Container Orchestration Research Articles

Design of Mixed Reality Visualization System for Operational Situation Using Cloud-based Geospatial Information

The importance of geospatial information is increasingly highlighted in the defense domain. Accurate and up-to-date geospatial data is essential for situational awareness, target analysis, and mission planning in millitary operations. The use of high-resolution geospatial data in military operations requires large storage and fast image processing capabilities. Efficient image processing is required for tasks such as extracting useful information from satellite images and creating 3D terrain for mission planning, In this paper, we designed a cloud-based operational situation mixed reality visualization system that utilizes large-scale geospatial information distributed processed on a cloud server based on the container orchestration platform Kubernetes. We implemented a prototype and confirmed the suitability of the design.

Proactive auto-scaling technique for web applications in container-based edge computing using federated learning model

Edge computing has emerged as an attractive alternative to traditional cloud computing by utilizing processing, network, and storage resources close to end devices, such as Internet of Things (IoT) sensors. Edge computing is still in its infancy, and resource provisioning and service scheduling remain research concerns. Kubernetes is a container orchestration tool in distributed environments. Proactive auto-scaling techniques in Kubernetes improve utilization by allocating resources based on future workload prediction. However, prediction models run on central cloud nodes, necessitating data transfer between edge and cloud nodes, which increases latency and response time. We present FedAvg-BiGRU, a proactive auto-scaling method in edge computing based on FedAvg and multi-step prediction by a Bidirectional Gated Recurrent Unit (BiGRU). FedAvg is a technique for training machine learning models in a Federated Learning (FL) model. FL reduces network traffic by exchanging only model updates rather than raw data, relieving learning models of the need to store data on a centralized cloud server. In addition, a technique for determining the number of Kubernetes pods based on the Cool Down Time (CDT) concept has been developed, preventing contradictory scaling actions. To our knowledge, our work is the first to employ FL for proactive auto-scaling in cloud and edge computing. The results demonstrate that the FedAvg-BiGRU method has a slightly higher prediction error than the load centralized processing mode, although this difference is not statistically significant. At the same time, it reduces the amount of data transmission between the edge nodes and the cloud server.

Research on optimization strategy of container orchestration technology for cloud computing environment

Abstract Containers are becoming the new standard of technology in the cloud era, and a huge number of services are shifting from the traditional virtual machine-based monolithic architecture to the container-based cloud-native architecture. This paper explores the optimization of container orchestration technology in the cloud computing environment, mainly focusing on the optimization study of Kubernetes, a cloud resource orchestration tool for Docker containers. The problems of this container cloud platform were analyzed, and it was found that the orchestration strategy adopted by Kubernetes is a static orchestration strategy, which can easily cause redundancy of container resources. This paper suggests an optimization strategy that first analyzes the PSUB container resource allocation strategy and then presents a new container orchestration optimization strategy based on the PSUB container resource allocation strategy. This paper proposes a PBFDR strategy that utilizes CPU, memory, network bandwidth, and disk IO in a more reasonable and balanced manner by comparing and analyzing the optimization strategies of different container orchestration technologies. Introducing the PBFDR strategy into the Kubernetes container orchestration framework and comparing and analyzing it with the mainstream container orchestration frameworks Rancher and Docker Swarm, the balanced point of resource utilization of KLLPL is around 23%, and the load balancing degree in different deployment sizes is around 13.7%. The evidence indicates that container orchestration technology that is based on PBFDR policy can effectively enhance the utilization of cluster resources.

A Distributed Software Architecture for IoT: Container Orchestration Impact and Evaluation

This paper proposes a Distributed Software Architecture (DSA) for Smart Building (SB) based on the Reactive Manifesto (RM) principles. To follow the RM principles, we analyze the usage of different deployment approaches, particularly the impact of using a container orchestrator on the application layer. After running performance tests on the different configurations, the container orchestrator usage led to enhanced distributed processing, lowering the latency, increasing flexibility, enhancing security, and providing cost-effectiveness and scalability. We introduce the implementation of a modern DSA, developed following state-of-the-art cloud patterns and compliant with the RM for the SB context. Furthermore, we have ensured the reproducibility of this implementation by making the initial tests and overall architecture code available in public repositories. The research follows the Design Science Research (DSR) methodology for elaborating each phase until we get the artifact (DSA) and, with this, contribute to the Knowledge Base. The architecture was properly tested, considering the performance as the principal test layer. This solution is tailored for application in domains of the Internet of Things (IoT), focusing on the SB and a case study involving the Laboratory of Distributed Systems and Concurrent Programming (LaSDPC) at São Paulo University. Moreover, its applicability extends to IoT domains like smart home, smart campus, smart city, and health-related applications.

