Performance Analysis of Lightweight Container Orchestration Platforms for Edge-Based IoT Applications

Compliance is not security

Containers are widely used by organizations to deploy diverse workloads such as web services, big data, and IoT applications. Container orchestration platforms are designed to manage the deployment of containerized applications in large-scale clusters. The majority of these platforms optimize the scheduling of containers on a fixed-sized cluster and are not enabled to autoscale the size of the cluster nor to consider features specific to public cloud environments. This chapter presents a resource management approach with three objectives: 1) optimize the initial placement of containers by efficiently scheduling them on existing resources, 2) autoscale the number of resources at runtime based on the cluster's workload, and 3) consolidate applications into fewer VMs at runtime. The framework was implemented as a Kubernetes plugin and its efficiency was evaluated on an Australian cloud infrastructure. The experiments demonstrate that a reduction of 58% in cost can be achieved by dynamically managing the cluster size and placement of applications.

Over the last two decades, the IT sector has shifted from managing workloads on physical servers to virtualizing them and then further consolidating them into containers in the subsequent iteration. Later, Kubernetes or Open Shift were used to coordinate container workloads built on Docker and Podman. The use of containerization platforms for high-performance computing (HPC) settings, however, has been trailing behind since there is still much to learn. Containers provide flexibility, adaptability, and maintenance advantages, and they often have lower overhead. Additionally, by monitoring their performance and constantly regulating their behaviour, container orchestration platforms—which are being embraced by companies more and more—are crucial for efficiently managing the life-cycle of multi-container informatics systems. Specifically, the Kubernetes platform, a new open standard for cloud services, is helping to achieve service agnostic deployment by helping to isolate individual cloud providers via its Cloud Controller Manager mechanism. Finding the best container strategy for typical use scenarios and evaluating each solution's advantages and disadvantages were the goals. Basic performance measurements, such as average service recovery time, average transfer rate, and total number of unsuccessful calls, were gathered via a series of organized tests. Docker, Kubernetes, and Proxmox were among the container systems that were examined. Based on a thorough analysis, it can be said that the most efficient high-availability solution for widely used Linux containers is often Docker with Docker Swarm. However, there are other situations where Proxmox excels, such as when load balancing is not a crucial need or where quick data transmission is a top concern.

With the ready availability of appropriate technologies and tools for crafting hybrid clouds, the move towards employing multiple clouds for hosting and running various business workloads is garnering subtle attention. The concept of cloud-native computing is gaining prominence with the faster proliferation of microservices and containers. The faster stability and maturity of container orchestration platforms also greatly contribute towards the cloud-native era. This paper guarantees the following contributions: 1) It describes the key motivations for multi-cloud concept and implementations. 2) It also highlights various key drivers of the multi-cloud paradigm. 3) It presents a brew of challenges that are likely to occur while setting up multi-cloud. 4) It elaborates the proven and potential solution approaches to solve the challenges. The technology-inspired and tool-enabled solution approaches significantly simplify and speed up the adoption of the fast-emerging and evolving multi-cloud concept in the cloud-native era.

In recent years, the popularity of containerization technologies has been growing. When they are used, computational tasks are placed in lightweight containers that can be easily moved between different computing nodes. Containerization using Docker is especially popular at the moment. The use of these solutions opens up enormous opportunities for building distributed and cluster computing systems. To maintain the operability of such systems, special tools are used, and one of them is an orchestrator. However, existing orchestrators are focused on not-so-large computing systems in which performance can be maintained by simply moving computational tasks from non-working nodes to working ones. In large systems with many nodes and a huge number of computational tasks, it is also necessary to take into account the uneven consumption of resources by various tasks. This article proposes a system architecture that can solve the problem of container orchestration using machine learning methods and given the uneven consumption of resources by

The emergence of edge computing has also changed the way contemporary applications are designed, with the focus put on achieving high-resilience, low-latency software programs that are deployed on decentralized and resource-limited systems. Edge-native software is software purposely made to operate at the edge of a network, to allow processing of real-time data, lower bandwidth consumption, and greater fault resiliency. This paradigm is particularly critical to those areas, including autonomous systems, industrial automation, smart cities, and energy infrastructure, that need sub-second responsiveness and system trustworthiness. In this paper, an in-depth design framework of edge-native applications based on microservices architecture is described. Some of the main principles that we discuss are decentralization, data locality, stateless vs. stateful design, redundancy, fault domain isolation and so on. Reference architecture is proposed to support scalable deployments in the event of network partition, and self-healing and management qualities in processing consistent data across heterogeneous edge nodes. We consider powered-up technologies such as lightweight container orchestration (i.e., K3S, Docker), edge communication (i.e., MQTT, gRPC), and Chaos engineering. The performance of the architecture in the latency, fault, and resource stress situations is confirmed by experimental results on the Kubernetes-based edge testbed. The results show that edge-native solutions have the potential to surpass the cloud-based model in measures of latency, throughput, fault recovery, and energy efficiency. We also comment on representative use cases like cars on autopilot and predictive maintenance, and discover such issues as the complexity of orchestration, safety, and managing AI models at the edge

