Container-based Virtualization Technology Research Articles

DeepFogAQ: A fog-assisted decentralized air quality prediction and event detection system

Air pollution is a pervasive environmental and public health concern, prompting the imperative development of predictive systems to facilitate proactive interventions. The complexity of predicting air pollution arises from intricate factors, including the accumulation of pollutants, traffic dynamics, and industrial emissions. Traditional methodologies, reliant on historical or real-time data analysis, often encounter limitations in providing comprehensive and accurate solutions to this multifaceted problem. To address the existing problem, we propose a novel fog-assisted decentralized air quality prediction and event detection system (DeepFogAQ) for managing air pollution of future cities. We integrate Deep Learning (DL), Fog Computing (FC), Complex Event Processing (CEP), and virtualization technologies within the architecture of DeepFogAQ. Specifically, for predicting pollutant concentrations, we employ Transformers, CNN-LSTM, GRU, and RFR models. Additionally, we construct Fog and Cloud layers based on container-based virtualization technology. To demonstrate the feasibility of the system, the developed ML/DL models were run on DeepFogAQ and alarm levels for future air quality were derived. In this way, both the success of the prediction models and the validity of the architecture were ensured. Experimental results showed that Transformers is the most successful model in air quality prediction and event detection. As a result, the proposed DeepFogAQ architecture has the potential to offer a powerful alternative to decision-makers to solve the air pollution problem with its decentralized, scalable, and fault-tolerant structure.

Task Containerization and Container Placement Optimization for MEC: A Joint Communication and Computing Perspective

Containers are used by an increasing number of Internet service providers to deploy their applications in multi-access edge computing (MEC) systems. Although container-based virtualization technologies significantly increase application availability, they may suffer expensive communication overhead and resource use imbalances. However, so far there has been a scarcity of studies to conquer these difficulties. In this paper, we design a workflow-based mathematical model for applications built upon interdependent multitasking composition, formulate a multi-objective combinatorial optimization problem composed of two subproblems—graph partitioning and multi-choice vector bin packing, and propose several joint task-containerization-and -container-placement methods to reduce communication overhead and balance multi-type computing resource utilization. The performance superiority of the proposed algorithms is demonstrated by comparison with the state-of-the-art task and container scheduling schemes.

Container cluster placement in edge computing based on reinforcement learning incorporating graph convolutional networks scheme

Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it is usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster (CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and time-varying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming (MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning (DRL) incorporating Graph Convolutional Networks (GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of placement. The experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.

A Comprehensive Survey on Cloud Computing

Cloud computing is one of the most spectacular technological achievements of the 21st century, allowing users to access various on-demand services (e.g., storage, servers, networks, apps, and services) from anywhere. All advanced technologies such as internet of things (IoT), smart cities, smart grid, system automation, 5G, and logistics systems require services offered by cloud computing. This paper provides a complete architectural tutorial on cloud computing, focusing on various service and deployment models, architectures, inter-clouds concepts, and multiple merits of cloud computing in artificial intelligence, fog computing, edge computing, and IoT. It also discusses different quality-of-service (QoS) parameters that are helpful for the consumers to decide upon the service quality offered by any cloud provider. A comparative study of the hypervisor and container-based virtualization technologies has been discussed. Finally, simulation tools and various open research issues of cloud computing are presented, appealing for the researchers to decide on future research directions.

High-Secured Data Communication for Cloud Enabled Secure Docker Image Sharing Technique Using Blockchain-Based Homomorphic Encryption

In recent years, container-based virtualization technology for edge and cloud computing has advanced dramatically. Virtualization solutions based on Docker Containers provide a more lightweight and efficient virtual environment for Edge and cloud-based applications. Because their use is growing on its own and is still in its early phases, these technologies will face a slew of security issues. Vulnerabilities and malware in Docker container images are two serious security concerns. The risk of privilege escalation is increased because Docker containers share the Linux kernel. This study presents a distributed system framework called Safe Docker Image Sharing with Homomorphic Encryption and Blockchain (SeDIS-HEB). Through homomorphic encryption, authentication, and access management, SeDIS-HEB provides secure docker image sharing. The SeDIS-HEB framework prioritizes the following three major functions: (1) secure docker image upload, (2) secure docker image sharing, and (3) secure docker image download. The proposed framework was evaluated using the InterPlanetary File System (IPFS). Secure Docker images were uploaded using IPFS, preventing unauthorized users from accessing the data contained within the secure Docker images. The SeDIS-HEB results were transparent and ensured the quality of blockchain data access control authentication, docker image metadata denial-of-service protection, and docker image availability.

