Edge Computing and Microservices: Extending Scalability Beyond the Cloud

Edge computing system is facing real challenges with the evolution of new information and communication technologies (ICT). In addition, with the advent of Internet of Things (IoT) and real-time services, current edge computing system is becoming an architecture that is inappropriate for some services that require ultra low-latency and very high throughput. In order to meet all the requirements of these services, a new edge computing architecture is required. For this purpose, Home Edge Computing (HEC) architecture has been proposed. However, edge computing system has encountered some issues such as resource limitation on home servers. Furthermore, most distributed systems (e.g. HEC) operate in online mode. In other words, once failures occur in the Internet (remote servers), applications/services will be not longer accessible. In this paper, we propose HEC-NerveNet architecture, a technique based on the HEC architecture and NerveNet technology (HEC-N), to allow users to have continuity of services (resilient), even if they do not access to the remote servers (cloud computing). As a reminder, NerveNet is a resilient distributed architecture that we proposed in 2011 after the natural disaster in Japan. It allows maintaining connectivity and services in case of network failure. In addition, NerveNet solution allows the automatic clustering, and fast recovery thanks to the mesh topology on NerveNet network. This can overcome the need to manually create clusters in the HEC architecture. In the simulation, we prove that our proposal (HEC- N) is very suitable for resilient architecture and the need for future generation networks (beyond 5G/6G networks) with improving the metrics such as ultra-low latency and very high throughput compared with the current HEC and NerveNet systems.

The large-scale deployment of photovoltaic installations at geographically remote sites underscores the need for effective and reliable monitoring systems. The monitoring tools should be capable of managing numerous devices that generate large amounts of data. One of the major challenges is to ensure interoperability with equipment and guarantee the reliability of photovoltaic system operations. The present work aims to propose monitoring architecture that can handle remote photovoltaic plants and integrate operation and maintenance strategies. The architecture embeds a distributed data flow, an anomaly detection pipeline in addition to performance indicator calculation in accordance with IEC 61724-1, and a real-time notification system. The architecture is based on a microservice architecture that integrates edge computing for local data acquisition and preprocessing. System validation was carried out to assess latency, data completeness, and data consistency. The results show stable latency with an average of 3.4 s with data completeness of 100% and 99.57% consistency. These findings demonstrate the ability of architecture to monitor remote plants, highlighting its suitability for large-scale deployment and the integration of advanced analysis. • A scalable edge computing and microservice architecture for PV monitoring. • Ensure multi-vendor interoperability via protocol adapters and data standardization. • Provides KPIs compliant with IEC 61724-1, anomaly detection pipeline and predictive maintenance insights. • Real-time data visualization for multi-site PV systems and notifications.

Improving consumer satisfaction in smart cities using edge computing and caching: A case study of date fruits classification

In the current enterprise management system, the coupling relationship between enterprise management and operation and maintenance database is not considered, which leads to many system vulnerabilities, slow running speed, and long response time. An enterprise management system based on edge computing architecture is designed. According to the characteristics of mobile edge computing, we design the architecture of the edge computing platform, integrate the MEC virtualization infrastructure layer, MEC platform layer, and MEC application layer, construct the edge computing model, reflect the coupling relationship between enterprise management and operation and maintenance database, calculate the delay vector of the enterprise management server, and complete the parameter calculation of enterprise management and operation and maintenance system. This paper analyzes the feasibility, objectives, and users and functional requirements of the enterprise management system, designs the functional structure of the system, realizes the design of the public module, function menu module, and login module, adopts the SQL database management system, and designs the database and data table, so as to realize the design of the enterprise management system based on edge computing architecture. Experimental results show that the proposed method has fewer system vulnerabilities and can effectively speed up the system operation speed and shorten the system response time.

Recent years have witnessed the widespread popularity of Internet of things (IoT). By providing sufficient data for model training and inference, IoT has promoted the development of artificial intelligence (AI) to a great extent. Under this background and trend, the traditional cloud computing model may nevertheless encounter many problems in independently tackling the massive data generated by IoT and meeting corresponding practical needs. In response, a new computing model called edge computing (EC) has drawn extensive attention from both industry and academia. With the continuous deepening of the research on EC, however, scholars have found that traditional (non-AI) methods have their limitations in enhancing the performance of EC. Seeing the successful application of AI in various fields, EC researchers start to set their sights on AI, especially from a perspective of machine learning, a branch of AI that has gained increased popularity in the past decades. In this article, we first explain the formal definition of EC and the reasons why EC has become a favorable computing model. Then, we discuss the problems of interest in EC. We summarize the traditional solutions and hightlight their limitations. By explaining the research results of using AI to optimize EC and applying AI to other fields under the EC architecture, this article can serve as a guide to explore new research ideas in these two aspects while enjoying the mutually beneficial relationship between AI and EC.