This paper proposes an implementation of container orchestration to accelerate the migration process from one cloud to another. The use of cloud computing as the infrastructure of a system is in high demand. Cloud computing has been adopted in the use of various applications that are very useful for organizations in establishing a system to reduce effort, cost, and time. The system that runs on the same cloud provider has disadvantages. If the cloud provider goes down, then all of the running services will be affected. Therefore, it is needed the solution to migrate the services to some containers with one controller. In this research, two types of architecture are compared, i.e. container orchestration architecture using Kubernetes and simple container architecture using Docker. We use Kubernetes since it is one of the most popular container orchestrators widely used in big company’s productions. The results show that the container creating time average in the container orchestration architecture using Kubernetes is 26 times faster and the percentage average of resource usage on CPU utilization is 3.6 times smaller compared to simple container architecture using Docker.

Container orchestration has emerged as the foundation of modern cloud-native application development, fundamentally transforming how distributed systems are designed, deployed, and managed across enterprise environments. Kubernetes, an open-source container orchestration platform, has emerged as the de facto standard for scaling containerized applications and sits at the center of this transformation. The platform's core components, including Pods, Services, and Deployments, establish the foundational architecture for microservices communication and lifecycle management. Advanced deployment strategies encompassing rolling updates, blue-green transitions, and canary releases enable organizations to maintain continuous service availability while implementing frequent code changes. The horizontal and vertical autoscaling capabilities automatically adjust resource allocation based on real-time demand patterns, ensuring optimal performance during traffic fluctuations. Cluster autoscaling complements application-level scaling through intelligent infrastructure provisioning and cost optimization. Comprehensive monitoring and configuration management features provide deep operational visibility while maintaining security and compliance standards. The declarative configuration model eliminates manual intervention requirements, reducing operational overhead while improving system reliability. Health checking mechanisms through liveness and readiness probes ensure automatic failure detection and recovery. The platform's extensive ecosystem integration capabilities support diverse monitoring, logging, and tracing solutions, creating comprehensive observability across distributed application infrastructures and enabling proactive performance optimization strategies.

The rapid adoption of cloud-native architectures, driven by containerization and orchestration platforms such as Kubernetes, has transformed the scalability and flexibility of modern applications. Yet, achieving true high availability (HA) in such dynamic environments remains challenging, as traditional load balancing approaches—such as Round Robin or Least Connections—are inherently reactive and fail to capture the transient health conditions of containerized workloads. This paper presents an integrated approach to cloud load balancing and container orchestration for high availability through the Predictive and Health-Aware Load Balancing (PHAL) algorithm. PHAL combines two synergistic components: a Predictive Forecasting Module (PFM), which leverages Long Short-Term Memory (LSTM) networks to anticipate workload surges and proactively scale resources, and a Health-Aware Routing Module (HRM), which dynamically distributes traffic across pods based on a composite health score derived from latency, CPU utilization, active connections, and predicted headroom. Through experimental evaluation on a Google Kubernetes Engine cluster running a multi-service e-commerce application, PHAL demonstrates significant improvements over conventional load balancing strategies, including reduced tail latency, enhanced throughput, and resilience against node and pod failures. The study highlights that integrating intelligent cloud load balancing with orchestration frameworks is essential for ensuring robust, fault-tolerant, and highly available cloud-native systems.

The massive industrial adoption of cloud technology has led to research into cloud-enabling traditional applications. The EMD research project proposes an elastic, reliable, and secure cloud-enabled Audio and Video (A/V) collaboration platform in replacement of a reliable hardware appliance based which had fixed constraints in terms of scalability. In this context, this article introduces heuristics and architectures that efficiently and preemptively allocate EMD’s A/V encrypted and container-based software components in the cloud. A software solution based on Kubernetes, a production-grade container orchestration platform, is compared with another solution focused on dedicated VMs. Both implement resource allocation heuristics that take into account the project’s requirements and location-aware encryption enforcement necessities: encryption is enforced for more sensitive data. A company training scenario with dynamically distributed instructors is modelled using existing A/V stream concepts, and component prototypes are extended to support encryption and containerisation, whose prototype performance evaluation drives the investigation of heuristics and architectures and feeds their larger-scale simulation-based assessment. Results show that container orchestration costs are at least 52% lower than dedicated VMs for this scenario, but rely on relaxing a project requirement: the time taken to establish a new streaming session was to be kept below 2 s. The switch to orchestrated containers raised this up to a maximum of 2.5 s.

