Cloud, Fog and Edge Computing in the IoT and Industry Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 8034

Special Issue Editors

Department of Informatics & Telecommunications, University of Ioannina, GR-47100 Arta, Greece
Interests: distributed systems; data networks; complex networks; network performance; fog and cloud computing; modeling; simulation
Department of Computer Science & Engineering, University of Ioannina, GR-45110 Ioannina, Greece
Interests: parallel and distributed systems; embedded systems; systems software; parallel programming; complex networks

Special Issue Information

Dear Colleagues,

Over the last few decades, the IoT and industry systems have become increasingly likely to adopt cloud, fog, and edge computing solutions to manage, process and store their data. This is a rather unavoidable need as transformation and innovation in almost all types of modern industries and IoT applications require latency-sensitive processing in real time and in a large scale, nearby or massive storage, reliability, security, and high data rate. By enabling each of these technologies or their combination, new opportunities and prospects arise for industry, market, and businesses. However, these distributed computing paradigms necessitate a comprehensive multiactor and multilevel approach in order to deal with the challenges of their integration in real-world IoT and industry applications. In the context of a wide-scale application, and from the perspective of complex networking, these paradigms have become a blueprint capable of being translated into a feasible, technologically practicable, and industry-viable solution.

This Special Issue is dedicated to bringing together advances, discussing common and interoperability problems, presenting novel solutions, and gathering efforts and recent developments in the aforementioned fields. Its goal is to explore the different visions of academia and industry on solutions that integrate these technologies in various scenarios and for different stakeholders.

Dr. Spiridoula V. Margariti
Dr. Vassilios V. Dimakopoulos
Guest Editors

Manuscript Submission Information

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Keywords

  • practical implementations, deployments, and use cases
  • performance analysis, evaluation, and improvement
  • design approaches for efficient, low-cost, and robust services
  • architectures and models
  • integration and synergies of ai, big data, and blockchain technologies with iot and industry systems
  • technological innovations in relation to distributed computing environments
  • network protocols, network topologies, networking issues
  • data management and data analysis in such environments
  • potential problems and limitations (e.g., interoperability)
  • resource scheduling and allocation
  • industrial security systems
  • energy consumption optimization
  • cost optimization

Published Papers (3 papers)

