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Algorithms
Special Issues
Optimization Techniques, Algorithms, Applications for Cloud and Edge/Fog Computing Environments
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Optimization Techniques, Algorithms, Applications for Cloud and Edge/Fog Computing Environments

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 8583

Special Issue Editors

Prof. Dr. Yuri N. Sotskov

E-Mail Website
Guest Editor
United Institute of Informatics Problems, National Academy of Sciences of Belarus, Surganova Street 6, 220012 Minsk, Belarus.
Interests: discrete optimization; scheduling; complexity; graph theory; stability analysis; uncertainty
Prof. Dr. Andrei Tchernykh

E-Mail Website
Guest Editor
1. CICESE Research Center, Carr Tijuana-Ensenada 3918, Zona Playitas, Ensenada 22860, Mexico
2. Ivannikov Institute for System Programming of the Russian Academy of Sciences, Alexander Solzhenitsyn st., 25. 109004 Moscow, Russia
3. Problem-Oriented Cloud Computing Environment International Laboratory, South Ural State University, Prospekt Lenina 76, 454080 Chelyabinsk, Russia
Interests: grid and cloud; multiobjective resource optimization; security; uncertainty; scheduling; heuristics and meta-heuristics; adaptive resource allocation; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Frank Werner

E-Mail Website
Guest Editor
Faculty of Mathematics, Otto-von-Guericke-University, P.O. Box 4120, D-39016 Magdeburg, Germany
Interests: scheduling, in particular development of exact and approximate algorithms; stability investigations is discrete optimization; scheduling with interval processing times; complexity investigations for scheduling problems; train scheduling; graph theory; logistics; supply chains; packing; simulation and applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research in the area of the development of optimization algorithms and their applications to this Special Issue, “Optimization Techniques, Algorithms, and Applications for Cloud and Edge/Fog Computing Environments”. Edge–cloud–fog systems are very complex, and the use of different approaches in their design, development, and operational processes is inevitable. We are looking for new and innovative approaches for solving optimization problems arising in cloud, edge, and fog computing. High-quality papers are solicited to address both theoretical and practical issues of optimization algorithms. Submissions are welcome both for traditional optimization techniques and new applications for cloud, edge, and fog computing. Potential topics include but are not limited to single-criterion and multicriteria optimization problems, as well as problems arising in emerging applications, such as healthcare, smart manufacturing, smart agriculture, logistic, transport, web services, energy management, and smart home and city.

Prof. Dr. Yuri N. Sotskov
Prof. Dr. Andrei Tchernykh
Prof. Dr. Frank Werner
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. Algorithms 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

  • edge, fog, and cloud computing
  • internet of things
  • distributed and grid technology
  • virtualization
  • cloud task scheduling
  • metaheuristic techniques
  • scheduling under uncertainty
  • real-time scheduling
  • workflow scheduling
  • machine learning
  • load balancing
  • resource optimization
  • cost optimization
  • data reliability and security optimization
  • simulation
  • artificial intelligence
  • big data workflows
  • optimization on graphs
  • project management
  • stochastic models
  • optimization in logistics
  • smart energy management
  • smart home and city
  • design and optimization of intelligent transportation

Published Papers (4 papers)