Horizontal Scaling Implementation with Container Orchestrator. Reliability Analysis during Stress and Performance Testing

Horizontal Scaling Implementation with Container Orchestrator. Reliability Analysis during Stress and Performance Testing

МЕТОД РЕКОНСТРУКЦІЇ ДОДАТКІВ ДЛЯ СТВОРЕННЯ КОНТЕЙНЕРІВ РОЗПОДІЛЕНОЇ МІКРОСЕРВІСНОЇ АРХІТЕКТУРИ

Abstract. Microservices are small, autonomous services. An application consisting of such microservices can be developed separately, with each microservice processing its own part of the logic without interacting with the others. Microservices are easy to deploy in containers, and container orchestration technologies help manage their distribution and updates. Therefore, many companies are moving from monolithic architectures to microservices. This article describes a method for converting a monolithic application into a microservice architecture of a container-based distributed system. Microservices are defined through data analysis of a monolithic application. It also provides a method for creating templated deployment scripts to support and distribute microservices in a containerized environment. The results of the study confirmed the effectiveness of the proposed approach. A runtime performance evaluation for API calls at various stages of development was conducted, which confirmed the benefits of a microservice architecture. The findings show that microservices created by the proposed method have higher reusability compared to those created by traditional methods. Keywords: container, microservice, API, scalability, distributed system

A Framework for Cognitive, Decentralized Container Orchestration

A Framework for Cognitive, Decentralized Container Orchestration

Використання блокчейну першого рівня LTO Network для автоматизації міжорганізаційних бізнес-процесів

The article is devoted to the consideration of blockchain technology as a means of automating inter-organizational business processes. It is noted that the digitalization and automation of internal business processes bring significant benefits in terms of increasing productivity and reducing costs. However, organizations face difficulties when trying to obtain such benefits for inter-organizational processes, partly due to a lack of trust. This paper considers a blockchain approach to address this problem. Bitcoin has demonstrated how blockchain can provide a distributed system without the need for trust through cryptography and decentralization. LTO extends this approach with a decentralized workflow engine for ad-hoc collaboration. Information is exchanged between participants through private event chains for each process and hashed on a public blockchain. This hybrid approach allows organizations to comply with data protection requirements and avoid scalability issues typical of blockchain projects. The advantages of LTO live contracts over Ethereum smart contracts are discussed. Live contracts do not contain values directly, but describe the interaction between the parties, unlike the cryptographic "boxes" of smart contracts. Modeling live contracts as finite state machines allows visualizing them in the form of a flowchart. Alternative modeling techniques such as Petri nets and BPMN are discussed. The article describes a scenario-based approach to defining a workflow containing states, actions, participants, assets, and embedded data objects. The features of data objects, participant identifiers, process execution and its adaptability are detailed. The author explains the operation of a private event chain as an ad-hoc private blockchain that ensures the consistency of the process state between nodes. Methods of ensuring data privacy are discussed. The second part of the article is devoted to the global public blockchain LTO to confirm information from private event chains. The consensus algorithm is explained based on the importance of token ownership and its use to prevent abuse. Optimizations, such as the NG protocol and aggregation blocks, are discussed to scale with a large number of transactions. The last part describes the architecture of the platform and its components: microservices, application layers, container orchestration. The platform makes it easy to digitalize and automate inter-organizational interactions using live contracts on the blockchain.

Development and Design of an Intelligent Financial Asset Management System Based on Big Data Analysis and Kubernetes

The rise of deep learning in the financial field has led to the integration of artificial intelligence and investment, providing users with intelligent investment decisions. However, the data volume of financial market continues to expand, traditional data processing methods can no longer meet the needs of efficiency and accuracy. This article focuses on deep reinforcement learning algorithms and delves into key issues such as stock price prediction, investment portfolios, and algorithmic trading. By comparing and analyzing the experimental results, not only was the performance of the model evaluated, but also the actual effect of the algorithm output was deeply explored. At the same time, drawing on Kubernetes container orchestration and microservice technology, a high concurrency and high-performance distributed financial data analysis system was constructed. This system not only meets the needs of users for real-time data analysis and deep learning, but also provides more reasonable investment suggestions for users. The contribution of this article lies in introducing deep reinforcement learning to solve nonlinear data problems in the financial field, proposing intelligent asset management methods, and designing a feasible intelligent financial asset management system, providing new ideas and practical experience for the further development of financial data analysis platforms.

