312 research outputs found
Real-Time Containers: A Survey
Container-based virtualization has gained a significant importance in a deployment of software applications in cloud-based environments. The technology fully relies on operating system features and does not require a virtualization layer (hypervisor) that introduces a performance degradation. Container-based virtualization allows to co-locate multiple isolated containers on a single computation node as well as to decompose an application into multiple containers distributed among several hosts (e.g., in fog computing layer). Such a technology seems very promising in other domains as well, e.g., in industrial automation, automotive, and aviation industry where mixed criticality containerized applications from various vendors can be co-located on shared resources.
However, such industrial domains often require real-time behavior (i.e, a capability to meet predefined deadlines). These capabilities are not fully supported by the container-based virtualization yet. In this work, we provide a systematic literature survey study that summarizes the effort of the research community on bringing real-time properties in container-based virtualization. We categorize existing work into main research areas and identify possible immature points of the technology
KuberneTSN: a Deterministic Overlay Network for Time-Sensitive Containerized Environments
The emerging paradigm of resource disaggregation enables the deployment of
cloud-like services across a pool of physical and virtualized resources,
interconnected using a network fabric. This design embodies several benefits in
terms of resource efficiency and cost-effectiveness, service elasticity and
adaptability, etc. Application domains benefiting from such a trend include
cyber-physical systems (CPS), tactile internet, 5G networks and beyond, or
mixed reality applications, all generally embodying heterogeneous Quality of
Service (QoS) requirements. In this context, a key enabling factor to fully
support those mixed-criticality scenarios will be the network and the
system-level support for time-sensitive communication. Although a lot of work
has been conducted on devising efficient orchestration and CPU scheduling
strategies, the networking aspects of performance-critical components remain
largely unstudied. Bridging this gap, we propose KuberneTSN, an original
solution built on the Kubernetes platform, providing support for time-sensitive
traffic to unmodified application binaries. We define an architecture for an
accelerated and deterministic overlay network, which includes kernel-bypassing
networking features as well as a novel userspace packet scheduler compliant
with the Time-Sensitive Networking (TSN) standard. The solution is implemented
as tsn-cni, a Kubernetes network plugin that can coexist alongside popular
alternatives. To assess the validity of the approach, we conduct an
experimental analysis on a real distributed testbed, demonstrating that
KuberneTSN enables applications to easily meet deterministic deadlines,
provides the same guarantees of bare-metal deployments, and outperforms overlay
networks built using the Flannel plugin.Comment: 6 page
ANALYZING THE SYSTEM FEATURES, USABILITY, AND PERFORMANCE OF A CONTAINERIZED APPLICATION ON CLOUD COMPUTING SYSTEMS
This study analyzed the system features, usability, and performance of three serverless cloud computing platforms: Google Cloud’s Cloud Run, Amazon Web Service’s App Runner, and Microsoft Azure’s Container Apps. The analysis was conducted on a containerized mobile application designed to track real-time bus locations for San Antonio public buses on specific routes and provide estimated arrival times for selected bus stops. The study evaluated various system-related features, including service configuration, pricing, and memory & CPU capacity, along with performance metrics such as container latency, Distance Matrix API response time, and CPU utilization for each service. Easy-to-use usability was also evaluated by assessing the quality of documentation, a learning curve for be- ginner users, and a scale-to-zero factor. The results of the analysis revealed that Google’s Cloud Run demonstrated better performance and usability when com- pared to AWS’s App Runner and Microsoft Azure’s Container Apps. Cloud Run exhibited lower latency and faster response time for distance matrix queries. These findings provide valuable insights for selecting an appropriate serverless cloud ser- vice for similar containerized web applications
Edge Computing Architectures for Enabling the Realisation of the Next Generation Robotic Systems
Edge Computing is a promising technology to provide new capabilities in
technological fields that require instantaneous data processing. Researchers in
areas such as machine and deep learning use extensively edge and cloud
computing for their applications, mainly due to the significant computational
and storage resources that they provide. Currently, Robotics is seeking to take
advantage of these capabilities as well, and with the development of 5G
networks, some existing limitations in the field can be overcome. In this
context, it is important to know how to utilize the emerging edge
architectures, what types of edge architectures and platforms exist today and
which of them can and should be used based on each robotic application. In
general, Edge platforms can be implemented and used differently, especially
since there are several providers offering more or less the same set of
services with some essential differences. Thus, this study addresses these
discussions for those who work in the development of the next generation
robotic systems and will help to understand the advantages and disadvantages of
each edge computing architecture in order to choose wisely the right one for
each application.Comment: 7 pages, 4 figures, med 202
RobotKube: Orchestrating Large-Scale Cooperative Multi-Robot Systems with Kubernetes and ROS
Modern cyber-physical systems (CPS) such as Cooperative Intelligent Transport
Systems (C-ITS) are increasingly defined by the software which operates these
systems. In practice, microservice architectures can be employed, which may
consist of containerized microservices running in a cluster comprised of robots
and supporting infrastructure. These microservices need to be orchestrated
dynamically according to ever changing requirements posed at the system.
