Container-based Virtualization 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.

Toward Optimal Virtualization: An Updated Comparative Analysis of Docker and LXD Container Technologies

Traditional hypervisor-assisted virtualization is a leading virtualization technology in data centers, providing cost savings (CapEx and OpEx), high availability, and disaster recovery. However, its inherent overhead may hinder performance and seems not scale or be flexible enough for certain applications, such as microservices, where deploying an application using a virtual machine is a longer and resource-intensive process. Container-based virtualization has received attention, especially with Docker, as an alternative, which also facilitates continuous integration/continuous deployment (CI/CD). Meanwhile, LXD has reactivated the interest in Linux LXC containers, which provides unique operations, including live migration and full OS emulation. A careful analysis of both options is crucial for organizations to decide which best suits their needs. This study revisits key concepts about containers, exposes the advantages and limitations of each container technology, and provides an up-to-date performance comparison between both types of containers (applicational vs. system). Using extensive benchmarks and well-known workload metrics such as CPU scores, disk speed, and network throughput, we assess their performance and quantify their virtualization overhead. Our results show a clear overall trend toward meritorious performance and the maturity of both technologies (Docker and LXD), with low overhead and scalable performance. Notably, LXD shows greater stability with consistent performance variability.

Hierarchical Resource Orchestration Framework for Real-time Containers

Container-based virtualization is a promising deployment model in fog and edge computing applications, because it allows a seamless co-existence of virtualized applications in a heterogeneous environment without introducing significant overhead. Certain application domains (e.g., industrial automation, automotive, or aerospace) mandate that applications exhibit a certain degree of temporal predictability. Container-based virtualization cannot be easily used for such applications, since the technology is not designed to support real-time properties and handle temporal disturbances. This article proposes a framework consisting of a static offline and a dynamic online phase for resource allocation and adaptive re-dimensioning of real-time containers. In the offline phase, the optimal initial deployment and dimensioning of containers are decided based on ideal system models. Additionally, to adapt to dynamic variations caused by changing workloads or interferences, the online phase adapts the CPU usage and limits of real-time containers at runtime to improve the real-time behavior of the real-time containerized applications while optimizing resource usage. We implement the framework in a real Linux-based system and show through a series of experiments that the proposed framework is able to adjust and re-distribute computing resources between containers to improve the real-time behavior of containerized applications in the presence of temporal disturbances while optimizing resource usage.

Service Reliability Based on Fault Prediction and Container Migration in Edge Computing

With improvements in the computing capability of edge devices and the emergence of edge computing, an increasing number of services are being deployed on the edge side, and container-based virtualization is used to deploy services to improve resource utilization. This has led to challenges in reliability because services deployed on edge nodes are pruned owing to hardware failures and a lack of technical support. To solve this reliability problem, we propose a solution based on fault prediction combined with container migration to address the service failure problem caused by node failure. This approach comprises two major steps: fault prediction and container migration. Fault prediction collects the log of services on edge nodes and uses these data to conduct time-sequence modeling. Machine-learning algorithms are chosen to predict faults on the edge. Container migration is modeled as an optimization problem. A migration node selection approach based on a genetic algorithm is proposed to determine the most suitable migration target to migrate container services on the device and ensure the reliability of the services. Simulation results show that the proposed approach can effectively predict device faults and migrate services based on the optimal container migration strategy to avoid service failures deployed on edge devices and ensure service reliability.

Container-based Virtualization for Real-time Industrial Systems—A Systematic Review

Industrial Automation and Control systems have matured into a stable infrastructure model that has been kept fundamentally unchanged, using discrete embedded systems (such as Programmable Logic Controllers) to implement the first line of sensorization, actuation, and process control and stations and servers providing monitoring, supervision, logging/database and data-sharing capabilities, among others. More recently, with the emergence of the Industry 4.0 paradigm and the need for more flexibility, there has been a steady trend towards virtualizing some of the automation station/server components, first by using virtual machines and, more recently, by using container technology. This trend is pushing for better support for real-time requirements on enabling virtualization technologies such as virtual machines and containers. This article provides a systematic review on the use of container virtualization in real-time environments such as cyber-physical systems, assessing how existing and emerging technologies can fulfill the associated requirements. Starting by reviewing fundamental concepts related to container technology and real-time requirements, it goes on to present the methodology and results of a systematic study of 37 selected papers covering aspects related to the enforcement of real-time constrains within container hosts and the expected task latency on such environments, as well as an overview of container platforms and orchestration mechanisms for RT systems.

DAScheduler: Dependency-Aware Scheduling Algorithm for Containerized Dependent Jobs

Containers have emerged recently as a cloud technology for improving and managing cloud resources. They improve resource sharing by allowing instances to run on top of the host’s operating system. Container-based virtualization runs and manages hosted instances via the host kernel. Resource sharing can cause resource contention. In addition, dependent jobs, which may be deployed across multiple hosts, require frequent communication, resulting in a high volume of network traffic and network contention. The majority of existing research focuses on load balancing, with no consideration for the fact that network contention also plays a significant role in container performance. In this research, we propose a Dependency-aware Scheduling algorithm (DAScheduler) that deploys jobs into containers while accounting for both load balancing and job dependencies. The experimental results show that DAScheduler reduces network traffic by more than half and balances the loads. In comparison to one of the existing state-of-the-art techniques, DAScheduler improves overall cloud performance.