Employing Vertical Elasticity for Efficient Big Data Processing in Container-Based Cloud Environments

Recently, “Big Data” platform technologies have become crucial for distributed processing of diverse unstructured or semi-structured data as the amount of data generated increases rapidly. In order to effectively manage these Big Data, Cloud Computing has been playing an important role by providing scalable data storage and computing resources for competitive and economical Big Data processing. Accordingly, server virtualization technologies that are the cornerstone of Cloud Computing have attracted a lot of research interests. However, conventional hypervisor-based virtualization can cause performance degradation problems due to its heavily loaded guest operating systems and rigid resource allocations. On the other hand, container-based virtualization technology can provide the same level of service faster with a lightweight capacity by effectively eliminating the guest OS layers. In addition, container-based virtualization enables efficient cloud resource management by dynamically adjusting the allocated computing resources (e.g., CPU and memory) during the runtime through “Vertical Elasticity”. In this paper, we present our practice and experience of employing an adaptive resource utilization scheme for Big Data workloads in container-based cloud environments by leveraging the vertical elasticity of Docker, a representative container-based virtualization technique. We perform extensive experiments running several Big Data workloads on representative Big Data platforms: Apache Hadoop and Spark. During the workload executions, our adaptive resource utilization scheme periodically monitors the resource usage patterns of running containers and dynamically adjusts allocated computing resources that could result in substantial improvements in the overall system throughput.

Performance Comparison of Multiple Containers Running Artificial Intelligence Applications

Due to the performance overhead of virtual machines, traditional virtualization solutions are generally not suitable for artificial intelligence applications. Container-based virtualization technology represented by Docker can provide a customized environment, good portability, good compatibility and low overhead. In this article, we give a performance comparison among Docker, Singularity, and Charliecloud containers. In particular, CPU and GPU are used in each container to run a real application of artificial intelligence for the performance comparison. Finally, the experimental results are given and discussed.

Virtualization in Cloud Computing: Moving from Hypervisor to Containerization—A Survey

Containers emerged as a lightweight alternative to virtual machines that offer better microservice architecture support. They are widely used by organizations to deploy their increasingly diverse workloads derived from modern applications such as big data, IoT, and edge/fog computing in either proprietary clusters or private, public cloud data centers. With the growing interest in container-based virtualization technologies, the requirement to explore the deployment and orchestration of clusters of containers has become a central research problem. Although progress has been made to study containerization, systematic consolidation of the existing literature with a summative evaluation is still missing. To fill this gap, in this paper, we first taxonomically classify the existing research studies on the performance comparison between hypervisor and container technology and then analyze state-of-the-art for container cluster management orchestration systems, its performance monitoring tools, and finally future research trends. This results in a better understanding of container technology with attention to provide summative analysis in terms of (i) how much performance overhead is generated by a hypervisor compared to container-based virtualization, (ii) which container technology is suited for a cloud application deployment based on the type of benchmark executing, (iii) how to provide management of containers deployed in a cluster environment, (iv) container performance monitoring tools, and (v) finally emerging concerns for future research directions.

