoidance in network management, inspires our approach. Computing clusters can be challenging to deploy and manage due to their complexity. Monitoring systems collect large amounts of data, which is daunting to understand manually. Machine learning provides a viable solution to detect anomalies in a Kubernetes cluster. KAD (Kubernetes et al.) is one such algorithm that can solve the Cluster anomalies problem. Enormous Cloud native applications market is projected to reach 17.0 billion USD by 2028, which was USD 5.9 billion in 2023. On par with those numbers is the global Machine Learning (ML) market size, which was valued at $19.20 billion in 2022 and is expected to grow from $26.03 billion in 2023 to $225.91 billion by 2030 (As per Fortune Business Insights). At the conjecture, both markets will take innovation to a new level, offering more adaptive solutions for contemporary cloud infrastructures.

Management and Operator Control System based on Microservice Architecture and Application on the HIPr Accelerator

The paper describes the basic principles of developing a distributed control system (DCS) and a GARNET operator control system based on a microservice architecture as part of a high-availability cluster. The application of the operator’s control system as a DCS component is described. The main elements of software components of operator control and DCS are presented and described. The process of conveyor assembly and publication of software tools into a working product environment, which implements the principle of continuous integration, is described. The mechanism of interaction of key components among themselves is presented. The mechanism for hosting management services using the Docker containerization system and Kubernetes container orchestration is demonstrated. Examples of services for interaction with users in the environment of the GARNET operator control system being developed, separation of users by roles and access rights, integration of the data visualization service using Grafana are shown. The vector of further development of DCS and operator control tools is described, in particular, the possibility of using the practice of developing user web interfaces using the micro frontend approach. The components and results of the operation of a prototype system designed to interact with the measurement infrastructure of the linear heavy ion accelerator HIPR are presented.

Secure container Orchestration: A framework for detecting and mitigating Orchestrator - level vulnerabilities

Secure container Orchestration: A framework for detecting and mitigating Orchestrator - level vulnerabilities

Definition and implementation of the Cloud Infrastructure for the integration of the Human Digital Twin in the Social Internet of Things

With the integration of virtualization technologies, the Internet of Things (IoT) is expanding its capabilities and quickly becoming a complex ecosystem of networked devices. The Social Internet of Things (SIoT), where intelligent things include social properties that improve functioning and user engagement, is the result of this progress. The SIoT still has issues with scalability, data management, and user-centric operations, despite tremendous progress. In order to overcome these obstacles, a strong architecture is needed that can handle the enormous number of IoT devices while simultaneously streamlining the user interface.This study provides a unique architecture for the IoT that uses containerization to efficiently deploy and manage services while integrating Virtual Users (VUs) and Social Virtual Objects (SVOs) into a scalable Cloud/Edge infrastructure. These innovative aspects collectively advance previous works presented in literature and focused on novel SIoT architectures and implementations, by addressing key challenges in scalability, efficiency, and automation within the SIoT. The proposed method presents an extensible, modular architecture that lets VUs self-manage IoT services, making user administration easier and improving system security and scalability. Important parts of the design include a host controller for container orchestration, a deployer for automated service deployment, and user clusters for aggregating VUs, SVOs, and apps to provide secured and efficient data sharing. We show through experimental assessment that the architecture can manage high-volume installations and operating needs, exceeding the conventional platform based on Google App Engine in terms of system overhead and deployment timeframes. The obtained results highlight how our suggested architecture, which provides an easy-to-use, scalable, and secure foundation for IoT deployments, has the potential to advance the SIoT landscape.

Enhanced Visibility for Real-time Monitoring and Alerting in Kubernetes by Integrating Prometheus,Prometheus, Grafana, Loki, and Alerta

With the increasing popularity of Kubernetes as the go-to platform for containerized applications, numerous companies are adopting Kubernetes as their preferred platform for containerized applications. As organizations embrace this container orchestration technology for its scalability, flexibility, and portability benefits, the need for robust monitoring solutions becomes paramount. Monitoring Kubernetes environments is essential to ensure the health, performance, and availability of applications running within the cluster. This paper aims to provide a comprehensive approach for monitoring Kubernetes via Prometheus, Grafana, and Alerta Prometheus, a powerful open-source monitoring system, col- lects metrics from Kubernetes pods and nodes, enabling real- time monitoring of resource utilization, performance metrics, and application health. Grafana complements Prometheus by providing intuitive visualization of collected metrics through customizable dashboards, facilitating comprehensive insights into cluster performance and trends. Loki and Promtail by Grafana are used to collect and aggregate the logs associated with the cluster. Alerta enhances the monitoring setup by enabling alerting based on predefined thresholds and conditions, ensuring prompt notification of potential issues or anomalies. Together, this stack empowers administrators to gain deep visibility into their Kubernetes infrastructure, proactively identify and mitigate issues, and maintain the high availability and reliability of their applications and services. Index Terms—Kubernetes, Prometheus, Grafana, Alerta, Loki, Promtail, Monitoring, Alerting, Logging.

Enhancing Resource Utilization Efficiency in Serverless Education: A Stateful Approach with Rofuse

Traditional container orchestration platforms often suffer from resource wastage in educational settings, and stateless serverless services face challenges in maintaining container state persistence during the teaching process. To address these issues, we propose a stateful serverless mechanism based on Containerd and Kubernetes, focusing on optimizing the startup process for container groups. We first implement a checkpoint/restore framework for container states, providing fundamental support for managing stateful containers. Building on this foundation, we propose the concept of “container groups” to address the challenges in educational practice scenarios characterized by a large number of similar containers on the same node. We then propose the Rofuse optimization mechanism, which employs delayed loading and block-level deduplication techniques. This enables containers within the same group to reuse locally cached file system data at the block level, thus reducing container restart latency. Experimental results demonstrate that our stateful serverless mechanism can run smoothly in typical educational practice scenarios, and Rofuse reduces the container restart time by approximately 50% compared to existing solutions. This research provides valuable exploration for serverless practices in the education domain, contributing new perspectives and methods to improve resource utilization efficiency and flexibility in teaching environments.