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16 pages, 611 KiB  
Article
Placement of IoT Microservices in Fog Computing Systems: A Comparison of Heuristics
by Claudia Canali, Caterina Gazzotti, Riccardo Lancellotti and Felice Schena
Algorithms 2023, 16(9), 441; https://doi.org/10.3390/a16090441 - 13 Sep 2023
Cited by 2 | Viewed by 1229
Abstract
In the last few years, fog computing has been recognized as a promising approach to support modern IoT applications based on microservices. The main characteristic of this application involve the presence of geographically distributed sensors or mobile end users acting as sources of [...] Read more.
In the last few years, fog computing has been recognized as a promising approach to support modern IoT applications based on microservices. The main characteristic of this application involve the presence of geographically distributed sensors or mobile end users acting as sources of data. Relying on a cloud computing approach may not represent the most suitable solution in these scenario due to the non-negligible latency between data sources and distant cloud data centers, which may represent an issue in cases involving real-time and latency-sensitive IoT applications. Placing certain tasks, such as preprocessing or data aggregation, in a layer of fog nodes close to sensors or end users may help to decrease the response time of IoT applications as well as the traffic towards the cloud data centers. However, the fog scenario is characterized by a much more complex and heterogeneous infrastructure compared to a cloud data center, where the computing nodes and the inter-node connecting are more homogeneous. As a consequence, the the problem of efficiently placing microservices over distributed fog nodes requires novel and efficient solutions. In this paper, we address this issue by proposing and comparing different heuristics for placing the application microservices over the nodes of a fog infrastructure. We test the performance of the proposed heuristics and their ability to minimize application response times and satisfy the Service Level Agreement across a wide set of operating conditions in order to understand which approach is performs the best depending on the IoT application scenario. Full article
(This article belongs to the Special Issue Optimization Techniques, Algorithms, Applications for Cloud and Edge/Fog Computing Environments)
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20 pages, 3886 KiB  
Article
IRONEDGE: Stream Processing Architecture for Edge Applications
by João Pedro Vitorino, José Simão, Nuno Datia and Matilde Pato
Algorithms 2023, 16(2), 123; https://doi.org/10.3390/a16020123 - 17 Feb 2023
Viewed by 1458
Abstract
This paper presents IRONEDGE, an architectural framework that can be used in different edge Stream Processing solutions for “Smart Infrastructure” scenarios, on a case-by-case basis. The architectural framework identifies the common components that any such solution should implement and a generic processing pipeline. [...] Read more.
This paper presents IRONEDGE, an architectural framework that can be used in different edge Stream Processing solutions for “Smart Infrastructure” scenarios, on a case-by-case basis. The architectural framework identifies the common components that any such solution should implement and a generic processing pipeline. In particular, the framework is considered in the context of a study case regarding Internet of Things (IoT) devices to be attached to rolling stock in a railway. A lack of computation and storage resources available in edge devices and infrequent network connectivity are not often seen in the existing literature, but were considered in this paper. Two distinct implementations of IRONEDGE were considered and tested. One, identified as Apache Kafka with Kafka Connect (K0-WC), uses Kafka Connect to pass messages from MQ Telemetry Transport (MQTT) to Apache Kafka. The second scenario, identified as Apache Kafka with No Kafka Connect (K1-NC), allows Apache Storm to consume messages directly. When the data rate increased, K0-WC showed low throughput resulting from high losses, whereas K1-NC displayed an increase in throughput, but did not match the input rate for the Data Reports. The results showed that the framework can be used for defining new solutions for edge Stream Processing scenarios and identified a reference implementation for the considered study case. In future work, the authors propose to extend the evaluation of the architectural variation of K1-NC. Full article
(This article belongs to the Special Issue Optimization Techniques, Algorithms, Applications for Cloud and Edge/Fog Computing Environments)
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21 pages, 870 KiB  
Article
Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application
by Dadmehr Rahbari
Algorithms 2022, 15(11), 397; https://doi.org/10.3390/a15110397 - 26 Oct 2022
Cited by 9 | Viewed by 2207
Abstract
In recent years, the increasing use of the Internet of Things (IoT) has generated excessive amounts of data. It is difficult to manage and control the volume of data used in cloud computing, and since cloud computing has problems with latency, lack of [...] Read more.
In recent years, the increasing use of the Internet of Things (IoT) has generated excessive amounts of data. It is difficult to manage and control the volume of data used in cloud computing, and since cloud computing has problems with latency, lack of mobility, and location knowledge, it is not suitable for IoT applications such as healthcare or vehicle systems. To overcome these problems, fog computing (FC) has been used; it consists of a set of fog devices (FDs) with heterogeneous and distributed resources that are located between the user layer and the cloud on the edge of the network. An application in FC is divided into several modules. The allocation of processing elements (PEs) to modules is a scheduling problem. In this paper, some heuristic and meta-heuristic algorithms are analyzed, and a Hyper-Heuristic Scheduling (HHS) algorithm is presented to find the best allocation with respect to low latency and energy consumption. HHS allocates PEs to modules by low-level heuristics in the training and testing phases of the input workflow. Based on simulation results and comparison of HHS with traditional, heuristic, and meta-heuristic algorithms, the proposed method has improvements in energy consumption, total execution cost, latency, and total execution time. Full article
(This article belongs to the Special Issue Optimization Techniques, Algorithms, Applications for Cloud and Edge/Fog Computing Environments)
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15 pages, 435 KiB  
Article
Research Trends, Enabling Technologies and Application Areas for Big Data
by Lars Lundberg and Håkan Grahn
Algorithms 2022, 15(8), 280; https://doi.org/10.3390/a15080280 - 09 Aug 2022
Cited by 2 | Viewed by 2307
Abstract
The availability of large amounts of data in combination with Big Data analytics has transformed many application domains. In this paper, we provide insights into how the area has developed in the last decade. First, we identify seven major application areas and six [...] Read more.
The availability of large amounts of data in combination with Big Data analytics has transformed many application domains. In this paper, we provide insights into how the area has developed in the last decade. First, we identify seven major application areas and six groups of important enabling technologies for Big Data applications and systems. Then, using bibliometrics and an extensive literature review of more than 80 papers, we identify the most important research trends in these areas. In addition, our bibliometric analysis also includes trends in different geographical regions. Our results indicate that manufacturing and agriculture or forestry are the two application areas with the fastest growth. Furthermore, our bibliometric study shows that deep learning and edge or fog computing are the enabling technologies increasing the most. We believe that the data presented in this paper provide a good overview of the current research trends in Big Data and that this kind of information is very useful when setting strategic agendas for Big Data research. Full article
(This article belongs to the Special Issue Optimization Techniques, Algorithms, Applications for Cloud and Edge/Fog Computing Environments)
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