Containers and orchestration of numerical ocean model for computational reproducibility and portability in public and private clouds: Application of ROMS 3.6

Various numerical models have been used to understand and predict ocean dynamics. For this reason, many information technology (IT) resources are required for high-resolution global ocean modeling. The development of cloud-computing technologies has enabled earth scientists to easily use numerical ocean models that require high-performance computing (HPC) and message-passing interface (MPI) software in private and public clouds. Although it is easier today to use computing resources than it was in the past, computational reproducibility and portability in diverse IT environments remain crucial issues. This study proposes a model execution architecture for computational reproducibility, portability, and agility based on container-based virtualization and orchestration technologies. We implement a containerized regional ocean-modeling system (ROMS), an MPI-based numerical ocean model that exists in various public or private cloud environments (e.g., personal computers and multiple-node servers). The preparation time for model setup is greatly reduced using our container-based HPC architecture. Containerization of ROMS is tested for its support of the portability of numerical modeling in a wide range of public-cloud environments. When leveraging an abstraction layer of complex and diverse infrastructure environments, we can run the ocean model more easily while obtaining computational reproducibility using a shareable deployment code. This advancement can be used to guide the containerization of various numerical models and to run them in parallel in public and private cloud-computing environments.

Deployment and Migration of Virtualized Services with Joint Optimization of Backhaul Bandwidth and Load Balancing in Mobile Edge-Cloud Environments

Mobile edge-cloud computing environments appear as a novel computing paradigm to offer effective processing and storage solutions for delay sensitive applications. Besides, the container based virtualization technology becomes solicited due to its natural lightweight and portability as well as its small migration overhead that leads to seamless service migration and load balancing. However, with the mobility property, the users’ demands in terms of the backhaul bandwidth is a critical parameter that influences the delay constraints of the running applications. Accordingly, a Binary Integer Programming (BIP) optimization problem is formulated. It minimizes the users’ perceived backhaul delays and enhances the load-balancing degree in order to offer more chance to accept new requests along the network. Also, by introducing bandwidth constraints, the available user backhaul bandwidth after the placement are enhanced. Then, the adopted methodology to design two heuristic algorithms based on Ant Colony System (ACS) and Simulated Annealing (SA) is presented. The proposed schemes are compared using different metrics,and the benefits of the ACS-based solution compared to the SA-based as well as a genetic algorithm (GA) based solutions are demonstrated. Indeed, the normalized cost and the total backhaul costs are given by more optimal values using the ACS algorithm compared to the other solutions.

Performance Evaluation of Containerization in Edge-Cloud Computing Stacks for Industrial Applications: A Client Perspective

Today, the edge-cloud computing paradigm starts to gain increasing popularity, aiming to enable short latency, fast decision-making and intelligence at the network edge, especially for industrial applications. The container-based virtualization technology has been put on the roadmap by the industry to implement edge-cloud computing infrastructures. Has the performance of the container-based edge-cloud computing stacks reached industry requirement? In this paper, from the industrial client perspective, we provide a performance evaluation methodology and apply it to the state-of-the-art containerization-based edge-cloud computing infrastructures. The influences of the message sending interval, payload, network bandwidth and concurrent devices on full stack latency are measured, and the processing capability of executing machine learning tasks are benchmarked. The results show that containerization on the edge does not introduce noticeable performance degradation in terms of communication, computing and intelligence capabilities, making it a promising technology for the edge-cloud computing paradigm. However, there is a large room for performance improvement between current implementation of the edge-cloud infrastructure and the demanding requirements anticipated by time-critical industrial applications. We also emphasize and showcase that partitioning of an industrial application into microservices throughout the whole stack can be considered during solution design. The proposed evaluation methodology can be a reference to users of edge-cloud computing as well as developers to get a client perspective overview of system performance.

An analysis of container-based virtualization technologies

An analysis of container-based virtualization technologies

Deploying a Kubernetes Cluster with Kubernetes Operation kops on AWS Cloud Experiments and Lessons Learned

