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Future Internet
Special Issues
Distributed Systems for Emerging Computing: Platform and Application
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Distributed Systems for Emerging Computing: Platform and Application

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (6 March 2023) | Viewed by 23545

Special Issue Editors

Dr. Xu Wang

grade E-Mail Website
Guest Editor
College of Software, Beihang University, Beijing 100191, China
Interests: intelligent software engineering; distributed system; software reliability and scalability
Special Issues, Collections and Topics in MDPI journals
Dr. Bin Shi

E-Mail Website
Assistant Guest Editor
School of Computer Science and Technology, Xi'an Jiaotong University, Xi’an 710049, China
Interests: virtualization; resource management; distributed computing; graph data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Yili Fang

E-Mail Website
Assistant Guest Editor
School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China
Interests: machine learning; crowdsourcing; social computing; knowledge representation; computer supported cooperative work

Special Issue Information

Dear Colleagues,

The emerging computing paradigms, including cloud/serverless computing, large-scale data processing, distributed machine learning, the Internet of Things, and social computing, have contributed toward great advances in the economy and in society. Among them, distributed systems such as computing platforms play a fundamental role which can connect thousands of computers, query and learn from large-scale data in parallel, creating a collaboration among computers, devices, and human beings. These platforms should be designed and implemented carefully for providing highly efficient, reliable, and secure services.

Based on platform services, a large amount of software applications are developed and deployed to leverage the features and APIs of underlying computing, network, data, physical, and human resources. One key challenge is how to improve the development efficiency and software quality of these applications for building the new ecosystems. Recently, data-driven and deep-learning-based approaches have attracted much attention and shown the great potential of intelligent development of applications.

The topics of this Special Issue cover the design and implementation of distributed systems for emerging computing paradigms and the recent advances in software engineering that are designed to develop applications hosted on these distributed systems.

Particular attention will be devoted to the following topics:

  • Distributed systems for cloud/serverless computing;
  • Distributed systems for large-scale data processing and query;
  • Distributed systems for distributed machine learning;
  • Distributed systems for the Internet of Things;
  • Distributed systems for crowdsourcing/social computing;
  • Data-driven software engineering methods;
  • Deep-learning-based software engineering methods;
  • Model-driven and knowledge-based software engineering methods;
  • Empirical studies on platforms and applications for emerging computing;
  • Reliable and secure services for emerging computing.

Dr. Xu Wang
Dr. Bin Shi
Dr. Yili Fang
Guest Editors

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. Future Internet 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

  • distributed systems
  • cloud/serverless computing
  • distributed machine learning
  • Internet of Things
  • data-driven software engineering

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Published Papers (5 papers)

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Editorial

Jump to: Research, Review

2 pages, 152 KiB  
Editorial
Distributed Systems for Emerging Computing: Platform and Application
by Xu Wang, Bin Shi and Yili Fang
Future Internet 2023, 15(4), 151; https://doi.org/10.3390/fi15040151 - 20 Apr 2023
Viewed by 1800
Abstract
In recent years, the new computing paradigms such as serverless computing, edge computing and blockchain-based computing have attracted much attention in both academia and industrial communities. Distributed systems and applications play fundamental roles in connecting the underlying computers, network and devices for collaboration, [...] Read more.
In recent years, the new computing paradigms such as serverless computing, edge computing and blockchain-based computing have attracted much attention in both academia and industrial communities. Distributed systems and applications play fundamental roles in connecting the underlying computers, network and devices for collaboration, as well as providing new services for users. However, due to the increasing complexity of the large-scale and dynamic heterogeneous resource, and the new requirements and features, these distributed systems and applications will face many challenges in terms of their efficiency, flexibility and algorithms. This editorial discusses the state-of-the-art advancements in distributed systems and applications for the emerging computing. Full article
(This article belongs to the Special Issue Distributed Systems for Emerging Computing: Platform and Application)