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.

CONTAINER-BASED VIRTUALIZATION FOR BLOCKCHAIN TECHNOLOGY: A SURVEY

Blockchain technology has garnered interest in several scientific and engineering fields. To improve blockchain services, its execution challenges must be addressed. Container-based virtualization enables running isolated apps on a shared OS where blockchain technology can leverage this technology to run numerous nodes, smart contracts, and decentralized apps in distinct containers allowing resource isolation and allocation, faster deployment and scalability, and improved security through limited host OS and other container access. This article covers container-based virtualization for Blockchain technology, including current methodologies, prospects, and future perspectives. Initially, this study explains blockchain and containerization as well as the reason for their integration. Then reviews container virtualization services to address blockchain complexity, size, scalability, and security. Conversely, Container technology uses blockchain to protect data and enhance resource management. Next, analyzes the latest containerization and blockchain integration studies. Finally, difficulties and future directions are considered to advance this promising research.

On the use of container-based virtualisation for IoT provisioning and orchestration: a survey

On the use of container-based virtualisation for IoT provisioning and orchestration: a survey

On the use of container-based virtualisation for IoT provisioning and orchestration: a survey

On the use of container-based virtualisation for IoT provisioning and orchestration: a survey

Characterizing and optimizing Kernel resource isolation for containers

Container-based virtualization has become increasingly popular as a lightweight alternative to hypervisor-based virtualization in cloud computing. Isolation is a fundamental property for consistent and reliable performance for cloud environment. However, the isolation between containers is much weaker than virtual machines as containers on the same host share one underlying host kernel. Existing works have mainly focused on the isolation problems at physical resources (e.g. CPU) level and almost not discussed with kernel resources (e.g. lock). In this paper, we perform a study to quantify kernel resource isolation for containers with a new microbenchmark, KRIBench. Then we describe kernel resource isolation issues and identify several kernel resources competition behind the poor isolation. Furthermore, we design and implement Valve, a general and flexible system that reduces kernel resources competition through limiting usage of system calls. Valve adopts Pareto-based container identification to locate misbehaving containers and supply–demand model to manage usage of system calls. The evaluation results demonstrate that our system can effectively enhance the kernel resource isolation for containers with negligible performance overhead.

GWO-Based Simulated Annealing Approach for Load Balancing in Cloud for Hosting Container as a Service

Container-based virtualization has gained significant popularity in recent years because of its simplicity in deployment and adaptability in terms of cloud resource provisioning. Containerization technology is the recent development in cloud computing systems that is more efficient, reliable, and has better overall performance than a traditional virtual machine (VM) based technology. Containerized clouds produce better performance by maximizing host-level resource utilization and using a load-balancing technique. To this end, this article concentrates on distributing the workload among all available servers evenly. In this paper, we propose a Grey Wolf Optimization (GWO) based Simulated Annealing approach to counter the problem of load balancing in the containerized cloud that also considers the deadline miss rate. We have compared our results with the Genetic and Particle Swarm Optimization algorithm and evaluated the proposed algorithms by considering the parameter load variation and makespan. Our experimental result shows that, in most cases, more than 97% of the tasks were meeting their deadline and the Grey Wolf Optimization Algorithm with Simulated Annealing (GWO-SA) performs better than all other approaches in terms of load variation and makespan.

Cold-start aware cloud-native service function chain caching in resource-constrained edge: A reinforcement learning approach

Virtualized Network Functions (VNF), Service Function Chains (SFC) and Network Functions Virtualization (NFV) architecture are promising basis of modern network infrastructures. How to make the best use of the limited resource at the edge yet achieve acceptable latency performance is one of the key challenges. Further, cloud-native network functions (CNF) enables a more flexible architecture with container-based virtualization, yet brings the problem of cold-start handling, since transferring and booting a container image can bring an indispensable latency. We formulate the cold-start aware cloud-native SFC caching problem as a mathematical optimization problem with a set of constraints based on the resource limitation and performance requirement. To efficiently handle this problem, which has been proved to be NP-Hard, we design a deep reinforcement learning (DRL) approach, along with two graph neural network-based embedding networks for the extraction of backbone network graph and caching request information, respectively. The resulting DRL agent is able to learn caching decisions, aiming at optimizing the processing latency, sub-frame processing latency, and launch latency performance while maintaining the request acceptance ratio. Extensive simulations conducted on multiple backbone network structures and various request load suggest that the proposed approach outperforms the state-of-the-art solutions in request acceptance ratio, latency performance under high loads, and cold-start handling with little extra execution time overhead.

