An Improved Container Cloud Resource Scheduling Strategy

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.

With the advent of the fourth industrial revolution, industry practitioners are moving towards container-based infrastructure for managing their digital workloads. Kubernetes, a container orchestration tool, is reported to help industry practitioners in automated management of cloud infrastructure and rapid deployment of software services. Despite reported benefits, Kubernetes installations are susceptible to security defects, as it occurred for Tesla in 2018. Understanding how frequently security defects appear in Kubernetes installations can help cybersecurity researchers to investigate security-related vulnerabilities for Kubernetes and generate security best practices to avoid them. In this position paper, we first quantify how frequently security defects appear in Kubernetes manifests, i.e., configuration files that are use to install and manage Kubernetes. Next, we lay out a list of future research directions that researchers can pursue. We apply qualitative analysis on 5,193 commits collected from 38 open source repositories and observe that 0.79% of the 5,193 commits are security-related. Based on our findings, we posit that security-related defects are under-reported and advocate for rigorous research that can systematically identify undiscovered security defects that exist in Kubernetes manifests. We predict that the increasing use of Kubernetes with unresolved security defects can lead to large-scale security breaches.

Cloud Computing is online technology where computing resources like hardware, software and applications are available as per the user's needs. A cloud computing architecture microservices-based application involves multiple microservices deployed, updated, and redeployed on lightweight virtualization technology called docker container rather than hypervisor-based virtualization. Docker Swarm, Kubernetes and Apache Mesos are container orchestration tool for scheduling and managing individual Container for microservice application within a cluster of private cloud and public cloud. Docker container orchestration can include creating and scheduling Container, availability of container and the host machine, rescheduling of failed Container, scaling of Container to balance the workload on infrastructure and securing the interaction between Container. This survey provides a complete description of docker container orchestration approaches with containers, analyzing the framework and classification of container orchestration management.KeywordsDockerContainerOrchestrationMicro-services

The resource scheduling problem becomes complex, as resources are distributed, heterogeneous, dynamic and autonomous. A resource scheduling scheme with three-level for grid computing is proposed in this paper by analyzing present resource scheduling scheme. Petri net is a powerful graphical and mathematics tool for describing the concurrent, asynchronous and dynamic events. This paper models and analyzes the resource scheduling scheme of grid computing using hierarchical Petri net. Different Petri net models for resource scheduling of different layers are given in this paper. Petri model for different layer has different behavior, which represents heterogeneous and autonomous features of grid computing resources. Finally, we get some important results such as throughput, load balance and makespan on resource scheduling by analyzing reachability of Petri nets.

In a 5G ultra-dense network, dynamic network topology and traffic patterns lead to excessive system overhead and complex radio resource conflicts. The cloud radio access network and the fog computing have the advantages of high computation capabilities and low transmission delays. Therefore, by taking full advantage of these two characteristics, this study proposes a novel radio resource coordination and scheduling scheme in an ultra-dense cloud-based small cell network. Interference among small cells (or remote radio heads) can be avoided by implementing centralized cooperative processing in the base band unit pool in advance. Resource sharing in coordination and transfer depend on fog computing to relieve the overloaded cloud processing platform and reduce transmission delays, thereby maximizing resource utilization and minimizing system overhead when the network topology and number of users change dynamically. The simulation shows that the proposed scheme can increase the system throughput by 20% compared with the clustering-based algorithm; it can also increase system throughput by 33% compared with the graph coloring algorithm, decrease the signaling overhead by about 50%, and improve network’s quality of service.

Resource scheduling and performance analysis of hybrid renewable energy systems with carbon neutrality consideration: A scenario-based multi-agent approach

