EdgeX over Kubernetes: Enabling Container Orchestration in EdgeX

Containers virtually package a piece of software and share the host Operating System (OS) upon deployment. This makes them notably light weight and suitable for dynamic service deployment at the network edge and Internet of Things (IoT) devices for reduced latency and energy consumption. Data collection, computation, and now intelligence is included in variety of IoT devices which have very tight latency and energy consumption conditions. Recent studies satisfy latency condition through containerized services deployment on IoT devices and gateways. They fail to account for the limited energy and computing resources of these devices which limit the scalability and concurrent services deployment. This paper aims to establish guidelines and identify critical factors for containerized services deployment on resource constrained IoT devices. For this purpose, two container orchestration tools (i.e., Docker Swarm and Kubernetes) are tested and compared on a baseline IoT gateways testbed. Experiments use Deep Learning driven data analytics and Intrusion Detection System services, and evaluate the time it takes to prepare and deploy a container (creation time), Central Processing Unit (CPU) utilization for concurrent containers deployment, memory usage under different traffic loads, and energy consumption. The results indicate that container creation time and memory usage are decisive factors for containerized micro service architecture.

Internet of Things (IoT) is the developing technology that enables devices to communicate without human interaction. IoT utilizes cloud computing services to collect and process data for IoT devices and to manage the device remotely. Cloud computing is not efficient enough to handle the fast stream of data produced by the IoT, therefore scaling up IoT applications to meet demands of high peak becomes easier and highly automated in fog computing. Containers are mostly used as virtualization solutions for IoT in fog computing. It enables the execution of small microservices to large applications. However, the rise of many lightweight containers has resulted in new application architectures and fundamentally changing how applications are deployed and visualized. Due to this change, container orchestration tools were proposed. These tools allow users to coordinate and manage containers. However, container orchestration tools need to meet the requirements of IoT applications and constraints imposed on the nodes in fog. This paper presents a systematic literature review on the selection of orchestration tools for the efficient deployment of IoT applications in fog computing. Moreover, the performance of IoT applications must be considered by applying different metrics. This paper aims to propose potential research directions to address identified gaps in the selection of orchestration tools.

The upcoming 5th Generation (5G) mobile networks will be different from the previous mobile network generations in the fact that it will enable the mobile networks industry, besides offering superior broadband services, to enhance Internet of Things (IoT) industries such as vehicular communication system, factory automation, smart healthcare system and many more. Many of these use cases have challenging and quite often contradicting requirements in terms of data rate, latency, throughput and so on. This suggests that 5G mobile networks need to adopt flexible models that can adapt to different IoT device and traffic requirements. Consequently, a fresh look into how mobile networks are currently designed and deployed is needed. Historically, mobile networks have relied on the axiomatic role of cells as the cornerstone of the Radio Access Networks (RAN). Mobile network systems have witnessed several recent trends such as the increased heterogeneity in heterogeneous types of IoT services infrastructure and spectrum as well as the rise of different traffic types with different Quality of Services (QoS) requirements. In this direction, this thesis focuses on improving the performance of cell-edge users or IoT devices in 5G mobile networks by initially implementing the network slicing management approach, particularly as, with the fast growth of IoT, billions of devices will join the internet in the next few years. Hence, the latest 5G mobile technologies expected to offer massive connectivity and management ability of high volume of data traffic at the presence of immense interferences from a mobile network of IoT devices. Further, it will face challenges due to congestion and overload of data traffic due to a humongous number of IoT devices. Besides, these devices likely to demand high throughput, low latency and high level of reliability especially for critical real-time smart systems in density and small zone, such as in Vehicular Communication System (VCS), these vehicles mainly rely on connectivity aspects. Furthermore, IoT devices transmit small and large-sized packets with different radio resource requirements. For example, Smart Healthcare System (SHS) devices transmit small-sized of a data with utilizing a small portion of Physical Resource Block (PRB) as the smallest radio resource unit, which is allocated to a single device for data transmission in 5G mobile networks. In the IoT services with transmitting a small-sized data, the capacity of the PRB is not fully utilized, which causes wastage and unfairness of using PRB among these IoT devices or services. The novelties made in this thesis significantly advance a Slice Allocation Management (SAM) model based on critical services such as (VCS) to satisfy low latency demand. The proposed model aims at providing dedicated slices based on service requirements such as expected low latency for (VCS). To ensure such performance to data traffic of IoT devices in Uplink (UL)of Relay Node (RN) cells in the 5G mobile networks by slicing the RAN, along with assigning the nearest Mobile Edge Computing (MEC) with isolating slices depend on technical and QoS requirements for each IoT nodes. Also, this thesis proposes a Data Traffic Aggregation (DTA) model for efficient utilization of the smallest untie of PRB by aggregating the data traffics of several IoT devices, which can support IoT node throughput such as SHS. Also, this thesis presents a comprehensive comparison of the packet scheduling mechanisms include Priority Queuing (PQ), First-In-First-Out (FIFO) and Weighted Fair Queuing (WFQ) applied based on data traffic slicing model through RN cells. These thesis models are validated through the OPNET simulator to measure the performance of the SAM and DTA Models along with the assessment of packet scheduling mechanism. The simulation considers IoT devices in various smart systems such as VCS, SHS and smartphones also, different protocols include Simple Mail Transfer Protocol (SMTP), File Transfer Protocol (FTP), and Voice over Internet Protocol (VoIP) and Real-time Transport Protocol (RTP). Simulation results show a significant improvement in IoT nodes packets transmission via RNs and Donor eNodeB (DeNB) cells, in My SAM Model scenario comparing with other scenarios. The model has improved such as End-to-End (E2E) delay in FTP node by reaching 1ms, loading in VoIP node by 80% and throughput of all nodes in the uplink side of networks by 66%. In addition, the results display significant impact of IoT data traffic with different priority, networks E2E performance is improved by aggregating data traffic of several IoT devices with DTA model, which is determined by simulating several scenarios, considerable performance improvement is achieved in terms of averages cell throughput, upload response time, packet E2E delay and radio resource utilization. Finally, the result found PQ packet scheduling mechanism as the appropriate scheduling mechanism in case of supporting several of priorities queuing for data traffic.