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.

Container Elasticity: Based on Response Time using Docker

Objective:: Cloud computing is an approach to provide the computing resources (machine) to end-users for running their application over the Internet. The computing resources consist of various things (e.g. RAM, Memory, CORE, etc.). These resources are allocated to an application without human intervention for managing the fluctuating workload. To manage the real-time fluctuating workload, cloud providers use VM based or Container-based virtualization to host the client services. Adding/removing resources dynamically as per the demand of application through cloud is known as elasticity. Cloud providers use the auto-scaling mechanism to implement elasticity. A machine that hosts an application can be either overloaded or under-loaded due to the real-time fluctuating workload. The cloud providers use an auto-scaling mechanism to automatically scale up or down the computing resources at the right moment for managing the real-time fluctuating workload. The failure of allocation/de-allocation of resources at right time leads to SLA violation, service unavailability, customers lost, more power consumption, minimum throughput and maximum response time. Hence, the allocation/de-allocation of resources at right moment becomes critical for successful completion of task in dynamic environment efficiently. Methods:: Resource provisioning for managing dynamic and fluctuating workload has been achieved through an algorithm (PID with dynamic HAProxy) which is based on decision-making approach that depends on response time of container using mechanism of control theory. Results:: The proposed work has improved performance of the system in terms of resource utilization and response time to manage the fluctuating workload. Conclusion:: The addition/removal of containers dynamically to manage fluctuating workload can be achieved more efficiently.

Dynamic Scheduling of Contextually Categorised Internet of Things Services in Fog Computing Environment.

Fog computing emerged as a concept that responds to the requirements of upcoming solutions requiring optimizations primarily in the context of the following QoS parameters: latency, throughput, reliability, security, and network traffic reduction. The rapid development of local computing devices and container-based virtualization enabled the application of fog computing within the IoT environment. However, it is necessary to utilize algorithm-based service scheduling that considers the targeted QoS parameters to optimize the service performance and reach the potential of the fog computing concept. In this paper, we first describe our categorization of IoT services that affects the execution of our scheduling algorithm. Secondly, we propose our scheduling algorithm that considers the context of processing devices, user context, and service context to determine the optimal schedule for the execution of service components across the distributed fog-to-cloud environment. The conducted simulations confirmed the performance of the proposed algorithm and showcased its major contribution—dynamic scheduling, i.e., the responsiveness to the volatile QoS parameters due to changeable network conditions. Thus, we successfully demonstrated that our dynamic scheduling algorithm enhances the efficiency of service performance based on the targeted QoS criteria of the specific service scenario.

A Docker Container Anomaly Monitoring System Based on Optimized Isolation Forest

Container-based virtualization has gradually become a main solution in today‘s cloud computing environments. Detecting and analyzing anomaly in containers present a major challenge for cloud vendors and users. This paper proposes an online container anomaly detection system by monitoring and analyzing multidimensional resource metrics of the containers based on the optimized isolation forest algorithm. To improve the detection accuracy, it assigns each resource metric a weight and changes the random feature selection in the isolation forest algorithm to the weighted feature selection according to the resource bias of the container. In addition, it can identify abnormal resource metrics and automatically adjust the monitoring period to reduce the monitoring delay and system overhead. Moreover, it can locate the cause of the anomalies via analyzing and exploring the container log. The experimental results demonstrate the performance and efficiency of the system on detecting the typical anomalies in containers in both simulated and real cloud environments.

Latency and quality-aware task offloading in multi-node next generation RANs

Next-Generation Radio Access Network (NG-RAN) is an emerging paradigm that provides flexible distribution of cloud computing and radio capabilities at the edge of the wireless Radio Access Points (RAPs). Computation at the edge bridges the gap for roaming end users, enabling access to rich services and applications. In this paper, we propose a multi-edge node task offloading system, i.e., QLRan, a novel optimization solution for latency and quality tradeoff task allocation in NG-RANs. Considering constraints on service latency, quality loss, edge capacity, and task assignment, the problem of joint task offloading, latency, and Quality Loss of Result (QLR) is formulated in order to minimize the User Equipment (UEs) task offloading utility, which is measured by a weighted sum of reductions in task completion time and QLR cost. The QLRan optimization problem is proved as a Mixed Integer Nonlinear Program (MINLP) problem, which is a NP-hard problem. To efficiently solve the QLRan optimization problem, we utilize Linear Programming (LP)-based approach that can be later solved by using convex optimization techniques. Additionally, a programmable NG-RAN testbed is presented where the Central Unit (CU), Distributed Unit (DU), and UE are realized by USRP boards and fully container-based virtualization approaches. Specifically, we use OpenAirInterface (OAI) and Docker software platforms to deploy and perform the NG-RAN testbed for different functional split options. Then, we characterize the performance in terms of data input, memory usage, and average processing time with respect to QLR levels. Simulation results show that our algorithm performs significantly improves the network latency over different configurations.