Comparison of application container orchestration platforms

This article presents a comparative analysis of three well-known container orchestration platforms: Docker Swarm, Kubernetes and Apache Mesos, focusing on the deployment of a test application and measuring parameters such as deployment time, memory, CPU and disk utilization, application response time and the time to restore a replica of the application using an auto-recovery mechanism. The aim of the research is to verify the performance and efficiency of the analyzed platforms, facilitating informed decisions while choosing an orchestrator for containerized applications. Two research hypotheses have been stated. The first one assumes that the time required to launch an application using the Docker Swarm tool is the shortest among the analyzed platforms. The second hypothesis is that Kubernetes provides the most efficient results in terms of load scheduling and application scaling. The analysis performed on the Jenkins application showed the superiority of the Docker Swarm platform over the other studied tools in terms of performance.

Efficient Resource Utilization in Kubernetes: A Review of Load Balancing Solutions

Modern distributed systems face the challenge of efficiently distributing workloads across nodes to ensure optimal resource utilization, high avail-ability, and performance. In this context, Kubernetes, an open-source container orchestration engine, plays a pivotal role in automating deployment, scaling, and management of containerized applications. This paper explores the landscape of load balancing strategies within Kubernetes, aiming to provide a comprehensive overview of existing techniques, challenges, and best practices. The paper delves into the dynamic nature of Kubernetes environments, where applications scale dynamically, and demand for resources fluctuates. We review various load balancing approaches, including those based on traffic, resource-aware algorithms, and affinity policies. Special attention is given to the unique characteristics of containerized workloads and their impact on load balancing decisions. In this paper the implications of load balancing on the scalability and performance of applications deployed in Kubernetes clusters. It explores the trade-offs between different strategies, considering factors such as response time, throughput, and the adapt-ability to varying workloads. As cloud-native architectures continue to evolve, understanding and addressing the intricacies of load balancing in dynamic con-tainer orchestration environments become increasingly crucial. In this paper we had consolidated the current state of knowledge on load balancing in Kubernetes, providing researchers and practitioners with valuable insights and a foundation for further advancements in the quest for efficient, scalable, and resilient distrib-uted systems.

Diktyo: Network-Aware Scheduling in Container-Based Clouds

Containers have revolutionized application deployment and life-cycle management in current cloud platforms. Applications have evolved from single monoliths to complex graphs of loosely-coupled microservices. However, the efficient allocation of microservice-based applications is challenging due to their complex inter-dependencies. Further, recent applications are becoming even more delay-sensitive, demanding lower latency between dependent microservices. Scheduling policies in popular container orchestration platforms mainly aim to increase the resource efficiency of the infrastructure, insufficient for latency-sensitive applications. Application domains such as the Internet of Things and multi-tier web services would benefit from network-aware policies that consider network latency and bandwidth in the scheduling process. Previous works have studied network-aware scheduling via theoretical formulations or heuristic-based methods evaluated via simulations or small testbeds, making their full applicability in popular platforms difficult. This paper proposes a novel network-aware framework for the popular Kubernetes (K8s) platform named Diktyo that determines the placement of dependent microservices in long-running applications focused on reducing the application’s end-to-end latency and guaranteeing bandwidth reservations. Simulations show that Diktyo can significantly reduce the network latency for various applications across different infrastructure topologies compared to default K8s scheduling plugins. Also, experiments in a K8s cluster with microservice benchmark applications show that Diktyo can increase database throughput by 22% and reduce application response time by 45%.