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10 pages, 367 KiB  
Article
Performance Analysis of Storage Systems in Edge Computing Infrastructures
by Antonios Makris, Ioannis Kontopoulos, Evangelos Psomakelis, Stylianos Nektarios Xyalis, Theodoros Theodoropoulos and Konstantinos Tserpes
Appl. Sci. 2022, 12(17), 8923; https://doi.org/10.3390/app12178923 - 05 Sep 2022
Cited by 4 | Viewed by 1961
Abstract
Edge computing constitutes a promising paradigm of managing and processing the massive amounts of data generated by Internet of Things (IoT) devices. Data and computation are moved closer to the client, thus enabling latency- and bandwidth-sensitive applications. However, the distributed and heterogeneous nature [...] Read more.
Edge computing constitutes a promising paradigm of managing and processing the massive amounts of data generated by Internet of Things (IoT) devices. Data and computation are moved closer to the client, thus enabling latency- and bandwidth-sensitive applications. However, the distributed and heterogeneous nature of the edge as well as its limited resource capabilities pose several challenges in implementing or choosing an efficient edge-enabled storage system. Therefore, it is imperative for the research community to contribute to the clarification of the purposes and highlight the advantages and disadvantages of various edge-enabled storage systems. This work aspires to contribute toward this direction by presenting a performance analysis of three different storage systems, namely MinIO, BigchainDB, and the IPFS. We selected these three systems as they have been proven to be valid candidates for edge computing infrastructures. In addition, as the three evaluated systems belong to different types of storage, we evaluated a wide range of storage systems, increasing the variability of the results. The performance evaluation is performed using a set of resource utilization and Quality of Service (QoS) metrics. Each storage system is deployed and installed on a Raspberry Pi (small single-board computers), which serves as an edge device, able to optimize the overall efficiency with minimum power and minimum cost. The experimental results revealed that MinIO has the best overall performance regarding query response times, RAM consumption, disk IO time, and transaction rate. The results presented in this paper are intended for researchers in the field of edge computing and database systems. Full article
(This article belongs to the Special Issue Cloud, Fog and Edge Computing in the IoT and Industry Systems)
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21 pages, 888 KiB  
Article
Reconfigurable Smart Contracts for Renewable Energy Exchange with Re-Use of Verification Rules
by Tomasz Górski
Appl. Sci. 2022, 12(11), 5339; https://doi.org/10.3390/app12115339 - 25 May 2022
Cited by 33 | Viewed by 3595
Abstract
Smart contracts constitute the foundation for blockchain distributed applications. These constructs enable transactions in trustless environments using consensus algorithms and software-controlled verification rules. In the current state of the art, there is a shortage of works on the adaptability of smart contracts, and [...] Read more.
Smart contracts constitute the foundation for blockchain distributed applications. These constructs enable transactions in trustless environments using consensus algorithms and software-controlled verification rules. In the current state of the art, there is a shortage of works on the adaptability of smart contracts, and the re-use of their source code is limited mainly to cloning. The paper discusses the pattern of smart contract design and implementation with the overt declaration of verification rules. The author introduces two advantages of the pattern: Firstly, run-time reconfigurability of the list of smart contract verification rules to adjust for various transaction types. Secondly, the re-use of verification rules between different configurations of the smart contract, and among diverse smart contracts. The paper uses blockchain platform-independent stereotypes from a dedicated Unified Modeling Language (UML) profile for designing smart contracts and verification rules. The implementation of the pattern is developed in object-oriented Java language. The pattern exploits polymorphism and controls inheritance by using sealed classes with permission for specialization only for selected final ones. Thus, the pattern ensures two recently highly desired properties in smart contract design and development: re-use and security. Moreover, the declared verification rules list facilitates test automation and reduces test preparation effort due to the re-use of test classes among smart contract configurations. The pattern usage is illustrated in the example of renewable energy exchange within the prosumers community and amid various communities. Full article
(This article belongs to the Special Issue Cloud, Fog and Edge Computing in the IoT and Industry Systems)
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15 pages, 2079 KiB  
Technical Note
CSS: Container Resource Manager Using System Call Pattern for Scientific Workflow
by Chunggeon Song, Heonchang Yu and Eunyoung Lee
Appl. Sci. 2022, 12(16), 8228; https://doi.org/10.3390/app12168228 - 17 Aug 2022
Cited by 1 | Viewed by 1111
Abstract
Multiple containers running scientific workflows in SMP-based high-performance computers generate some bottlenecks due to workload flexibility. To improve system resource utilization by minimizing these bottlenecks, vertical resource management is required to determine an appropriate resource usage policy according to the resource usage type [...] Read more.
Multiple containers running scientific workflows in SMP-based high-performance computers generate some bottlenecks due to workload flexibility. To improve system resource utilization by minimizing these bottlenecks, vertical resource management is required to determine an appropriate resource usage policy according to the resource usage type of the container. However, the traditional methods have additional overhead for collecting monitoring metrics, and the structure of the resource manager is complex. In this paper, in order to compensate for these shortcomings, we propose CSS, a dynamic resource manager utilizing system call data collected for security purposes. The CSS utilizes the SBCC algorithm, which uses the number of futex system calls as a heuristic measure to determine the number of IO-intensive workload occurrences. In addition, the CTBRA algorithm is used to determine the range of resources to be allocated for each container and to perform actual resource allocation. We implemented a prototype of CSS and conducted experiments on NPB to analyze the performance of CSS with various types of large-scale tasks of a scientific workflow. As a result of the experiment, it showed a performance improvement of up to 7% compared with the environment where Linux cgroups were not applied. In addition, CANU performance analysis was performed to verify the effectiveness of applications used in the real world, and performance improvement of up to 4.5% was shown. Full article
(This article belongs to the Special Issue Cloud, Fog and Edge Computing in the IoT and Industry Systems)
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