In today's world, new virtualization technologies have become popular like container-based virtualization technologies due to its nature like lightweight, scalability, and flexibility. Kubernetes is the most popular open-source platform for container-based cluster management systems that provide mechanisms for deploying, maintaining, and scaling containerized applications across a cluster of hosts. KOPS is a Kubernetes Operations for attempting at simplifying complex installation and maintenance processes of the Kubernetes cluster. Kops helps in deploying highly available Kubernetes cluster, make installation secure, easy, and automatable applications on the AWS cloud. The rapid increase of cloud-native applications has taken cloud computing to the next level, but there are several issues in the deployment of automatable applications, and also complexity increases due to this. Nevertheless, there are still issues to be solved, such as cluster monitoring and cluster deployment automation. The goal of this paper is to deploy a containerized application onto a Kubernetes cluster using KOPS on cloud providers like AWS. This paper discusses how the Kubernetes cluster is deployed on the cloud service provider like AWS cloud using Kops which is the best tool for deploying Kubernetes cluster on AWS and also the Kubernetes services available today on cloud providers like AWS cloud and examines what they offer in comparison with other cloud providers like Google(GKE), Azure(AKE), and also the security issues in AWS cloud provider.

Building Autonomic Elements from Video-Streaming Servers

HTTP Streaming is nowadays the main approach for delivering video-streaming on the Internet. As a consequence of that, the widely deployed HTTP infrastructures face new challenges posed by the sensitivity of video-streaming users to service quality degradation and the specificities of video-streaming workloads. Performance issues represent one main class of problems in the server infrastructure that can result into a significant deterioration of the end-users’ quality of experience (QoE), proportional to the upfront time spent by them watching the videos. This paper addresses the development of autonomic HTTP Streaming servers organized into Autonomic Elements (AEs), the building blocks of Autonomic Computing (AC) systems. AEs are structured using container-based virtualization and are provided with monitoring, failure prediction, failure diagnosis and repair features. These features are incorporated into SHStream, a self-healing framework developed by us. SHStream relies on online learning algorithms to build and evaluate classification models dynamically for prediction and diagnosis of performance anomalies. The results of our experimental analysis have shown that: (1) failure prediction can be performed with approximately $$98\%$$ of recall and $$99\%$$ of precision; (2) the diagnosis activity can localize and identify the resource responsible for performance failures, without misclassifications; (3) the classifiers’ performance stabilizes using a small number of learning instances; and (4) container-based virtualization technologies enable recovery times shorter than 1 s through rebooting and shorter than 3 s using server migration techniques.

Mobile Edge Computing Platform with Container-Based Virtualization Technology for IoT Applications

The next generation of fifth generation (5G) network, implementing mobile edge computing (MEC), network function virtualization (NFV) and software defined networking technologies, establishes a flexible and resilient network in line with various internet of things (IoT) devices. While NFV adds flexibility scale in or out networks by allowing network functions to be dynamically deployed and inter-connected, MEC provide intelligence at the edge of a mobile network; reduces latency, and increases capacity. With the diverse development of networking applications, the proposed MEC with container-based virtualization technology (CVT) as IoT gateway with IoT devices for flow control mechanism in scheduling and analysis methods will effectively enhance the quality of service. In this work, the proposed IoT gateway will be analyzed to elucidate the combined effect of simultaneously deploying virtual network functions and MEC applications on the same network infrastructure. Low latency, high bandwidth and high agility, supporting the connection of large-scale devices, and the efficient combination of resources from network edge and cluster clouds, account for real-time network conditions, reducing the IoT applications and services to indicate that a number is the average of 30% of the latency, that could get more suitable service quality to develop such as both augmented reality and virtual reality application intelligence in coming 5G network.

5G Virtualized Multi-access Edge Computing Platform for IoT Applications

The next generation of fifth generation (5G) network, which is implemented using Virtualized Multi-access Edge Computing (vMEC), Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies, is a flexible and resilient network that supports various Internet of Things (IoT) devices. While NFV provides flexibility by allowing network functions to be dynamically deployed and inter-connected, vMEC provides intelligence at the edge of the mobile network reduces latency and increases the available capacity. With the diverse development of networking applications, the proposed vMEC use of Container-based Virtualization Technology (CVT) as gateway with IoT devices for flow control mechanism in scheduling and analysis methods will effectively increase the application Quality of Service (QoS). In this work, the proposed IoT gateway is analyzed. The combined effect of simultaneously deploying Virtual Network Functions (VNFs) and vMEC applications on a single network infrastructure, and critically in effecting exhibits low latency, high bandwidth and agility that will be able to connect large scale of devices. The proposed platform efficiently exploiting resources from edge computing and cloud computing, and takes IoT applications that adapt to network conditions to degrade an average 30% of end to end network latency.

