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Information
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Microservices and Cloud-Native Solutions: From Design to Operation
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Microservices and Cloud-Native Solutions: From Design to Operation

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (1 May 2021) | Viewed by 9306

Special Issue Editor

Dr. Jacopo Soldani

E-Mail Website
Guest Editor
Department of Computer Science, University of Pisa, Pisa, Italy
Interests: microservices; service-oriented computing; service-oriented architectures; cloud-native applications; cloud computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Microservices and cloud-native solutions are gaining more and more momentum in enterprise IT, with major IT players (such as Amazon, Netflix, Spotify, and Twitter) already adopting them to deliver their core businesses. Cloud-native solutions enable organizations to design, develop, and run scalable multiservice applications in modern and dynamic environments like the Cloud. Microservice applications are probably the most prominent example of cloud-native solutions, which in general enable obtaining loosely coupled and highly scalable multicomponent systems. Combined with containers, deployment automation technologies, and CI/CD, cloud-native solutions allow application providers to continuously deliver their software, with software updates released frequently with minimal toil. 

At the same time, microservices and cloud-native solutions are inherently complex, due to the high number of interacting software components forming microservices/cloud-native applications. This makes their design, development, enactment, and management costly, complex, and error-prone. Solutions and best practices for accomplishing such tasks are hence needed and set the scope for this Special Issue. Theoretical and practical research papers, as well as review papers, are all welcome. 

Topics of interest include but are not limited to:

  • Availability, fault-resilience, and scalability in microservices and cloud-native solutions;
  • Best practices and empirical studies on microservices and cloud-native solutions, and on their adoption;
  • CI/CD and deployment automation in microservices and cloud-native solutions;
  • Design principles, architectural smells, and architectural refactoring of microservices and cloud-native solutions;
  • Fault localization in microservices and cloud-native solutions;
  • Formal methods, models, and techniques for microservices and cloud-native solutions;
  • Infrastructure and integration components for microservices and cloud-native solutions;
  • Integration of microservices and cloud-native solutions;
  • Model-driven design and development of microservices and cloud-native solutions;
  • Programming languages for microservices and cloud-native solutions;
  • Reverse engineering for microservices and cloud-native solutions;
  • Security of microservices and cloud-native solutions;
  • Serverless applications and function-as-a-service solutions;
  • Microservices and cloud-native solutions adaptation, exploitation, and deployment over the Cloud-to-IoT computing continuum (including fog and edge infrastructures);
  • Testing of microservices and cloud-native solutions;
  • Verification of microservices and cloud-native solutions. 
Dr. Jacopo Soldani
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Microservices
  • Cloud-native applications
  • Cloud-native solutions

Published Papers (2 papers)

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Research

10 pages, 509 KiB  
Article
Research and Implementation of Scheduling Strategy in Kubernetes for Computer Science Laboratory in Universities
by Zhe Wang, Hao Liu, Laipeng Han, Lan Huang and Kangping Wang
Information 2021, 12(1), 16; https://0-doi-org.brum.beds.ac.uk/10.3390/info12010016 - 03 Jan 2021
Cited by 9 | Viewed by 3853
Abstract
How to design efficient scheduling strategy for different environments is a hot topic in cloud computing. In the private cloud of computer science labs in universities, there are several kinds of tasks with different resource requirements, constraints, and lifecycles such as IT infrastructure [...] Read more.
How to design efficient scheduling strategy for different environments is a hot topic in cloud computing. In the private cloud of computer science labs in universities, there are several kinds of tasks with different resource requirements, constraints, and lifecycles such as IT infrastructure tasks, course design tasks submitted by undergraduate students, deep learning tasks and and so forth. Taking the actual needs of our laboratory as an instance, these tasks are analyzed, and scheduled respectively by different scheduling strategies. The Batch Scheduler is designed to process tasks in rush time to improve system throughput. Dynamic scheduling algorithm is proposed to tackle long-term lifecycle tasks such as deep learning tasks which are hungry for GPU resources and have dynamically changing priorities. Experiments show that the scheduling strategies proposed in this paper improve resource utilization and efficiency. Full article
(This article belongs to the Special Issue Microservices and Cloud-Native Solutions: From Design to Operation)
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19 pages, 944 KiB  
Article
A Flexible IoT Stream Processing Architecture Based on Microservices
by Luca Bixio, Giorgio Delzanno, Stefano Rebora and Matteo Rulli
Information 2020, 11(12), 565; https://0-doi-org.brum.beds.ac.uk/10.3390/info11120565 - 02 Dec 2020
Cited by 8 | Viewed by 4203
Abstract
The Internet of Things (IoT) has created new and challenging opportunities for data analytics. The IoT represents an infinitive source of massive and heterogeneous data, whose real-time processing is an increasingly important issue. IoT applications usually consist of multiple technological layers connecting ‘things’ [...] Read more.
The Internet of Things (IoT) has created new and challenging opportunities for data analytics. The IoT represents an infinitive source of massive and heterogeneous data, whose real-time processing is an increasingly important issue. IoT applications usually consist of multiple technological layers connecting ‘things’ to a remote cloud core. These layers are generally grouped into two macro levels: the edge level (consisting of the devices at the boundary of the network near the devices that produce the data) and the core level (consisting of the remote cloud components of the application). The aim of this work is to propose an adaptive microservices architecture for IoT platforms which provides real-time stream processing functionalities that can seamlessly both at the edge-level and cloud-level. More in detail, we introduce the notion of μ-service, a stream processing unit that can be indifferently allocated on the edge and core level, and a Reference Architecture that provides all necessary services (namely Proxy, Adapter and Data Processing μ-services) for dealing with real-time stream processing in a very flexible way. Furthermore, in order to abstract away from the underlying stream processing engine and IoT layers (edge/cloud), we propose: (1) a service definition language consisting of a configuration language based on JSON objects (interoperability), (2) a rule-based query language with basic filter operations that can be compiled to most of the existing stream processing engines (portability), and (3) a combinator language to build pipelines of filter definitions (compositionality). Although our proposal has been designed to extend the Senseioty platform, a proprietary IoT platform developed by FlairBit, it could be adapted to every platform based on similar technologies. As a proof of concept, we provide details of a preliminary prototype based on the Java OSGi framework. Full article
(This article belongs to the Special Issue Microservices and Cloud-Native Solutions: From Design to Operation)
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