MicroFog: A framework for scalable placement of microservices-based IoT applications in federated Fog environments

MicroService Architecture (MSA) is gaining rapid popularity for developing large-scale IoT applications for deployment within distributed and resource-constrained Fog computing environments. As a cloud-native application architecture, the true power of microservices comes from their loosely coupled, independently deployable and scalable nature, enabling distributed placement and dynamic composition across federated Fog and Cloud clusters. Thus, it is necessary to develop novel placement algorithms that utilise these microservice characteristics to improve the performance of the applications. However, existing Fog computing frameworks lack support for integrating such placement policies due to their shortcomings in multiple areas, including MSA application placement and deployment across multi-fog multi-cloud environments, dynamic microservice composition across multiple distributed clusters, scalability of the framework to operate within federated environments, support for deploying heterogeneous microservice applications, etc. To this end, we design and implement MicroFog, a Fog computing framework compatible with cloud-native technologies such as Docker, Kubernetes and Istio. MicroFog provides an extensible and configurable control engine that executes placement algorithms and deploys applications across federated Fog environments. Furthermore, MicroFog provides a sufficient abstraction over container orchestration and dynamic microservice composition, thus enabling users to easily incorporate new placement policies and evaluate their performance. The capabilities of the MicroFog framework, such as the scalability and flexibility of the design and deployment architecture of MicroFog and its ability to ensure the deployment and composition of microservices across distributed fog–cloud environments, are validated using multiple use cases. Experiments also demonstrate MicroFog’s ability to integrate and evaluate novel placement policies and load-balancing techniques. To this end, we integrate multiple microservice placement policies to demonstrate MicroFog’s ability to support horizontally scaled placement, service discovery and load balancing of microservices across federated environments, thus reducing the application service response time up to 54%.

Approach to Evaluate Scheduling Strategies in Container Orchestration Systems

This article offers an in-depth examination of scheduling strategies in container orchestration systems, emphasizing the importance of selecting the most suitable strategy for a system's specific needs. The article explores the evolution of container systems and challenges around cloud reliability and fault tolerance. A structured experimental methodology was introduced to compare different strategies systematically, utilizing various testing methods like packing until first rejection, packing until the cluster is full, and introducing different malfunctions such as liveness and network partitioning. Comparative results between the “binpack” and “spread” strategies highlighted that while the former excels in higher scheduling requests acceptance rate, the latter is superior in offering higher availability. The findings show the need for aligning the scheduling strategy with system objectives, be it resource optimization, fault tolerance or other desired characteristics

Research of the efficiency of computing services management platforms in the organization of fog computing

The work is devoted to studying the effectiveness of computing service management platforms in the organization of Fog Computing. As part of the work, the effectiveness of container orchestration platforms with the Fog computing organization is being studied. During the research, it is necessary to complete the following tasks: 1) select literature, scientific publications and Internet articles necessary for the research; 2) inspect container orchestration platforms; 3) determine the key requirements and criteria for conducting the study; 4) design and implement an automatic testing utility; 5) conduct a study of the effec-tiveness of container orchestration platforms with the organization of fog computing; 6) ana-lyze the results obtained and draw related conclusions. Deployment of Docker containers is organized. Docker Swarm is used to create a clus-ter. The problems of measuring the following parameters are solved: deployment time of one container, deployment time of a group of containers, response time of the horizontal zoom task, transmission delay time. The analysis of the obtained test results is carried out.

Orchestration Tools For Efficient Deployment of IoT Applications In Fog Computing: A Systematic Review

Internet of Things (IoT) is the developing technology that enables devices to communicate without human interaction. IoT utilizes cloud computing services to collect and process data for IoT devices and to manage the device remotely. Cloud computing is not efficient enough to handle the fast stream of data produced by the IoT, therefore scaling up IoT applications to meet demands of high peak becomes easier and highly automated in fog computing. Containers are mostly used as virtualization solutions for IoT in fog computing. It enables the execution of small microservices to large applications. However, the rise of many lightweight containers has resulted in new application architectures and fundamentally changing how applications are deployed and visualized. Due to this change, container orchestration tools were proposed. These tools allow users to coordinate and manage containers. However, container orchestration tools need to meet the requirements of IoT applications and constraints imposed on the nodes in fog. This paper presents a systematic literature review on the selection of orchestration tools for the efficient deployment of IoT applications in fog computing. Moreover, the performance of IoT applications must be considered by applying different metrics. This paper aims to propose potential research directions to address identified gaps in the selection of orchestration tools.