Additionally, these systems are embedded in DevOps processes aiming at
continually updating and upgrading both the capabilities of CPS components and
of the system as a whole. In this paper, we present RobotKube, an approach to
orchestrating containerized microservices for large-scale cooperative
multi-robot CPS based on Kubernetes. We describe how to automate the
orchestration of software across a CPS, and include the possibility to monitor
and selectively store relevant accruing data. In this context, we present two
main components of such a system: an event detector capable of, e.g.,
requesting the deployment of additional applications, and an application
manager capable of automatically configuring the required changes in the
Kubernetes cluster. By combining the widely adopted Kubernetes platform with
the Robot Operating System (ROS), we enable the use of standard tools and
practices for developing, deploying, scaling, and monitoring microservices in
C-ITS. We demonstrate and evaluate RobotKube in an exemplary and reproducible
use case that we make publicly available at
https://github.com/ika-rwth-aachen/robotkube .Comment: 7 pages, 2 figures, 2 tables; Accepted to be published as part of the
26th IEEE International Conference on Intelligent Transportation Systems
(ITSC), Bilbao, Spain, September 24-28, 202
Comparison between Docker and Kubernetes based Edge Architectures for Enabling Remote Model Predictive Control for Aerial Robots
Edge computing is becoming more and more popular among researchers who seek
to take advantage of the edge resources and the minimal time delays, in order
to run their robotic applications more efficiently. Recently, many edge
architectures have been proposed, each of them having their advantages and
disadvantages, depending on each application. In this work, we present two
different edge architectures for controlling the trajectory of an Unmanned
Aerial Vehicle (UAV). The first architecture is based on docker containers and
the second one is based on kubernetes, while the main framework for operating
the robot is the Robotic Operating System (ROS). The efficiency of the overall
proposed scheme is being evaluated through extended simulations for comparing
the two architectures and the overall results obtained.Comment: 6 pages, 15 figures, conference article, IECON 202
Hard-Real-Time Computing Performance in a Cloud Environment
The United States Department of Defense (DoD) is rapidly working with DoD Services to move from multi-year (e.g., 7-10) traditional acquisition programs to a commercial industrybased approach for software development. While commercial technologies and approaches provide an opportunity for rapid fielding of mission capabilities to pace threats, the suitability of commercial technologies to meet hard-real-time requirements within a surface combat system is unclear. This research establishes technical data to validate the effectiveness and suitability of current commercial technologies to meet the hard-real-time demands of a DoD combat management system. (Moreland Jr., 2013) conducted similar research; however, microservices, containers, and container orchestration technologies were not on the DoD radar at the time. Updated knowledge in this area will inform future DoD roadmaps and investments. A mission-based approach using Mission Engineering will be used to set the context for applied research. A hypothetical yet operationally relevant Strait Transit scenario has been established to provide context for definition of experimental parameters to be set while assessing the hypothesis. System models federated to form a system-of-systems architecture and data from a cloud computing environment are used to collect data for quantitative analysis
A Framework for Comparative Evaluation of High-Performance Virtualized Networking Mechanisms
This paper presents an extension to a software framework designed to evaluate the efficiency of different software and hardware-accelerated virtual switches, each commonly adopted on Linux to provide virtual network connectivity to containers in high-performance scenarios, like in Network Function Virtualization (NFV). We present results from the use of our tools, showing the performance of multiple high-performance networking frameworks on a specific platform, comparing the collected data for various key metrics, namely throughput, latency and scalability, with respect to the required computational power
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