Impact of power consumption in containerized clouds: A comprehensive analysis of open-source power measurement tools

Recently, container-based solutions have become de facto compute units of modern cloud-native applications. However, the exponential growth in data traffic and the power consumption of these technologies to handle high data traffic alarm the strong need for energy evaluation approaches in containerized clouds. Furthermore, the proliferation of highly distributed edge clouds raises additional concerns regarding the power consumption of future cloud architectures. This article presents a detailed overview of methods and techniques for monitoring power consumption within popular cloud platforms. The study offers an in-depth evaluation of these approaches, demonstrating variations in measured power consumption based on the chosen technique. A well-known container orchestration platform named Kubernetes (K8s) has been applied in our extensive measurements. This work argues that energy-efficient container clouds will play a vital role in building a more sustainable and eco-friendly digital infrastructure by optimizing power consumption and reducing carbon footprint, paving the way for a greener future. The paper also discusses open challenges and future research directions on energy sustainability, leading to the conclusion, offering lessons learned and prospects on potential solutions to foster sustainable practices within the container ecosystem.

SLA-Adaptive Threshold Adjustment for a Kubernetes Horizontal Pod Autoscaler

Kubernetes is an open-source container orchestration system that provides a built-in module for dynamic resource provisioning named the Horizontal Pod Autoscaler (HPA). The HPA identifies the number of resources to be provisioned by calculating the ratio between the current and target utilisation metrics. The target utilisation metric, or threshold, directly impacts how many and how quickly resources will be provisioned. However, the determination of the threshold that would allow satisfying performance-based Service Level Objectives (SLOs) is a long, error-prone, manual process because it is based on the static threshold principle and requires manual configuration. This might result in underprovisioning or overprovisioning, leading to the inadequate allocation of computing resources or SLO violations. Numerous autoscaling solutions have been introduced as alternatives to the HPA to simplify the process. However, the HPA is still the most widely used solution due to its ease of setup, operation, and seamless integration with other Kubernetes functionalities. The present study proposes a method that utilises exploratory data analysis techniques along with moving average smoothing to identify the target utilisation threshold for the HPA. The objective is to ensure that the system functions without exceeding the maximum number of events that result in a violation of the response time defined in the SLO. A prototype was created to adjust the threshold values dynamically, utilising the proposed method. This prototype enables the evaluation and comparison of the proposed method with the HPA, which has the highest threshold set that meets the performance-based SLOs. The results of the experiments proved that the suggested method adjusts the thresholds to the desired service level with a 1–2% accuracy rate and only 4–10% resource overprovisioning, depending on the type of workload.

Fast state transfer for updates and live migration of industrial controller runtimes in container orchestration systems

Many industrial processes (e.g., steel, fertilizer, paper production) utilize programmable logic controllers or distributed control systems, which cyclically execute control applications. Dynamic updates of such applications at runtime are desirable for optimizing the production while avoiding production stops. However, such updates are challenging since they require fast switching to an updated instance and executing an internal state transfer within the control cycle times. Existing dynamic update approaches for industrial applications can for example not update controller runtimes or operating systems, since they do not utilize modern container virtualization technologies. Furthermore, existing state transfer methods for container orchestration systems do not support fast cyclic applications. We propose a novel state transfer method for industrial control applications deployed as containerized Kubernetes microservices on server hosts or resource-rich industrial personal computers. The method enables dynamic updates and live migration of applications to others hosts without interrupting the underlying production facilities, which avoids costly downtimes. We have implemented our method as a Kubernetes extension and found that it can transfer large application states (i.e. 100.000 variables) via OPC UA in less than 42 ms across different hosts, which can fit into the available cycle slack time, therefore enabling a wide range of bumpless updates at runtime.

Microservices and serverless functions—lifecycle, performance, and resource utilisation of edge based real-time IoT analytics

Edge Computing harnesses resources close to the data sources to reduce end-to-end latency and allow real-time process automation for verticals such as Smart City, Healthcare and Industry 4.0. Edge resources are limited when compared to traditional Cloud data centres; hence the choice of proper resource management strategies in this context becomes paramount. Microservice and Function as a Service architectures support modular and agile patterns, compared to a monolithic design, through lightweight containerisation, continuous integration/deployment and scaling. The advantages brought about by these technologies may initially seem obvious, but we argue that their usage at the Edge deserves a more in-depth evaluation. By analysing both the software development and deployment lifecycle, along with performance and resource utilisation, this paper explores microservices and two alternative types of serverless functions to build edge real-time IoT analytics. In the experiments comparing these technologies, microservices generally exhibit slightly better end-to-end processing latency and resource utilisation than serverless functions. One of the serverless functions and the microservices excel at handling larger data streams with auto-scaling. Whilst serverless functions natively offer this feature, the choice of container orchestration framework may determine its availability for microservices. The other serverless function, while supporting a simpler lifecycle, is more suitable for low-invocation scenarios and faces challenges with parallel requests and inherent overhead, making it less suitable for real-time processing in demanding IoT settings.