Recent years have witnessed the proliferation of mobile computing and the Internet of Things (IoT), in which billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. However, it is challenging and infeasible to transfer and process zillions bytes of data using the current cloud-device architecture, due to bandwidth constraints of networks, potentially uncontrollable latency of cloud services, and privacy concerns while collecting data from IoT devices. To tackle these challenges, edge computing, an emerging computing paradigm, has received a tremendous amount of interest. By pushing data storage, computing, and controls closer to the network edge, edge computing has been widely recognized as a promising solution to meet the requirements of low latency, high scalability, and energy efficiency, as well as to mitigate the network traffic burdens. However, with the emergence of diverse IoT applications (e.g., smart city, industrial automation, and connected car), it becomes challenging for edge computing to deal with these heterogeneous IoT environments.

This study discussion point of this paper is to make an in-depth analysis of the development impact of the Internet of Things combined with edge computing and artificial intelligence. In the analysis process, the importance and criticality of data processing and decision making of edge computing as well as the challenges faced should be elaborated respectively. With the rapid popularization and development of Internet of Things devices, edge computing has brought more innovative solutions for different application scenarios such as intelligent furniture industrialization, automatic driving and intelligent transportation by reducing the delay of processing data and improving the characteristics of security data, film and television. Resource and energy efficiency have certain limitations, so it is necessary to combine artificial intelligence to enhance edge computing devices, hardware accelerators, and its utility and federated learning technologies, which can effectively improve the performance and scalability of edge computing and promote the development of more self-service network systems for smart devices. The core of this study is how to promote the Internet through AI-driven edge computing to further develop and provide insights for research priorities and suggest related future research directions.

The Internet of Things (IoT) based Smart city applications are the latest technology-driven solutions designed to collect and analyze data to enhance the quality of life for urban residents by creating more sustainable, efficient, and connected communities. Communication nodes are networked independently to monitor the circumstances, where they require being more energy-efficient and securing, to improve the performance of sustainable cities. Because, the enormous number of networked devices makes the smart cities application vulnerable to many security breaches, all of which have serious ramifications for a city and its residents’ safety, well-being, and economic development. Low-powered sensors have limitations in terms of battery life, short transmission range, and security considerations, despite the fact that the combination of edge computing and Green IoT considerably enhances network performance in terms of processing and data storage. Consequently, it is necessary to implement an advanced approach to provide energy resources with secure data transmission for sustainable cities. Therefore, this research proposes an Intelligent Buffalo-based Secure Edge-enabled Computing (IB-SEC) framework for smart cities by IoT platform, which aims to enhance communication efficiency, and reliability, with minimizing latency in terms of energy consumption and data security for data transmission. The developed IB-SEC platform utilizes a combination of the African Buffalo Optimization (ABO) algorithm and a Distributed Hash function-based security algorithm to enhance the efficiency, security, and reliability of data transmission in IoT-based smart city networks. This platform leverages the capabilities of secure edge computing and MAC protocols to achieve its goals, by implementing encryption, authentication, and access control mechanisms that ensure the wireless communication is secure, and the data is protected against unauthorized access. Overall, the IB-SEC framework provides an advanced approach to secure IoT networks and enable smart city applications. Moreover, the developed platform enables the integration of heterogeneous edge devices, sensors, and systems which is effectively managed and adapted by Median Access Control (MAC) protocols. The IB-SEC is implemented in MATLAB and validated through cutting-edge security algorithms in terms of improved security, reliability, throughput, and reduced latency and energy consumption.

The growing interest in the Internet of Things (IoT) has prompted a more innovative architectural design approach and evolutionary frameworks to integrate heterogeneous IoT devices better. Edge computing, which provides services close to end-user devices without requiring high-speed Internet connectivity, has become a popular paradigm for reducing network congestion and allowing real-time Internet of Things applications. However, the heterogeneous and resource-constrained edge nodes make it difficult to design edge computing frameworks. However, because the edge computing environment is dynamic, with irregular connections and limited bandwidth, it's critical to deal with the challenge of determining the relative performance of applications deployed in the edge computing paradigm when input traffic, networking protocols, or system software changes. So, this paper explores three software architectures, namely monolithic architecture, microservices architecture (MSA), and service-oriented architecture (SOA), providing a framework for edge computing's many requirements. A comparative analysis of these three software architectures is also covered based on the fundamental characteristics of an edge computing framework.

In the previous decade, Internet of Things (IoT) has paved ways for connecting devices, people, infrastructure, and practically any entity that can be connected. Cloud infrastructure has been the irrefutable part of IoT solutions with substantiated claims of resource provisioning, scalable platforms, and minimal infrastructure costs. Cloud computing does not argue well with applications that require reduced latency, positioning capabilities, and support for mobility. In such instances, edge computing can be used to compliment the cloud infrastructure and help in mitigating the various technical bottlenecks that are part of cloud services. In IoT environment, edge computing can help in improving energy efficiency, optimal resource utilization, and contextual data processing and in reducing the network traffic of backbone networks. The characteristics of edge computing may lead to new challenges for ensuring security and privacy. This chapter proposes a conceptual framework which consists of the essential components to ensure security and privacy for edge computing applications.