The advent, popularity and widespread adoption of Cloud-Native architectures and platforms have revolutionized the way in which applications are being built and deployed in the Cloud. Applications designed for Cloud-Native architectures are being migrated from traditional monolithic approach to a Microservices architecture, in which an application is broken down into a collection of small, independent and collaborative services. These applications are dynamically scheduled and managed by Cloud Native platforms. Container Orchestrators (CO), such as Docker, Kubernetes, Cloud Foundry, enable the deployment and management of stateless and scalable microservices. However, as more and more stateful applications and workloads are being deployed in Cloud-Native platforms, these platforms must support the workloads by providing persistent storage to the microservices so that data can be protected and accessed beyond the lifecycle of an ephemeral container. In this paper, we present the design and development of a Cloud-Native storage service orchestration platform by leveraging capabilities of the IBM Ubiquity framework. Our ultimate goal is to provide persistent storage across different container orchestrators and supporting multi-protocol access for Volume management within the storage systems, such as SSH, REST, GPFS native protocol using CLI, NFS and Object. We demonstrate the effectiveness of the storage service orchestration development vision by providing a prototype implementation using Docker and General Parallel Filesystem (GPFS), aiming to provide shared storage volumes to containers orchestrated by Docker. As the volume and variety of microservices continue to grow and to dominate the development and deployments of Cloud applications, we conjecture that the proposed storage service orchestration framework will be a critical enabling building block for supporting elastic, reliable and effective microservice architectures in Cloud datacenters.

Fog computing (also known as edge computing) is a decentralized computing architecture that seeks to minimize service latency and average response time in IoT applications by providing compute and network services physically close to end-users. Fog environment consists of a network of fog nodes and IoT applications are composed of containerized microservices communicating with each other. Due to limited resources of fog nodes, it is often not possible to deploy all the containers of an application on a single fog node. Therefore, communicating containers need to be distributed on multiple fog nodes. Distribution and management of containerized IoT applications is always a critical issue to the system performance in a fog environment. Kubernetes, an open-source system, has grown into a container orchestration standard by simplifying the deployment and management of containerized applications. Despite the progress made by the academia and industry with respect to container management and the wide-scale acceptance of Kubernetes in cloud environments, container management in fog environment is still in the early stage in terms of research and practical deployment. This article aims to fill this gap by analyzing the expediency of Kubernetes container orchestration tool in the fog computing model. The paper also highlights limitations with the current Kubernetes approach and provide ideas for further research to adapt to the needs of the fog environment. Lastly, we provide experiments that demonstrate the feasibility and industrial practicality of deploying and managing containerized IoT applications in the fog computing environment.

Going all the way to IoT with web technologies opens up the door to isomorphic IoT system architectures, which deliver flexible deployment and live migration of code between any device in the overall system. In this vision paper, we propose using WebAssembly to implement lightweight containers and deliver the required portability. Our long-term vision is to use the technology to support developers of liquid IoT applications offering seamless, hassle-free use of multiple devices.

Kubernetes originated from the Greek word κυβερνήτης, meaning “governor,” “helmsman,” or “pilot.” That’s what the founders Joe Beda, Brendan Burns, and Craig McLuckie had in mind. They wanted to “drive a container ship” leading to the creation of a container orchestration platform, which these days is becoming the de facto standard for running microservices in the cloud.

compelling alternatives to traditional virtual machine architectures. This evolution addresses longstanding challenges with VMs, including resource intensity, slow provisioning, and operational complexity. Containers achieve greater efficiency by sharing the host system's kernel while maintaining isolation, enabling faster startup times and improved resource utilization. Kubernetes has emerged as the dominant container orchestration platform, providing automated management for large-scale deployments through its robust architecture of control planes, nodes, pods, services, and deployments. Organizations adopting containerization and Kubernetes benefit from enhanced scalability, optimized resource allocation, simplified deployment processes, and accelerated development cycles. The comprehensive capabilities of Kubernetes—including self-healing, horizontal scaling, service discovery, and automated deployments—have driven widespread adoption across industries, supported by its vendor-neutral design and active community ecosystem.