The planning and deployment of mega constellations has posed increasing pressure on the existing telemetry, track and command (TTC) resource scheduling strategy. The current plan-based scheduling strategy distributes resources according to needs, however, it cannot address sudden resource requests with prompt responses. By analyzing the status quo of resource scheduling systems, we have proposed a distributed collaborative scheduling technology for random access in real-time, based on the theory of low costs and rapid transfers of scheduling task resources. First, we use knowledge graph technology to map the link between scheduling tasks and idle resources and develop a graph network. Furthermore, by analyzing and evaluating the resource transfer capability of a multi-target TTC system in hemispherical coverage (MT-TTC), we propose the concept of a virtual resource pool and develop a two-layer scheduling framework in which the central scheduling system coordinates with multiple regional systems. This scheduling strategy works well when a sudden TTC request occurs. The central system manages the satellite requests and make plans, while regional systems are responsible for handling the sudden requests and report to the central system on its resource change. With regional systems sharing some degree of autonomy, the central system making a final decision and systems across the framework coordinating in real-time, we design a model on resource transfers for MT-TTC equipment and a collaboration mechanism that coordinates the resource transfers across MT-TTC equipment and other single-beam equipment within and between regions. We do experiments in simulation scheduling environments. The results show that the regional scheduling system based on MT-TTC equipment is able to manage continuous random-access requests in real-time, and avoid resource conflicts across regions by collaborating with the central scheduling system. It is of value to be applied in real scenarios.

Nowadays, Kubernetes is an advanced container orchestration tool due to its high reliability, scalability and fault tolerance. However, Kubernetes requires a significant number of resources for its work. Therefore, to ensure the operation of Kubernetes in conditions of limited resources, lightweight analogues such as MicroKubernetes and K3S were created. These platforms provide easier deployment and support. In this paper, the authors analyze performance metrics for orchestration actions such as adding/removing nodes and starting/stopping deployments in terms of resource utilization, cluster startup speed, and consumed time for lightweight platforms and original Kubernetes. The results show that the original Kubernetes outperforms MicroKubernetes and K3S in many tests, but K3S demonstrates better disk utilization. On the other hand, MicroKubernetes demonstrates worst results in the performed tests.

Containerization technology utilizes operating system level virtualization to package applications to run with required libraries and are isolated from other processes on the same host. Lightweight and quick deployment make containers popular in many data centers. Running distributed applications in data centers usually involves multiple clusters of machines. Docker Swarm is a container orchestration tool for managing a cluster of Docker containers and their hosts. However, Docker Swarm’s scheduler does not consider resource utilization when placing containers in a cluster. This paper first investigated performance interference in container clusters. Our experimental study showed that distributed applications’ performance can be degraded when co-located with other containers which aggressively consume resources. A new scheduler is proposed to improve performance while keeping high resource utilization. The experimental results demonstrated that the proposed prototype with machine learning based clustering algorithms could effectively improve distributed applications’ performance by up to 14.5% with an average at around 12%. This work also provides theoretical bounds for the container placement problem.

The enhanced Mobile Broadband (eMBB) and ultra-Reliable Low Latency Communications (uRLLC) are the two main scenarios of $5^{th}$ generation (5G) mobile communication system networks. There is an obvious difference in service requirements between different scenarios. When multi-scenario services coexist in the 5G networks, exploring optimized resource scheduling and allocation strategies become a critical issue. The 5G New Radio (NR) and numerology technologies have been standardized, which lay the foundation for flexible frame structure and adaptive scheduling. In this paper, we propose the self-adaptive flexible transmission time interval (TTI) scheduling (SAFE-TS) strategy in the eMBB and uRLLC coexistence scenario. Machine learning (ML) is applied to achieve flexible TTI scheduling. Moreover, we design the random forest-based ensemble TTI decision algorithm (RF-ETDA) to accomplish the TTI selection for each service. Compared with the existing ML methods, the proposed algorithm has a performance improvement in selecting TTI, especially for the uRLLC services. Then, the TTI selection results will be the basis of system resource scheduling and allocation. The simulation results prove that the proposed SAFE-TS effectively reduce the delay and packet loss rate of the uRLLC services while guaranteeing the eMBB requirements. Therefore, it is highly recommended that flexible TTI scheduling should be applied in the construction of the 5G networks to achieve superior network performances.

The introduction of cloud‐native technology has significantly changed the architecture of applications and the mechanism for the collaborative operation of components in power distribution edge computing terminals (PDECT). To develop an effective quantitative analysis tool for PDECT performance, the composition and characteristics of cloud‐native PDECT are studied, and the modelling and simulation of cloud‐native PDECT are proposed. Subsequently, modelling is implemented through the simulation software CloudSim, achieving the simulation of microservices, containers, declarative configuration, and container orchestration with the consideration of power distribution scenarios. Then, by the proposed simulation scenario module, various elements of the power distribution scenarios can be self‐defined. Finally, by demonstrating the principles and implementation mechanisms of the proposed modelling method and simulation tool, and comparing simulation results for different service time ranges, access devices, resource configurations of PDECT, request occurrence rates, and resource scheduling strategies, the validity and effectiveness of the proposed modelling method and simulation tool are verified.