Proactive auto-scaling technique for web applications in container-based edge computing using federated learning model

Cloud Computing is an emerging technology that is used not only by developers but also by end-users. It has vital importance in the Information Technology (IT) industries as its future would create a great transition from conventional IT services. These days, containerization in cloud computing has become an important research area. The selection of container orchestration tools is one of the difficult tasks for the organizations involved in the management of the vast number of containers. These tools have their strengths, weaknesses, and functionalities which need to be considered. This paper presents a comparative analysis of the container orchestration tools. This analysis would help the professionals to decide whether they need an orchestrator bound to a single technology or an orchestrator which provides the independent solution. In this paper, four popular orchestration tools viz., Kubernetes, Docker Swarm, Mesos, and Redhat OpenShift are analyzed on various parameters viz., security, deployment, stability, scalability, cluster installation, and learning curve. We observed that Kubernetes has the best scheduling features whereas Docker Swarm is easy to use. We also found that Mesos has good scalability whereas OpenShift is a highly secure orchestration tool.

The Cloud Computing paradigm promoted the outsourcing of IT infrastructure and enterprise applications paving the way to save costs of building and maintaining computing infrastructures on-premise. In this environment, scale up of applications to attend demands in high peaks become easier and highly automated. Virtualization was a key technology to enable these characteristics. Nowadays, Container technology became popular as an alternative to Virtual Machines, and is being widely applied, as a consequence, Orchestration tools are being extensively applied in the Cloud environment. Despite its success, when it comes to the Internet of Things (IoT), Cloud Computing falls short to meet several requirements. Fog Computing appear as a complimentary technology to the Cloud to deliver the missing requirements in the IoT scene. Managing services deployed in a Fog Environment is a complex task and infrastructure management and orchestration tools can make it seamless. In this paper, we evaluate how Containers can affect the overall performance of applications in Fog Nodes. We analyze different Container Orchestration tools and how they meet Fog requirements to run applications. We also propose a Container Orchestration Framework for Fog Computing infrastructures.

