Advances in Cloud and Edge Computing

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Parallel and Distributed Algorithms".

Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 4612

Special Issue Editors


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Guest Editor
Enzo Ferrari Engineering Department, University of Modena and Reggio Emilia, 41125 Modena, Italy.
Interests: cloud and edge computing infrastructures; load balancing strategies in distributed systems; wireless multi-hop networks
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Science & Engineering (DISI), University of Bologna, 40136 Bologna, Italy
Interests: distributed systems; industrial internet of things; industrial digital twins; edge cloud computing; resource orchestration in cloud/edge
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Enzo Ferrari Engineering Department, University of Modena and Reggio Emilia, 41125 Modena, Italy.
Interests: management of virtual elements in software-defined data centers; monitoring in cloud computing IaaS infrastructures; management of cloud computing IaaS infrastructures; performance evaluation of multi-tier web clusters
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The origin of cloud computing dates back to 2006, when the term was introduced for the first time in the context of an industrial conference. Academia, big IT players, and international standardization organizations have since made huge efforts to transform a revolutionary computing paradigm into a mature and reliable technology. Today, Cloud computing can provide flexible and scalable resources capable of meeting any application need in terms of computing power. However, with the continuous and uncontrolled increase of the number of “Things” (~20 billion, as of today) becoming connected to the Internet, large amounts of data are being generated and injected in the Cloud in an unprecedented volume, variety, and velocity. The availability of such devices, ranging from smart personal devices up to small dumb connected sensors, enables a whole new set of applications that rely on collecting, aggregating, and analyzing a huge amount of data. Examples range from the most common crowdsensing application up to the management of industrial processes or support for autonomous driving. When dealing with these novel applications, the Cloud can practically supply unlimited computing resources to scale any data size. On the other hand, the geographical distance between Things and Cloud premises makes the data transfer extremely expensive and may impact computing performance indices, e.g., computation timeliness. Very recently, the Edge computing paradigm has emerged to support the elaboration of data flows along the IoT-to-Cloud path by exploiting (less powerful) resources which are alternative to those supplied by the Cloud. By bringing computation closer to data sources, Edge computing enforces a paradigm shift that promises to meet the strict real-time requirements of the most demanding emerging applications (e.g., critical industrial control tasks, autonomous driving) that the Cloud model is not able to serve. Novel Edge computing can be used alone, creating a distributed mesh computing platform, or can be integrated with existing cloud computing infrastructures, creating a continuum computing scenario. 

The novel challenges of infrastructure management in this scenario are related to several aspects. The efficient monitoring of such infrastructure already represents a non-trivial task, but the complexity of the problem is exacerbated by the need to react in a timely and unsupervised manner to ever-changing workloads, characterized by unpredictable oscillations in the demands of each application and by new applications being deployed or dropped by the system. Finally, a further dimension to consider is the presence of virtualization, which is applied both at the level of computational elements and of networking functions. The goal of this Special Issue is to devise models, techniques, and algorithms that can support the management of cloud and edge computing infrastructures. The nature of these applications may introduce new challenges because coordinating large data processing tasks in a widely heterogeneous and distributed environment may pose new issues that need to be addressed. Furthermore, the management of data that may include critical, personal, or sensitive information introduces a new dimension to the problem that cannot be handled by relying on the high security standards that characterize cloud computing.

We invite researchers to submit innovative and original proposals that can advance the state of the art in the field of management of cloud and edge computing infrastructures.

Dr. Claudia Canali
Dr. Giuseppe Di Modica
Dr. Riccardo Lancellotti
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

  • models and algorithms for resource management in the computing continuum
  • energy models for cloud and edge infrastructures
  • resource allocation solutions for microservice-based and container-based systems
  • management of distributed infrastructures in the presence of software-defined networks, network function virtualization, and/or virtual routers
  • support for iot applications
  • solutions for the management of edge computing scenarios
  • performance models for distributed edge computing infrastructures
  • distributed algorithms for data processing and analysis, such as distributed application of machine learning
  • security issues in edge computing infrastructures
  • deployment of applications based on microservices on edge computing
  • application scenarios and experiences

Published Papers (2 papers)

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Research

19 pages, 1056 KiB  
Article
Efficient Online Engagement Analytics Algorithm Toolkit That Can Run on Edge
by Saw Thiha and Jay Rajasekera
Algorithms 2023, 16(2), 86; https://doi.org/10.3390/a16020086 - 06 Feb 2023
Cited by 1 | Viewed by 1503
Abstract
The rapid expansion of video conferencing and remote works due to the COVID-19 pandemic has resulted in a massive volume of video data to be analyzed in order to understand the audience engagement. However, analyzing this data efficiently, particularly in real-time, poses a [...] Read more.
The rapid expansion of video conferencing and remote works due to the COVID-19 pandemic has resulted in a massive volume of video data to be analyzed in order to understand the audience engagement. However, analyzing this data efficiently, particularly in real-time, poses a scalability challenge as online events can involve hundreds of people and last for hours. Existing solutions, especially open-sourced contributions, usually require dedicated and expensive hardware, and are designed as centralized cloud systems. Additionally, they may also require users to stream their video to remote servers, which raises privacy concerns. This paper introduces scalable and efficient computer vision algorithms for analyzing face orientation and eye blink in real-time on edge devices, including Android, iOS, and Raspberry Pi. An example solution is presented for proctoring online meetings, workplaces, and exams. It analyzes audiences on their own devices, thus addressing scalability and privacy issues, and runs at up to 30 fps on a Raspberry Pi. The proposed face orientation detection algorithm is extremely simple, efficient, and able to estimate the head pose in two degrees of freedom, horizontal and vertical. The proposed Eye Aspect Ratio (EAR) with simple adaptive threshold demonstrated a significant improvement in terms of false positives and overall accuracy compared to the existing constant threshold method. Additionally, the algorithms are implemented and open sourced as a toolkit with modular, cross-platform MediaPipe Calculators and Graphs so that users can easily create custom solutions for a variety of purposes and devices. Full article
(This article belongs to the Special Issue Advances in Cloud and Edge Computing)
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10 pages, 556 KiB  
Article
Cloud Computing in Free Route Airspace Research
by Peter Szabó, Miroslava Ferencová and Vladimír Železník
Algorithms 2022, 15(4), 123; https://doi.org/10.3390/a15040123 - 07 Apr 2022
Cited by 2 | Viewed by 2117
Abstract
We use technical documentation, data structures, data, and algorithms in our research. These objects support our work, but we cannot offer a unique citation for each object. This paper proposes a method (for citation and reference management) to cite such supportive resources using [...] Read more.
We use technical documentation, data structures, data, and algorithms in our research. These objects support our work, but we cannot offer a unique citation for each object. This paper proposes a method (for citation and reference management) to cite such supportive resources using Cloud Computing. According to the method, the publication cites only one source in the Cloud, and this source contains the Cloud schema, which describes the Cloud infrastructure. When we make a citation using the Cloud schema, we can pinpoint a cited object exactly. The proposed method supports open research; all research—Cloud items—is freely available. To illustrate the method, we applied it in the case of free route airspace (FRA) modelling. FRA is a new concept of Air Traffic Management and it is also the subject of our research. Full article
(This article belongs to the Special Issue Advances in Cloud and Edge Computing)
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