LXCloud-CR: Towards LinuX Containers Distributed Hash Table based Checkpoint-Restart

Infrastructure-as-a-Service (IaaS) container-based virtualization technology is gaining, over these years, significant interest in industry as an alternative platform for running distributed applications. They present an interesting alternative to virtual machines in the Cloud due to newer advances in container-based virtualization as a new technology that simplify the deployment of applications. As the cloud architectures continue to grow in scale and complexity, faults become very recurrent which make reliability a true challenge. Given the dynamic nature of IaaS clouds and the pay-as-you-go cloud model where the costs are directly proportional to the resource usage, a Checkpoint Restart mechanism is essential in this context. We propose LXCloud-CR our new decentralized Checkpoint-Restart model based on a distributed checkpoints repository using key–value store on a Distributed Hash Table (DHT). It is able to take snapshots of the whole Linux Container (LXC) instances. LXCloud-CR aims to reduce the runtime and storage overheads of checkpointing. Large scale experiments on the Grid’5000 testbed demonstrate the benefits of our proposal. Obtained results validate our model and improve the performance of applications.

An Empirical Performance Evaluation of Docker Container, Openstack Virtual Machine and Bare Metal Server

<p>Server virtualization is a fundamental technological innovation that is used extensively in IT enterprises. Server virtualization enables creation of multiple virtual machines on single underlying physical machine. It is realized either in form of hypervisors or containers. Hypervisor is an extra layer of abstraction between the hardware and virtual machines that emulates underlying hardware. In contrast, the more recent container-based virtualization technology runs on host kernel without additional layer of abstraction.Thus container technology is expected to provide near native performance compared to hypervisor based technology. We have conducted a series of experiments to measure and compare the performance of workloads over hypervisor based virtual machines, Docker containers and native bare metal machine. We use a standard benchmark workload suite that stress CPU, memory, disk IO and system. The results obtained show that Docker containers provide better or similar performance compared to traditional hypervisor based virtual machines in almost all the tests. However as expected the native system still provides the best performance as compared to either containers or hypervisors.</p>

Container-based virtualization technologies

AbstractAs a newer concept, in comparison to hypervisor technologies, container-based virtualization is a rather lightweight virtualization concept. By not emulating any hardware it has a much lower overhead with good isolation. The basic idea is to generate isolated containers which use the same kernel as the host system, instead of individual ones per virtual machine. Hence the virtualized applications or systems have to be compatible to the same kernel. Networking testbeds like ToMaTo can benefit from such technology, since it allows to run many virtual machines in parallel. In this paper, three representatives of Linux container-based virtualization technologies will be presented: OpenVZ, Linux-VServer and LXC. The main features and concepts of each technology will be discussed, followed by a comparison about performance, security, virtualization system integration and client software. At the end their value for ToMaTo will be rated.

사용자 맞춤형 분산 컴퓨팅을 위한 컨테이너 기반 클러스터 관리 시스템

Several fields of science have traditionally demanded large-scale workflow support, which requires thousands of central processing unit (CPU) cores. In order to support such large-scale scientific workflows, large-capacity cluster systems such as supercomputers are widely used. However, as users require a diversity of software packages and configurations, a system administrator has some trouble in making a service environment in real time. In this paper, we present a container-based cluster management platform and introduce an implementation case to minimize performance reduction and dynamically provide a distributed computing environment desired by users. This paper offers the following contributions. First, a container-based virtualization technology is assimilated with a resource and job management system to expand applicability to support large-scale scientific workflows. Second, an implementation case in which docker and HTCondor are interlocked is introduced. Lastly, docker and native performance comparison results using two widely known benchmark tools and Monte-Carlo simulation implemented using various programming languages are presented.