The rapid growth of internet and communication technology, and rapid adaption internet of things (IoT) devices which generate different applications, put pressure on Cloud computing Infrastructure which leads to high latency and high energy consumption for latency-sensitive applications. Designing an architecture based on IoT applications has a lot of inevitable issues that must be taken consideration for enhancing the performance of the system the issues are security, massive traffic, high availability, high reliability, and energy constraints. The latest distributed computing paradigms, such as Fog computing, and Edge computing, software-defined networking (SDN), network virtualization (NV), and block-chain has been implemented in IoT networks to swamped the aforementioned challenges while fulfilling the expected quality of service (QoS). In this chapter, we proposed the architecture of fog computing along with the IoT devices, how IoT devices access the virtual resources in the virtual environment. IoT devices are very much capable of capturing data using the sensors, collection of the data over the network and perform some analysis for better decision making for improvement of the productivity of current processes as well as to cater to new types of services to the multiple geographical locations. Fog computing architecture consists of IoT layer moves the data generated by the IoT devices to the upper 218layer that consists of fog nodes (FNs) which are controlled by software-enabled devices which we call as Software-defined Network(SDN) to achieve better reliability and availability for latency-sensitive application. The SDN based network architecture is equipped with controllers and resource-constrained devices. The existing cloud computing technology has certain drawbacks which could enforce to develop new distributed computing which we referred here as fog computing which is based on collaboration with cloud computing and has become the next generation of the cloud computing which is placed between cloud and IoTs to meet the requirement of IoTs based application. Fog computing helps to reduce transmission latency because it is available locally to the IoT layer, i.e., at less distance, it can be accessed and substantially decrease monetary cost for accessing resources. In this chapter, we study the various issues related to network services delivered to the application request by users. A Software-Defined Networking (SDN) is an emerging architecture that enables dynamic manageable, cost-effective adaptable to provide high bandwidth to the dynamic nature of the application. It also decouples the network control and forwarding the functions. So by using this, management of the network became easy. IoT and SDN are complementing each other to enhance the system architecture for big data management, etc. In this chapter, we are trying to propose a service architecture model in fog computing, which is SDN enabled.

Compared to the traditional approach of using virtual machines as the basis for the development and deployment of applications running in Cloud-based infrastructures, container technology provides developers with a higher degree of portability and availability, allowing developers to build and deploy their applications in a much more efficient and flexible manner. A number of tools have been proposed to orchestrate complex applications comprising multiple containers requiring continuous monitoring and management actions to meet application-oriented and non-functional requirements. Different container orchestration tools provide different features that incur different overheads. As such, it is not always easy for developers to choose the orchestration tool that will best suit their needs. In this paper we compare the benefits and overheads incurred by the most popular open source container orchestration tools currently available, namely: Kubernetes and Docker in Swarm mode. We undertake a number of benchmarking exercises from well-known benchmarking tools to evaluate the performance overheads of container orchestration tools and identify their pros and cons more generally. The results show that the overall performance of Kubernetes is slightly worse than that of Docker in Swarm mode. However, Docker in Swarm mode is not as flexible or powerful as Kubernetes in more complex situations.

Edge computing is a viable approach to improve service delivery and performance parameters by extending the cloud with resources placed closer to a given service environment. Numerous research papers in the literature have already identified the key benefits of this architectural approach. However, most results are based on simulations performed in closed network environments. This paper aims to analyze the existing implementations of processing environments containing edge resources, taking into account the targeted quality of service (QoS) parameters and the utilized orchestration platforms. Based on this analysis, the most popular edge orchestration platforms are evaluated in terms of their workflow that allows the inclusion of remote devices in the processing environment and their ability to adapt the logic of the scheduling algorithms to improve the targeted QoS attributes. The experimental results compare the performance of the platforms and show the current state of their readiness for edge computing in real network and execution environments. These findings suggest that Kubernetes and its distributions have the potential to provide effective scheduling across the resources on the network's edge. However, some challenges still have to be addressed to completely adapt these tools for such a dynamic and distributed execution environment as edge computing implies.

Internet of Thing (IoT) devices are rapidly becoming an indispensable part of our life with their increasing deployment in many promising areas, including tele-health, smart city, intelligent agriculture. Understanding the mobility of IoT devices is essential to improve quality of service in IoT applications, such as route planning in logistic management, infrastructure deployment, cellular network update and congestion detection in intelligent traffic. Despite its importance, there are not many results pertaining to the mobility of IoT devices. In this article, we aim to answer three research questions: (i) what are the mobility patterns of IoT device? (ii) what are the differences between IoT device and smartphone mobility patterns? (iii) how the IoT device mobility patterns differ among device types and usage scenarios? We present a comprehensive characterization of IoT device mobility patterns from the perspective of cellular data networks, using a 36-days long signal trace, including 1.5 million IoT devices and 0.425 million smartphones, collected from a nation-wide cellular network in China. We first investigate the basic patterns of IoT devices from two perspectives: temporal and spatial characteristics. Our study finds that IoT device mobility exhibits significantly different patterns compared with smartphones in multiple aspects. For instance, IoT devices move more frequently and have larger radius of gyration. Then we explore the essential mobility of IoT devices by utilizing two models that reveal the nature of human mobility, i.e., exploration and preferential return (EPR) model and entropy based predictability model. We find that IoT devices, with few exceptions, behave totally different from human, and we further derive a new formulation to describe their movement. We also find the gap mobility predictability and predictability limit between IoT and human is not as big as people expected.

