The role of microservice approach in edge computing: Opportunities, challenges, and research directions

Hossain, Delowar; Sultana, Tangina; Akhter, Sharmen; Hossain, Md. Imtiaz; Thu, Ngo Thien; Huynh, Luan N. T.; Lee, Ga-Won; Huh, Eui‐Nam

doi:10.1016/j.icte.2023.06.006

Cited by 10 publications

(3 citation statements)

References 72 publications

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“…In the study by Gopal [31] and Hossain et al [32], it is proposed that although microservices, as a lightweight architecture that can fully utilize the capabilities of edge nodes, can effectively address challenges such as poor scalability of monolithic programs, the complex invocation relationships that exist between microservices expose a larger attack surface. The placement, scheduling, and auto-scaling of microservices may have security threats, so it is crucial to develop lightweight cryptographic mechanisms or secure placement algorithms.…”

Section: Microservices Securitymentioning

confidence: 99%

Differentiated Security Requirements: An Exploration of Microservice Placement Algorithms in Internet of Vehicles

Zhang,

Liang,

et al. 2024

Electronics

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In recent years, microservices, as an emerging technology in software development, have been favored by developers due to their lightweight and low-coupling features, and have been rapidly applied to the Internet of Things (IoT) and Internet of Vehicles (IoV), etc. Microservices deployed in each unit of the IoV use wireless links to transmit data, which exposes a larger attack surface, and it is precisely because of these features that the secure and efficient placement of microservices in the environment poses a serious challenge. Improving the security of all nodes in an IoV can significantly increase the service provider’s operational costs and can create security resource redundancy issues. As the application of reinforcement learning matures, it is enabling faster convergence of algorithms by designing agents, and it performs well in large-scale data environments. Inspired by this, this paper firstly models the placement network and placement behavior abstractly and sets security constraints. The environment information is fully extracted, and an asynchronous reinforcement-learning-based algorithm is designed to improve the effect of microservice placement and reduce the security redundancy based on ensuring the security requirements of microservices. The experimental results show that the algorithm proposed in this paper has good results in terms of the fit of the security index with user requirements and request acceptance rate.

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Section: Microservices Securitymentioning

confidence: 99%

Differentiated Security Requirements: An Exploration of Microservice Placement Algorithms in Internet of Vehicles

Zhang,

Liang,

et al. 2024

Electronics

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“…According to [7], microservices offer benefits such as better scalability, faster deployment, flexibility, resiliency, smaller development teams, modularity, reliability, and reusability. However, there are drawbacks and present challenges in their use, as specified by Velepucha and Flores [8]This new architecture has a high learning curve, requires little experience in team development, has network overhead, is more complex, and duplicates data.…”

Section: Introductionmentioning

confidence: 99%

Pannotator integrated with Medpipe provides immunological and subcellular location features using a microservice

Gonçalves,

Santos

2024

Preprint

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Genome sequencing and assembly are trivial tasks nowadays. After assembling contigs and scaffolds from a genome, the subsequent step is annotation. An annotation evidencing the expected features, like rRNA, tRNA, and CDS, is a signal of the quality of our sequencing and assembly. Different techniques to obtain and reproduce DNA samples, as well as sequencing and assembly of genomes, can impact the quality of a genome's expected features. The Pannotator tool was conceived as an aid annotation tool focusing on the differences between assembling and its reference genome. Some of the key features for bacterial genome annotations are the subcellular location and immunological potential of a CDS. Instead of reimplementing the prediction of these features in Pannotator, we leveraged the capabilities of our microservice to provide them. In the end, Medpipe software was not modified, and Pannotator underwent minor changes to incorporate the subcellular location and immunological potential of all exported proteins annotated by the tool. Moreover, our Medpipe microservice can also be incorporated into other software. The Medpipe microservice is open to anyone, not only to our Pannotator tool. The successful integration of Medpipe to Pannotator, powered by the Medpipe microservice, offers a powerful approach to advanced genomic analysis. The Medpipe microservice, built on Kotlin with the Spring Boot framework, is instrumental in the automation of Medpipe processing. It achieves this using REST endpoints, such as the execution of Medpipe in an asynchronous manner, status retrieval, and prediction generation, which enhance the modularity and scalability of the microservice. The availability of endpoint documentation, detailed request examples, and logs make our microservice user-friendly. The results of this integration demonstrate the value of the information provided by Medpipe, enriching genomic annotation with additional details, such as the density of mature epitopes (MED) and protein subcellular location classification. The Pannotator has evolved beyond basic function annotation and now provides data on immunological potential, structure, and subcellular location after being integrated with our microservice. The Medpipe microservice is available at https://github.com/santosardr/medpipe-ms.git.

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“…It offers improved scalability, portability, and resource utilization. Containers enable lightweight, isolated environments for running applications, making it easier to manage and scale applications across heterogeneous edge computing infrastructures [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Modular Framework for Data Processing at the Edge: Design and Implementation

Urblik,

Kajati,

Papcun

et al. 2023

Sensors

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There is a rapid increase in the number of edge devices in IoT solutions, generating vast amounts of data that need to be processed and analyzed efficiently. Traditional cloud-based architectures can face latency, bandwidth, and privacy challenges when dealing with this data flood. There is currently no unified approach to the creation of edge computing solutions. This work addresses this problem by exploring containerization for data processing solutions at the network’s edge. The current approach involves creating a specialized application compatible with the device used. Another approach involves using containerization for deployment and monitoring. The heterogeneity of edge environments would greatly benefit from a universal modular platform. Our proposed edge computing-based framework implements a streaming extract, transform, and load pipeline for data processing and analysis using ZeroMQ as the communication backbone and containerization for scalable deployment. Results demonstrate the effectiveness of the proposed framework, making it suitable for time-sensitive IoT applications.

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The role of microservice approach in edge computing: Opportunities, challenges, and research directions

Cited by 10 publications

References 72 publications

Differentiated Security Requirements: An Exploration of Microservice Placement Algorithms in Internet of Vehicles

Differentiated Security Requirements: An Exploration of Microservice Placement Algorithms in Internet of Vehicles

Pannotator integrated with Medpipe provides immunological and subcellular location features using a microservice

A Modular Framework for Data Processing at the Edge: Design and Implementation

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