The explosive growth of the Internet of Things (IoT) has led to the emergence of the IoT-fog-cloud continuum, in an attempt to facilitate the real-time processing of IoT data. In such multi-tier environments, it is crucial to adopt an efficient resource allocation and scheduling scheme, in order to provide effective load balancing and timeliness for the real-time workload. A load balancing approach that has been proven to be efficient and effective in traditional distributed environments, is the power of two choices – or <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> choices, in its general form. Only recently has this technique been examined in multi-tier environments, without considering, however, important aspects of such frameworks. To this end, in this paper we propose and investigate three resource allocation and scheduling heuristics for real-time workflow jobs in an IoT-fog-cloud environment. The first strategy, performs exhaustive search at each scheduling step in order to find the most suitable resource in the fog and cloud layers for the workload assignment. On the other hand, the two other policies adopt the power of two choices approach. The simulation results shed light on interesting insights regarding the performance and applicability of each method.

Orthogonal Frequency-Division Multiple Access (OFDMA) systems have attracted considerable attention through technologies such as 3GPP Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX). OFDMA is a flexible multiple-access technique that can accommodate many users with widely varying applications, data rates, and Quality of Service (QoS) requirements. OFDMA has the advantages of handling lower data rates and bursty traffic at a reduced power compared to single-user OFDM or its Time Division Multiple Access (TDMA) or Carrier Sense Multiple Access (CSMA) counterparts. In our work, we propose a Particle Swarm Optimization based resource allocation and scheduling scheme (PSORAS) with improved quality of service for OFDMA Systems. Simulation results indicate a clear reduction in delay compared to the Frequency Division Multiple Access (FDMA) scheme for resource allocation, at almost the same throughput and fairness. This makes our scheme absolutely suitable for handling real time traffic such real time video-on demand.

Besides avoiding inter-symbol interference and leading to high capacity, wireless orthogonal frequency division multiplexing (OFDM) provide fine granularity for resource allocation since they are capable of dynamically assigning sub-carriers to multiple users and adaptively allocating transmit power. The current dominate layered networking architecture, in which each layer is designed and operated independently, results in inefficient and inflexible resource use in wireless networks due to the nature of the wireless medium, such as time-varying channel fading. Thus, we need an integrated adaptive design across different layers. In this paper, we focus on resource allocation and scheduling in wireless multiuser OFDM networks based on joint physical and medium access control (MAC) layer optimization. An adaptive cross-layer design for the downlink multiuser OFDM systems, to maximize the weighted sum capacity of all users, where each user has multiple heterogeneous traffic queues simultaneously is proposed. A packet dependent (PD) scheduling scheme is employed at the MAC layer, which determines the packet transmission order by assigning different weights to different packets, and is shown by simulations more efficient than the previous methods where all packets in a queue have the same weight. The weight design in PD scheduling considers the delay, size and quality of service (QoS) priority level of packets. Each user weight employed in resource allocation at the physical (PHY) layer is obtained by summing up the weights of selected packets for the user. We also deeply investigate the various resource scheduling schemes for comparisons.

&lt;p&gt;Cloud manufacturing is a grand manufacturing concept aimed at reducing the waste of manufacturing resources. By borrowing the ideas of cloud computing and utilizing information technology to achieve high sharing of manufacturing resources, the core issues that both the supply and demand sides are concerned about in the cloud manufacturing process are price, product quality, and product production cycle. Therefore, this paper constructs a manufacturing process resource scheduling model that includes three elements. In order to solve the optimal solution of the model, an improved intelligent algorithm is used to solve it. The intelligent algorithm is based on the advantages of the gray wolf algorithm in solving accuracy and convergence speed. The integration of the Bat algorithm in the gray wolf algorithm improves the search ability and performance of the original gray wolf algorithm. At the same time, the gray wolf algorithm can avoid the convergence speed of the Bat algorithm from changing with the increase of iteration times. The phenomenon of slow or almost stopping. To avoid generating local optimal solutions, the accuracy of the cloud resource scheduling model and algorithm solution constructed in this paper were verified through the construction of a simulation environment. &lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;