Connecting food supply chains

Due to rising cyber threats, IoT devices’ security vulnerabilities are expanding. However, these devices cannot run complicated security algorithms locally due to hardware restrictions. Data must be transferred to cloud nodes for processing, giving attackers an entry point. This research investigates distributed computing on the edge, using AI-enabled IoT devices and container orchestration tools to process data in real time at the network edge. The purpose is to identify and mitigate DDoS assaults while minimizing CPU usage to improve security. It compares typical IoT devices with and without AI-enabled chips, container orchestration, and assesses their performance in running machine learning models with different cluster settings. The proposed architecture aims to empower IoT devices to process data locally, minimizing the reliance on cloud transmission and bolstering security in IoT environments. The results correlate with the update in the architecture. With the addition of AI-enabled IoT device and container orchestration, there is a difference of 60% between the new architecture and traditional architecture where only Raspberry Pi were being used.

In concert with advances of wireless technologies in facilitating internet connectivity of Internet of Things (IoT) devices, mobile edge computing can provision and distribute computing resources at the cloudlets to efficiently process a high volume of IoT data. Among the IoT applications, multi-party data sharing among IoT devices, wireless access nodes and cloudlets is becoming increasingly critical, not only because the data collected by each single IoT device will often stay unmined, but also because of the security concern. As IoT applications' dependence on the cloud environment grows, the rich resources at cloudlets often become the attack targets, and the IoT data that are stored or processed using the cloud resources will be jeopardized. For the internet of important things, we have investigated how to efficiently and securely share the data among multi-party. In particular, for a group of cooperative IoT devices, by leveraging the cloud resources available at the wireless access points, a secure cache site with fast data uploading rate is chosen for each user. To minimize the overall data downloading time, the multi-party multi-path data delivery scheme is also designed such that each user can efficiently retrieve the data belonging to other parties.

Edge computing brings down storage, computation, and communication services from the cloud server to the network edge, resulting in low latency and high availability. The Internet of things (IoT) devices are resource-constrained, unable to process compute-intensive tasks. The convergence of edge computing and IoT with computation offloading offers a feasible solution in terms of performance. Besides these, computation offload saves energy, reduces computation time, and extends the battery life of resource constrain IoT devices. However, edge computing faces the scalability problem, when IoT devices in large numbers approach edge for computation offloading requests. This research article presents a three-tier energy-efficient framework to address the scalability issue in edge computing. We introduced an energy-efficient recursive clustering technique at the IoT layer that prioritizes the tasks based on weight. Each selected task with the highest weight value offloads to the edge server for execution. A lightweight client–server architecture affirms to reduce the computation offloading overhead. The proposed energy-efficient framework for IoT algorithm makes efficient computation offload decisions while considering energy and latency constraints. The energy-efficient framework minimizes the energy consumption of IoT devices, decreases computation time and computation overhead, and scales the edge server. Numerical results show that the proposed framework satisfies the quality of service requirements of both delay-sensitive and delay-tolerant applications by minimizing energy and increasing the lifetime of devices.

The spread of Internet of Things (IoT) devices has led to an exponential increase in data generation, transmission, and processing. However, the traditional cloud-centric approach to IoT data processing has several limitations, including high potential, bandwidth limitations, and security worries. Edge computing has emerged as a capable solution to address these challenges by processing data closer to the source, i.e., at the edge of the network. This paper presents an all-inclusive review of edge computing architecture within the application area of IoT devices. We discussed the key components, architectures, and technologies that enable edge computing, including fog computing, mist computing, and cloudlet. We also explore the benefits and challenges of edge computing in IoT applications, such as real-time processing, reduced latency, improved security, and increased scalability. Furthermore, we identify the research gaps and future directions in edge computing for IoT devices, including the need for homogeneous architectures, efficient resource management, and robust security devices.