Research

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20 pages, 594 KiB  
Article
A Novel Strategy for VNF Placement in Edge Computing Environments
by Anselmo Luiz Éden Battisti, Evandro Luiz Cardoso Macedo, Marina Ivanov Pereira Josué, Hugo Barbalho, Flávia C. Delicato, Débora Christina Muchaluat-Saade, Paulo F. Pires, Douglas Paulo de Mattos and Ana Cristina Bernardo de Oliveira
Future Internet 2022, 14(12), 361; https://doi.org/10.3390/fi14120361 - 30 Nov 2022
Cited by 10 | Viewed by 2449
Abstract
Network function virtualization (NFV) is a novel technology that virtualizes computing, network, and storage resources to decouple the network functions from the underlying hardware, thus allowing the software implementation of such functions to run on commodity hardware. By doing this, NFV provides the [...] Read more.
Network function virtualization (NFV) is a novel technology that virtualizes computing, network, and storage resources to decouple the network functions from the underlying hardware, thus allowing the software implementation of such functions to run on commodity hardware. By doing this, NFV provides the necessary flexibility to enable agile, cost-effective, and on-demand service delivery models combined with automated management. Different management and orchestration challenges arise in such virtualized and distributed environments. A major challenge in the selection of the most suitable edge nodes is that of deploying virtual network functions (VNFs) to meet requests from multiple users. This article addresses the VNF placement problem by providing a novel integer linear programming (ILP) optimization model and a novel VNF placement algorithm. In our definition, the multi-objective optimization problem aims to (i) minimize the energy consumption in the edge nodes; (ii) minimize the total latency; and (iii) reducing the total cost of the infrastructure. Our new solution formulates the VNF placement problem by taking these three objectives into account simultaneously. In addition, the novel VNF placement algorithm leverages VNF sharing, which reuses VNF instances already placed to potentially reduce computational resource usage. Such a feature is still little explored in the community. Through simulation, numerical results show that our approach can perform better than other approaches found in the literature regarding resource consumption and the number of SFC requests met. Full article
(This article belongs to the Special Issue Distributed Systems for Emerging Computing: Platform and Application)
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29 pages, 2707 KiB  
Article
QuickFaaS: Providing Portability and Interoperability between FaaS Platforms
by Pedro Rodrigues, Filipe Freitas and José Simão
Future Internet 2022, 14(12), 360; https://doi.org/10.3390/fi14120360 - 30 Nov 2022
Cited by 8 | Viewed by 3625
Abstract
Serverless computing hides infrastructure management from developers and runs code on-demand automatically scaled and billed during the code’s execution time. One of the most popular serverless backend services is called Function-as-a-Service (FaaS), in which developers are often confronted with cloud-specific requirements. Function signature [...] Read more.
Serverless computing hides infrastructure management from developers and runs code on-demand automatically scaled and billed during the code’s execution time. One of the most popular serverless backend services is called Function-as-a-Service (FaaS), in which developers are often confronted with cloud-specific requirements. Function signature requirements, and the usage of custom libraries that are unique to cloud providers, were identified as the two main reasons for portability issues in FaaS applications, leading to various vendor lock-in problems. In this work, we define three cloud-agnostic models that compose FaaS platforms. Based on these models, we developed QuickFaaS, a multi-cloud interoperability desktop tool targeting cloud-agnostic functions and FaaS deployments. The proposed cloud-agnostic approach enables developers to reuse their serverless functions in different cloud providers with no need to change code or install extra software. We also provide an evaluation that validates the proposed solution by measuring the impact of a cloud-agnostic approach on the function’s performance, when compared to a cloud-non-agnostic one. The study shows that a cloud-agnostic approach does not significantly impact the function’s performance. Full article
(This article belongs to the Special Issue Distributed Systems for Emerging Computing: Platform and Application)
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13 pages, 1058 KiB  
Article
Latency Analysis of Blockchain-Based SSI Applications
by Tamas Pflanzner, Hamza Baniata and Attila Kertesz
Future Internet 2022, 14(10), 282; https://doi.org/10.3390/fi14100282 - 29 Sep 2022
Cited by 7 | Viewed by 2898
Abstract
Several revolutionary applications have been built on the distributed ledgers of blockchain (BC) technology. Besides cryptocurrencies, many other application fields can be found in smart systems exploiting smart contracts and Self Sovereign Identity (SSI) management. The Hyperledger Indy platform is a suitable open-source [...] Read more.
Several revolutionary applications have been built on the distributed ledgers of blockchain (BC) technology. Besides cryptocurrencies, many other application fields can be found in smart systems exploiting smart contracts and Self Sovereign Identity (SSI) management. The Hyperledger Indy platform is a suitable open-source solution for realizing permissioned BC systems for SSI projects. SSI applications usually require short response times from the underlying BC network, which may vary highly depending on the application type, the used BC software, and the actual BC deployment parameters. To support the developers and users of SSI applications, we present a detailed latency analysis of a permissioned BC system built with Indy and Aries. To streamline our experiments, we developed a Python application using containerized Indy and Aries components from official Hyperledger repositories. We deployed our experimental application on multiple virtual machines in the public Google Cloud Platform and on our local, private cloud using a Docker platform with Kubernetes. We evaluated and compared their performance benchmarked by Read and Write latencies. We found that the local Indy ledger reads and writes 30–50%, and 65–85% faster than the Indy ledger running on the Google Cloud Platform, respectively. Full article
(This article belongs to the Special Issue Distributed Systems for Emerging Computing: Platform and Application)
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Review

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24 pages, 2977 KiB  
Review
Research on Progress of Blockchain Consensus Algorithm: A Review on Recent Progress of Blockchain Consensus Algorithms
by Huanliang Xiong, Muxi Chen, Canghai Wu, Yingding Zhao and Wenlong Yi
Future Internet 2022, 14(2), 47; https://doi.org/10.3390/fi14020047 - 30 Jan 2022
Cited by 70 | Viewed by 11399
Abstract
Blockchain technology can solve the problem of trust in the open network in a decentralized way. It has broad application prospects and has attracted extensive attention from academia and industry. The blockchain consensus algorithm ensures that the nodes in the chain reach consensus [...] Read more.
Blockchain technology can solve the problem of trust in the open network in a decentralized way. It has broad application prospects and has attracted extensive attention from academia and industry. The blockchain consensus algorithm ensures that the nodes in the chain reach consensus in the complex network environment, and the node status ultimately remains the same. The consensus algorithm is one of the core technologies of blockchain and plays a pivotal role in the research of blockchain technology. This article gives the basic concepts of the blockchain, summarizes the key technologies of the blockchain, especially focuses on the research of the blockchain consensus algorithm, expounds the general principles of the consensus process, and classifies the mainstream consensus algorithms. Then, focusing on the improvement of consensus algorithm performance, it reviews the research progress of consensus algorithms in detail, analyzes and compares the characteristics, suitable scenarios, and possible shortcomings of different consensus algorithms, and based on this, studies the future development trend of consensus algorithms for reference. Full article
(This article belongs to the Special Issue Distributed Systems for Emerging Computing: Platform and Application)
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