Scalable decarbonisation using automated operation optimisation

One of the biggest challenges in facing the climate crisis is the decarbonization of the large and diverse building stock. A reduction of carbon dioxide emissions can be achieved by technical measures and engaging the building occupants to adapt their behaviour. Among the technical measures, implementing predictive control as an upgrade of the existing heating, ventilation, air conditioning and cooling system is especially promising as it allows reductions at potentially low running cost. However, the effort for adapting, implementing and deploying these methods to fit specific buildings and scenarios is high and requires special domain knowledge, hindering the wide-spread application. In this paper, we present a highly automated and data-driven implementation process utilizing an open-source container orchestration system, and the results from real-life case studies in existing buildings in which predictive control was retrofitted. Additionally, occupant information systems were installed in the buildings for increasing transparency about the building performance and the effect of the occupants’ behaviour. The shown method is useful for reducing the time required and manual effort for implementing new control strategies, and thus reducing carbon dioxide emissions while simultaneously increasing thermal comfort and air quality.

DRS: A deep reinforcement learning enhanced Kubernetes scheduler for microservice‐based system

SummaryRecently, Kubernetes is widely used to manage and schedule the resources of microservices in cloud‐native distributed applications, as the most famous container orchestration framework. However, Kubernetes preferentially schedules microservices to nodes with rich and balanced CPU and memory resources on a single node. The native scheduler of Kubernetes, called Kube‐scheduler, may cause resource fragmentation and decrease resource utilization. In this paper, we propose a deep reinforcement learning enhanced Kubernetes scheduler named DRS. We initially frame the Kubernetes scheduling problem as a Markov decision process with intricately designed state, action, and reward structures in an effort to increase resource usage and decrease load imbalance. Then, we design and implement DRS mointor to perceive six parameters concerning resource utilization and create a thorough picture of all available resources globally. Finally, DRS can automatically learn the scheduling policy through interaction with the Kubernetes cluster, without relying on expert knowledge about workload and cluster status. We implement a prototype of DRS in a Kubernetes cluster with five nodes and evaluate its performance. Experimental results highlight that DRS overcomes the shortcomings of Kube‐scheduler and achieves the expected scheduling target with three workloads. With only 3.27% CPU overhead and 0.648% communication delay, DRS outperforms Kube‐scheduler by 27.29% in terms of resource utilization and reduces load imbalance by 2.90 times on average.

Autonomous workload distribution for container-based micro services environments

Microservice architecture represents a cloud application design approach that transfers the intricacies from the conventional monolithic applications to the infrastructure. It involves breaking down the application into small, containerized microservices, each responsible for a specific functional requirement. These microservices can be independently deployed, scaled, and tested through automated orchestration systems. Our paper introduces an autonomous system for distributing workloads among containerized microservices within the cloud like a swarm, designed specifically for microservices operating within OpenVZ containers. This system has the potential to enhance performance compared to existing centralized container orchestration systems.

IMPLEMENTATION OF CONTAINER ORCHESTRATOR MANAGEMENT IN LEARNING MANAGEMENT SYSTEM

The pandemic that started in 2019 made technology even more important. Accelerating digital adaptation and transformation in the education sector, namely the Learning Management System (LMS) is very helpful in the teaching and learning process. Various LMS platforms are widely used and developed by various agencies. Pradita University is also developing an LMS which is made with the node.js programming language and the next.js framework which can be accessed on the pradita.digital site. The choice of using the wrong technology can cause the problematic LMS that is used to become slow and heavy, to the point where it cannot be accessed. The use of technology with the hypervisior concept which was previously commonly used has various weaknesses. For this reason, the Pradita LMS was developed using container orchestrator technology and Rancher Kubernetes as container orchestrator management. By utilizing Rancher as a container orchestrator management, it can make it easier to use with a user friendly GUI. In this study aims to prove that the use of Rancher in improving the quality and performance of LMS Pradita. Among them by identifying system efficiency, providing better scalability, increasing the availability of Pradita LMS, Rancher is able to manage applications and containers used for Pradita LMS, and saving costs used in implementing physical devices. By using the experimental method applied in this study, the results of system efficiency testing and scalability testing carried out with a load of up to 200 users can run properly and efficiently, then the availability test proves that the LMS is still available even when there are workers who are not operating, and testing rancher management capabilities lead to conclusions Rancher runs optimally and uses resources efficiently in accordance with the objectives in this study.