Edge computing is a technique of optimizing cloud computing systems by performing data processing at the edge of the network, near the source of the data in the systems. And, cloud computing is a service that delivers on-demand self-service, broad network access, resource pooling, rapid elasticity or expansion, which is trending in today's technology-driven world. With the advantage of flexibility, storage, sharing, and easy accessibility, cloud is being used by major players in IT (information technology). This chapter highlights cloud/edge computing architecture and its current issues and challenges from technological and organizational aspects. A brief introduction of edge computing architecture with similar technologies along with its service models is discussed. A few counterexamples of cloud computing architecture are showed. Organizational aspects of cloud computing architecture, as well as IBM and Oracle reference cloud architecture, are briefly presented. Some emerging issues and challenges associated with cloud/edge computing on its utilization are also elaborated.

Recent advancements in technology now allow for the generation of massive quantities of data. There is a growing need to transmit this data faster and more securely such that it cannot be accessed by malicious individuals. Edge computing has emerged in previous research as a method capable of improving data transmission times and security before the data ends up in the cloud. Edge computing has an impressive transmission speed based on fifth generation (5G) communication which transmits data with low latency and high bandwidth. While edge computing is sufficient to extract important features from the raw data to prevent large amounts of data requiring excessive bandwidth to be transmitted, cloud computing is used for the computational processes required for developing algorithms and modeling the data. Edge computing also improves the quality of the user experience by saving time and integrating quality of life (QoL) features. QoL features are important for the healthcare sector by helping to provide real-time feedback of data produced by healthcare devices back to patients for a faster recovery. Edge computing has better energy efficiency, can reduce the electricity cost, and in turn help people reduce their living expenses. This paper will take a detailed look into edge computing applications around Internet of Things (IoT) devices, smart city infrastructure, and benefits to healthcare.

A centralized infrastructure system carries out existing data analytics and decision-making processes from our current highly virtualized platform of wireless networks and the Internet of Things (IoT) applications. There is a high possibility that these existing methods will encounter more challenges and issues in relation to network dynamics, resulting in a high overhead in the network response time, leading to latency and traffic. In order to avoid these problems in the network and achieve an optimum level of resource utilization, a new paradigm called edge computing (EC) is proposed to pave the way for the evolution of new age applications and services. With the integration of EC, the processing capabilities are pushed to the edge of network devices such as smart phones, sensor nodes, wearables, and on-board units, where data analytics and knowledge generation are performed which removes the necessity for a centralized system. Many IoT applications, such as smart cities, the smart grid, smart traffic lights, and smart vehicles, are rapidly upgrading their applications with EC, significantly improving response time as well as conserving network resources. Irrespective of the fact that EC shifts the workload from a centralized cloud to the edge, the analogy between EC and the cloud pertaining to factors such as resource management and computation optimization are still open to research studies. Hence, this paper aims to validate the efficiency and resourcefulness of EC. We extensively survey the edge systems and present a comparative study of cloud computing systems. After analyzing the different network properties in the system, the results show that EC systems perform better than cloud computing systems. Finally, the research challenges in implementing an EC system and future research directions are discussed.

Emerging Internet of Things (IoT) applications share the same characteristics of involving multiple processing components ( i.e. , function modules) and requiring a massive amount of data to be processed with low latency. To meet these needs, edge/fog computing has been proposed for next-generation mobile networks to migrate the computing from the cloud to the edge of the network. Thanks to the development of Network Functions Virtualization (NFV), with which edge computing platform can virtualize function modules and deploy them on any edge devices to provide flexible services on the edge networks. However, such platform would need to deal with complicated function module calling relationship ( i.e. , call graph) of applications and user mobility, and both are not thoroughly considered by existing works of NFV and edge computing. In this paper, based on our previous idea of virtual local-hub (VLH), we propose a complete design of edge computing framework, which applies NFV technology on edge computing environment for IoT applications. To handle the complicated call graphs of IoT applications with better resource utilization, the VLH framework adapts the technologies of container-based virtualization and microservice architecture, which enables remote function module sharing on the edge computing environment. The framework includes the heuristic algorithm for function module allocation with the objective of minimizing total bandwidth consumption. We also present a design of protocols for system operations and mobility handling in the framework. Then we implement the framework on commodity hardware as a testbed. Via simulations under a large-scale environment with practical settings and experiments on the testbed under real-world scenarios, we demonstrate and verify the effectiveness and practicability of the proposed VLH framework for IoT application service provision.

In this paper, we try to integrate edge computing with the cloud computing paradigm. There are several issues and challenges in cloud computing paradigm which can be resolved by edge computing. So, in this paper we have discussed about those issues and given an idea that how edge computing is able to resolve that issues. In this paper, first we have discussed about edge computing with its definition and data flow diagram followed by its applications in various fields like smart city, smart home, cloud offloading and image and video analysis, then at last we have discussed about the issues and challenges of cloud computing followed by the idea of how edge computing can solve that issues.